Single-cell sequencing data analysis cloud platform

Abstract

This invention provides a cloud platform for single-cell sequencing data analysis. The platform adopts a front-end / back-end separation architecture, including a view layer, a control layer, a computation layer, and a data layer. The view layer provides a graphical user interface; the control layer is responsible for request scheduling and task management; the computation layer integrates multiple bioinformatics analysis modules to perform a full-process analysis from data preprocessing, normalization, dimensionality reduction, clustering, differential expression analysis, cell annotation to gene enrichment analysis, transcription factor analysis, inter-cell communication analysis, copy number variation analysis, pseudo-temporal analysis, and pan-cancer analysis; the data layer uses a combination of relational database and file system to store project metadata, analysis parameters, and result files. This invention's platform lowers the technical threshold for single-cell data analysis through zero-code graphical interface operation and supports multi-task concurrency and asynchronous result viewing.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a cloud platform for single-cell sequencing data analysis. Background Technology

[0002] With the rapid development of biotechnology, remarkable achievements have been made in fields such as genomics, transcriptomics, and proteomics. Research in these fields has not only revealed the fundamental mechanisms of life but also provided strong support for the diagnosis, treatment, and prevention of diseases. However, traditional bulk sequencing technology obtains the average expression profile of a population of cells by homogenizing mixed samples. This "averaging" effect is mostly based on the population cell level, masking the heterogeneity between cells and causing specific cell types and key regulatory mechanisms to be overlooked.

[0003] Cellular heterogeneity refers to the diversity in morphology, function, and molecular characteristics among different cells within the same tissue or organ. This difference involves multiple levels, including gene expression, metabolic processes, and functional states, and plays a crucial role in biological processes such as embryonic development, immune responses, tissue regeneration, and disease treatment, becoming a significant marker of the complexity of living systems. To overcome the technical bottlenecks of traditional sequencing, single-cell sequencing technology has emerged.

[0004] Single-cell sequencing (SCRNA) is a high-throughput sequencing technology capable of sequencing the genome, transcriptome, and epigenome at the single-cell level. It precisely reveals the gene structure and expression status of individual cells, reflecting cellular heterogeneity and providing new perspectives and tools for precision medicine and personalized treatment. This technology demonstrates unique value at multiple levels: at the genomic level, it can identify gene mutations, chromosomal abnormalities, and copy number variations, elucidating tumor heterogeneity, drug resistance mechanisms, and recurrence patterns; at the transcriptomic level, it can construct single-cell gene expression maps, analyzing cell types, states, and interactions; and at the proteomic level, it can reveal protein composition, post-translational modifications, interaction networks, and signal transduction dynamics. Therefore, single-cell sequencing not only deepens our understanding of cellular heterogeneity, reveals the dynamic changes of cells in the spatiotemporal dimensions, and broadens our understanding of cellular dynamic changes under different environments, but also provides a theoretical basis for precise diagnosis and treatment. Among these, single-cell RNA sequencing (scRNA-seq) has become a mainstream research direction. Transcription, as the core regulatory link in gene expression, involves promoters and enhancers generating stable and unstable transcripts through different initiation modes. Given that RNA expression levels directly reflect cellular functional status and that measurement of expression levels is relatively easy, this invention focuses on scRNA-seq technology. Since its inception in 2009, scRNA-seq has made groundbreaking progress in analyzing tissue and organ composition, cellular functional classification, and interaction networks, becoming a cutting-edge tool for exploring transcriptome heterogeneity.

[0005] However, scRNA-seq technology faces significant challenges in its widespread application. First, the data processing workflow is complex and highly specialized. The analysis process encompasses more than ten steps, including quality control, batch effect correction, dimensionality reduction clustering, differential expression, and cell annotation, involving specialized toolkits such as Seurat and Scanpy. These tools rely on R / Python programming environments and command-line operations, posing a high technical barrier for medical and biological researchers. Many researchers need to hand over their data to third-party data processing personnel, which involves significant communication and additional time costs. Second, existing analysis platforms are fragmented. According to the scRNA-tools database, as of 2021, there were over 1,000 single-cell analysis tools, but most focus on a single step, lacking the ability to integrate the entire process from raw data to biological interpretation. Finally, it is difficult to balance flexibility and reproducibility in data analysis. Fixed-parameter workflows cannot adapt to different experimental design needs, while frequent manual parameter adjustments increase the risk of errors and make traceability difficult. More pressingly, the gap between clinical translation needs and computational resources is increasingly prominent. Clinical samples typically contain tens of thousands to millions of cells, with the data scale growing exponentially, placing stringent demands on computational performance and stability. Meanwhile, clinical decision-making requires intuitive and reliable annotation results, but traditional marker-based manual annotation relies on expert experience, is highly subjective, and inefficient. Therefore, building an integrated, intelligent, and user-friendly cloud platform for single-cell transcriptome analysis has become an urgent need to overcome technological bottlenecks and promote the deep integration of basic research and clinical applications.

[0006] RNA sequencing (RNA-seq) technology, as a powerful transcriptome sequencing tool, analyzes RNA in cells or tissues through high-throughput sequencing platforms to reflect the expression levels of various RNAs, including mRNA, small RNA, and noncoding RNA. This technology has advanced the quantitative analysis of gene expression and the study of RNA splicing variations, enabling researchers to understand the transcriptome state of cells and tissues at a more refined level. In the early stages of RNA-seq, scientists began to explore gene expression in single cells or tissues using high-throughput sequencing. Li et al. developed the whole-genome amplification (WGA) method, making it possible to obtain sufficient DNA from a single cell for sequencing. Early single-cell RNA sequencing platforms mainly relied on traditional sequencing technologies, such as PCR-based amplification and microarray-based detection. While these methods addressed the problem of amplifying small amounts of DNA to some extent, they still suffered from amplification bias and low amplification efficiency.

[0007] The real breakthrough in single-cell sequencing technology occurred when Tang et al. first successfully performed single-cell RNA sequencing (scRNA-seq) using high-throughput sequencing technology. This enabled scientists to measure gene expression at the single-cell level and reveal intercellular heterogeneity. This technological breakthrough greatly propelled the development of single-cell research, particularly in fields such as oncology, immunology, and neuroscience. With the continuous development of single-cell RNA sequencing technology, various single-cell sequencing platforms have emerged, such as single-cell whole-genome sequencing (scWGS), single-cell DNA methylation sequencing (scBS-seq), and novel single-cell neogenic RNA sequencing detection using click chemistry (scGRO-seq). These technological innovations have made the extraction and analysis of RNA from single cells more efficient and accurate, greatly enhancing the ability to study cellular heterogeneity. Single-cell RNA sequencing can not only reveal the gene expression profiles of different cell types but also capture dynamic changes between cells, providing new perspectives for understanding how cells respond to environmental changes or disease progression.

[0008] With the widespread application of single-cell RNA sequencing technology, various commercial platforms for single-cell RNA sequencing have emerged in the market. One type focuses on product providers that primarily develop their own equipment and consumables, such as the Fluidigm C1 platform, the 10x Genomics platform, and the BD Rhapsody platform. Each platform has its own unique features and supports different single-cell capture technologies. The Fluidigm C1 platform was one of the earliest commercially available platforms applied to the single-cell field, employing microfluidic technology to sort individual cells. Although its throughput is relatively low, it still maintains high accuracy in small-scale experiments. The 10xGenomics platform uses droplet technology, encapsulating individual cells in droplets for RNA extraction, reverse transcription, and amplification. This method significantly improves sequencing sensitivity and throughput, making efficient single-cell RNA sequencing possible.

[0009] With the emergence of these platforms, the high throughput and high sensitivity of single-cell RNA sequencing technology have been fully utilized. However, the widespread adoption of single-cell RNA sequencing technology does not solely rely on hardware breakthroughs; data analysis also plays a crucial role. Existing single-cell RNA sequencing data analysis tools such as Seurat and Scanpy provide complete data analysis workflows through integrated environments, including data preprocessing, normalization, dimensionality reduction, and clustering. However, many existing tools still face limitations. For example, most platforms rely on programming languages ​​such as R or Python, which poses a significant barrier to use for non-specialist researchers, limiting their adoption among users without programming backgrounds, such as medical professionals.

[0010] To enable more researchers to perform efficient data analysis using single-cell RNA sequencing technology, graphical user interface (GUI) platforms are becoming increasingly popular. These platforms simplify the workflow, allowing users without programming backgrounds to easily learn and perform various analytical tasks, from data preprocessing to cell clustering. ASAP is a typical example. ASAP is a web-based server that provides an intuitive and easy-to-use graphical interface, allowing researchers to complete complex single-cell RNA sequencing data analysis with simple clicks. The platform integrates many standardized analytical functions, such as data quality control, preprocessing, denoising, and cluster analysis, helping researchers quickly screen and process data. However, while ASAP excels in basic data processing, it still has limitations when handling some advanced analytical tasks. For example, ASAP currently does not provide comprehensive support for more complex tasks such as copy number variation analysis and pan-cancer analysis, which limits the platform's ability to handle multi-dimensional, biologically in-depth analysis. Similarly, platforms such as ICARUS and CELLAR also provide graphical interfaces and can handle basic single-cell RNA sequencing data analysis tasks. The emergence of these platforms has significantly simplified the barrier to entry for using single-cell RNA sequencing technology. However, despite their progress in routine tasks such as data preprocessing and quality control, these platforms still have limitations in advanced downstream analysis. To address this issue, some platforms have begun to integrate more specialized functions. OmicStudio integrates gene set enrichment analysis (GSEA), a widely used method in genomics research that identifies gene sets associated with specific biological processes, functions, or pathways. Through this analysis, OmicStudio not only helps researchers identify potential biological mechanisms but also provides important clues for complex questions such as disease mechanisms and drug action mechanisms. Furthermore, SciAp, through its integrated trajectory inference function, allows researchers to depict the dynamic evolution of cells over time at the single-cell level. Trajectory inference technology is particularly suitable for studying cell differentiation, development, and tumor progression, helping researchers gain a clearer understanding of the transition pathways of cells in different states. Meanwhile, ezSingleCell provides cell-cell communication analysis, a function that reveals the importance of cell-cell interactions. Intercellular communication is fundamental to many biological processes in organisms, including immune responses and cell-cell interactions within the tumor microenvironment. Through this function, researchers can gain a deeper understanding of the interactions and coordination between different cell populations.It is worth noting that although these platforms have made considerable breakthroughs in cell omics data analysis, their current analytical capabilities are still insufficient to fully explore single-cell transcriptome data. Therefore, developing a single platform that integrates basic analysis and more comprehensive and specialized downstream analysis modules remains an urgent need in current research.

[0011] Patent application CN109448788A discloses an online microbiome analysis platform architecture for genomics and bioinformatics, comprising: a bottom support layer, including a bioinformatics analysis module and a cloud platform support module. The bioinformatics analysis module is used to perform personalized analysis on 16S, ITS, 18S, and microbial metagenomic sequencing using the cloud platform support module, interacting with a data and functional layer and an interaction layer, and finally presenting the results to the user through an interactive interface. The cloud platform support module includes the necessary hardware and software conditions for the cloud platform; a data and functional layer is used to provide user data and system data to the bottom support layer; and an interaction layer is used to present the analysis results of the bioinformatics analysis module to the user through an interactive interface. However, this patent cannot completely solve the existing technical problems, nor can it meet the needs of this invention. Summary of the Invention

[0012] To address the shortcomings of existing technologies, the purpose of this invention is to provide a cloud platform for single-cell sequencing data analysis.

[0013] The single-cell sequencing data analysis cloud platform provided by the present invention includes: a view layer, a control layer, a computing layer, and a data layer;

[0014] The view layer is used to provide a user interface, receive project configuration parameters, analysis task instructions and single-cell sequencing data files input by the user, and display analysis results and visualizations. The control layer is communicatively connected to the view layer and is used to receive requests from the view layer, verify and encapsulate the project configuration parameters and single-cell sequencing data, distribute the encapsulated tasks to the computing layer, and receive the processing results returned by the computing layer and forward them to the view layer. The computing layer is communicatively connected to the control layer and includes multiple independently encapsulated bioinformatics analysis modules for executing corresponding analysis processes according to task instructions distributed by the control layer. The analysis process includes at least data preprocessing, normalization, dimensionality reduction, clustering, differential expression analysis, and cell annotation. The data layer is communicatively connected to the control layer and the computing layer, and is used to store user accounts, project metadata, task parameters and task status information in a relational database, and to store the raw single-cell sequencing data files uploaded by users, intermediate data files generated by the computing layer and final result files in a file system.

[0015] Preferably, the view layer is built on the Vue.js framework and includes a user management page, a data upload page, a parameter configuration page, and a result display page; The control layer is implemented based on the Flask framework, providing file upload interface, parameter configuration interface, task submission interface and result acquisition interface, and uses an asynchronous task model to process requests from the view layer; In the data layer, the relational database is MySQL, and its table structure is designed based on a three-level association model of user-project-task. Project metadata and task parameters are stored in JSON format.

[0016] Preferably, in the data layer, the file system is divided into a raw file area, an intermediate file area, and a result area, and the file naming follows the combination rule of project ID + task ID + timestamp; The control layer is also used to trigger an asynchronous cleanup task of the corresponding directory in the file system when a user deletes an item or task.

[0017] Preferably, the bioinformatics analysis module in the computational layer includes: Quality control module: used to filter single-cell sequencing data based on set thresholds, including the number of genes detected in each cell, the total number of RNA molecules, and the proportion of mitochondrial genes; High-variability gene screening module: used to identify high-variability genes from filtered data. It adopts a strategy based on gene normalized variance to calculate the relationship between the mean and variance of gene expression and retain the gene set with the highest variance ranking. Dimensionality reduction module: used to perform principal component analysis on the highly variable gene expression matrix and further perform nonlinear dimensionality reduction, the nonlinear dimensionality reduction method includes t-distributed random neighborhood embedding and unified manifold approximation and projection; Clustering module: Used to construct a cell adjacency graph in the dimensionality-reduced feature space based on the K-nearest neighbor algorithm, and to perform community partitioning on the graph using a modular optimization algorithm to obtain cell cluster labels.

[0018] Preferably, the bioinformatics analysis module in the computational layer further includes a cell annotation module, which integrates at least one of the following methods: The annotation method based on the reference expression matrix matches the expression profile of the cells to be annotated with the reference expression matrix with cell type labels, and assigns a label to each cell. The annotation method based on machine learning models uses a pre-trained or user-uploaded training classification model to predict the cell expression matrix to obtain cell type labels. An annotation method based on a large language model inputs a list of marker genes obtained from differential expression analysis into the language model, and generates candidate cell type labels through model inference.

[0019] Preferably, the computing layer further includes a high-level analysis module, which includes: Gene enrichment analysis module: Used for GO functional or KEGG pathway enrichment analysis of differentially expressed gene lists based on hypergeometric test or Fisher's exact test; the specific process is as follows: Define the background gene set as all genes detected in the expression matrix, with a total number of N genes; for a certain functional gene set to be tested, the number of genes contained in this set in the background set is M, and the total number of genes contained in the target gene set is n, of which k genes simultaneously belong to the functional gene set; based on this, a statistical test is constructed: the hypergeometric test is used to calculate the probability P of observing at least k genes simultaneously falling into both sets, the formula is:

[0020] The smaller the P-value, the more significant the enrichment. The multiple P-values ​​generated by the functional item test are corrected for false discovery rate to generate corrected P-values. Finally, a list containing enriched items, gene counts, enrichment ratios, P-values ​​and corrected P-values ​​is output, and an enrichment bar chart is automatically generated. Cell communication analysis module: Based on a ligand-receptor interaction database, this module infers and quantifies signaling communication networks between different cell types by aggregating the expression levels of ligands and receptors in aggregated cell populations. Specifically, for each ligand-receptor pair (L, R) in the database, the potential communication strength between the sending cell population i and the receiving cell population j is calculated, and the average expression level of ligand L in the sending population i is calculated. and the average expression level of receptor R in receiver group j Using geometric mean To comprehensively characterize the co-expression level of ligand-receptor; a Michaelis-Menten-like equation model is used to convert aggregate expression levels into communication probabilities to simulate the saturation effect of signal binding, the expression being:

[0021] in, It is an inferred communication probability or strength. It is the half-maximal effect constant. The Hill coefficient is used; a null distribution is constructed by randomly permuting cell type labels, and the empirical P-value of the observed values ​​is calculated; the communication strength of all ligand-receptor pairs belonging to the same signaling pathway is integrated to obtain the intercellular communication network at the pathway level. The results are visualized in the form of a circular network diagram and heatmap to show the global interaction patterns and key signaling pathways. Copy number variation analysis module: By comparing gene expression in the observed cell population with that in the reference cell population, smoothing the expression along the chromosome direction using a moving window, identifying genomic regions with continuously increasing or decreasing expression, and inferring copy number variations; the specific process is as follows: a set of cells with known genomic stability is designated as the reference cell population, and the remaining cells to be analyzed are designated as the observed cell population. For each observed cell c and each gene g, the logarithmic ratio of its expression level relative to the average expression level of the reference cell population is calculated.

[0022] in, It represents the expression level of gene g in cell c. It is its average expression level in the reference group. It is a preset minimal constant; along the physical location of the chromosome, a sliding window of fixed width is used to... The values ​​are smoothed to obtain the smoothed expression offset. The smoothed data is segmented using a thresholding method or a Gaussian mixture model to divide the genomic regions into different copy number states: Amplification: Missing: ;neutral: ,in The threshold was set; the analysis results were finally presented in the form of a heatmap, with rows representing cells, columns arranged according to genomic location, and colors visually displaying the inferred copy number variation regions; The pseudo-temporal analysis module constructs a master path graph for a selected cell population in a reduced-dimensional space, calculates the path length from each cell to a specified starting point as the pseudo-temporal value, and analyzes the trend of gene expression along the pseudo-temporal sequence. Specifically, on a user-selected subset of cells, a nearest neighbor graph is constructed based on the reduced-dimensional space. Nodes in the graph represent cells, and edges connect each cell to its K nearest neighbors. Based on this graph, a simplified skeleton structure, called the master path graph, is learned using a minimum spanning tree or inverse graph embedding algorithm. This graph contains branch points and leaf nodes, representing the main paths and fate decision points of cell state transitions. After the user specifies one or a group of cells as the trajectory starting point, the system calculates the shortest path distance from each cell node to the root node in the graph; this distance is defined as the pseudo-temporal value for that cell. The expression level of each gene is smoothly fitted to the pseudo-temporal value, and the significance of its expression trend is evaluated. The output includes the pseudo-temporal value for each cell, the trajectory graph structure, and a list of trending genes, visualized in the form of a pseudo-temporal projection map and a gene expression trend map. The pan-cancer analysis module integrates a public database of gene expression across multiple cancer types, allowing users to input target genes and perform cross-cancer expression differential, epigenetic association, immune invasion correlation, and survival association analyses. The specific process is as follows: Within each cancer type, the expression difference of the gene between tumor tissue and adjacent normal tissue is calculated, the p-value is calculated using the Wilcoxon rank-sum test, and the logarithmic folding change is calculated. The results are presented as a cross-cancer expression differential box plot. Within each cancer type, the Spearman correlation coefficient between target gene expression and the methylation level of CpG sites in its promoter region is calculated, and the strength of the correlation is displayed as a heatmap. Within each cancer type sample, the Spearman correlation coefficient between target gene expression and the estimated invasion fraction of various immune cells is calculated, and the results are presented as a correlation heatmap. Within each cancer type, patients are divided into high and low expression groups based on the target gene expression level. The hazard ratio and its confidence interval are calculated using a Cox proportional hazards regression model, and survival differences are compared using a Log-rank test. The results are summarized and displayed as Kaplan-Meier survival curves and forest plots.

[0023] Preferably, in the dimensionality reduction module, principal component analysis maps the original data to a set of linearly independent principal components through orthogonal transformation, and the direction of the first principal component is the direction with the largest variance of the original data; The t-SNE method is optimized by calculating a Gaussian kernel-based similarity distribution P in high-dimensional space, establishing a t-distribution-based similarity distribution Q in low-dimensional space, and minimizing the Kullback-Leibler divergence between the two. The KL divergence calculation formula is as follows:

[0024] in, This represents the similarity between cell i and cell j in high-dimensional space. This represents the similarity between corresponding values ​​in a low-dimensional space. The differential expression analysis was performed using a model based on the negative binomial distribution.

[0025] Preferably, the computational layer further includes a batch effect integration module for correcting multi-sample single-cell sequencing data. The module integrates at least one of the following methods: a correction method that pairs cells that are the nearest neighbors between different batches, or a method that projects and aligns data from different batches into a shared latent space through iterative optimization.

[0026] Preferably, the control layer is also used to implement task management and result traceability functions, and records the analysis process in a tree structure. The root node corresponds to the project data, the child nodes correspond to different cell annotation model results, and the grandchild nodes correspond to specific high-level analysis tasks. Each task node records its running parameters, submission time, running status and result storage path. The platform allows users to submit multiple analysis tasks simultaneously and schedule them asynchronously.

[0027] Preferably, the result display page of the view layer integrates an interactive drawing component, supporting online rendering and interactive operation of quality control violin plots, dimensionality-reduced scatter plots, clustering result plots, marker gene bubble plots, gene enrichment bar plots, transcription factor analysis result plots, cell communication network plots, copy number variation heatmaps, pseudo-time series trajectory plots, and pan-cancer analysis box plots.

[0028] Compared with the prior art, the present invention has the following beneficial effects: (1) By constructing a web-based graphical interactive interface, the complex command-line analysis process such as Seurat and Scanpy is encapsulated into selectable modules, enabling medical and biological researchers without programming background to independently complete the entire process from raw data upload, quality control, dimensionality reduction clustering, cell annotation to advanced downstream analysis, completely eliminating the dependence of traditional analysis methods on programming skills and significantly reducing the technical application threshold.

[0029] (2) The platform not only integrates basic analysis modules such as data preprocessing, normalization, clustering, and differential expression, but also deeply integrates multiple professional downstream analysis modules such as gene enrichment analysis, transcription factor analysis, intercellular communication analysis, copy number variation analysis, pseudo-time series analysis, and pan-cancer analysis. This "one-stop" integration avoids the cumbersome switching between different fragmented tools and data export for users, and meets the diversified and in-depth analysis needs from basic scientific research to clinical translational research.

[0030] (3) The platform integrates multiple cell annotation methods, including reference database matching, machine learning model prediction, and large language model inference, and supports users in training customized models using their own small sample data. This multi-strategy fusion mechanism improves the automation, interpretability, and adaptability of annotation to rare cell types or specific research scenarios, thereby enhancing the reliability of the analysis results.

[0031] (4) Through the three-level data model of “user-project-task” and detailed parameter version records, the platform ensures that the conditions and results of each analysis task are traceable and reproducible. Combined with the multi-task asynchronous concurrent scheduling mechanism, users can submit and manage multiple analysis projects at the same time and view the results after closing the terminal, which greatly improves the management efficiency of large-scale data analysis and the standardization of scientific research.

[0032] (5) By embedding large public omics databases such as TCGA, the platform supports users in quickly linking and validating single-cell level discoveries with macro-level information such as pan-cancer expression profiles and clinical prognoses. This function provides direct data support for exploring the universality of biological significance and clinical relevance, enhancing the persuasiveness and translational potential of research findings.

[0033] (6) The platform provides various publication-quality charts and visualizations for each step of the analysis results, and supports users to dynamically adjust parameters and conduct interactive exploration. The intuitive graphical output and flexible result export function greatly facilitate the interpretation of data, the generation of reports, and the publication of scientific research results. Attached Figure Description

[0034] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a diagram of the architecture of a single-cell transcriptome sequencing analysis platform. Figure 2 This is a schematic diagram of the platform analysis process; Figure 3 This is a diagram illustrating the effectiveness of quality control. Figure 4 This is a diagram showing the clustering results; Figure 5 Annotation diagram for cells; Figure 6 This is a diagram illustrating the marker gene effect. Figure 7 Figure showing the GO analysis results; Figure 8 The image shows the results of the CellChat analysis. Figure 9 Figure showing the results of copy number variation analysis; Figure 10 This is a graph showing the results of the pseudo-time series analysis; Figure 11 Gene expression trend graph; Figure 12 This is a graph showing the results of the pan-cancer analysis; Figure 13 This is a graph showing the results of transcription factor analysis. Detailed Implementation

[0035] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0036] Example This invention possesses dual theoretical value at the methodological level of computational biology. First, the constructed SCSEQ platform integrates mainstream algorithms such as Seurat, Harmony, and CellChat into standardized service modules, establishing a standardized workflow covering the entire process from "data preprocessing—cell annotation—deep downstream analysis," providing an engineering paradigm for the standardization and reproducibility of single-cell analysis processes. In particular, the introduction of Retrieval-Augmented Generation (RAG) technology to construct a Large Language Model (LLM)-assisted annotation module, utilizing professional databases such as PanglaoDB as external knowledge sources, and generating candidate cell types through semantic retrieval and contextual enhancement, explores the interpretable application path of artificial intelligence in single-cell annotation tasks. This strategy not only improves annotation efficiency but also establishes a new human-computer collaborative paradigm of "computational prediction—expert verification," providing a methodological reference for the interdisciplinary field of life sciences and artificial intelligence. Secondly, the platform's multi-task concurrent scheduling and parameter version traceability mechanism solves the core contradiction between resource isolation and result reproduction in large-scale computing. Its proposed three-level data management model of "project-task-version" can provide a general theoretical framework for the architecture design of similar bioinformatics cloud computing platforms.

[0037] In terms of clinical application translation, the zero-code interactive platform developed in this invention directly responds to the urgent need for the popularization of single-cell technology in precision medicine. The platform accelerates implementation in the following ways: First, through a zero-code interface, the platform allows users to complete the entire analysis from raw data to publication-quality charts without programming, enabling medical professionals without a programming background to independently complete the entire process from data upload to result interpretation, significantly lowering the technical application threshold. Second, it integrates professional modules such as InferCNV, Monocle, and TCGA pan-cancer analysis, supplementing the functions of tumor microenvironment analysis and developmental trajectory inference. The overall functionality is more complete, meeting more advanced user analysis needs. The analysis process has been validated with real data, and its accuracy meets expectations. Third, the platform has a large number of adjustable parameters and visualization methods built-in. Users can precisely control the analysis process by adjusting parameters, maximizing the satisfaction of users' personalized needs. The rich variety of interactive visualizations greatly facilitates user analysis results while also providing high-quality publication-quality images, accelerating the translation of research findings. Furthermore, this platform establishes a mechanism for training customized annotation models using user data, allowing users to upload annotation results for training. This supports the learning of rare cell types and novel marker genes, which is of great significance for studying novel cells or small cell samples. This method has been validated on human liver cancer cells and is serving industry-wide research projects. By integrating advanced modules such as pan-cancer analysis and cell communication, the platform provides a "one-stop" solution for clinical problems such as tumor immune microenvironment research and drug target screening, effectively promoting the translation of single-cell technology from scientific research to clinical application.

[0038] In summary, this invention addresses the clinical translation bottleneck of single-cell omics technology. Through the innovative integration of cloud computing and artificial intelligence technologies, it constructs a fully functional, stable, and user-friendly analysis platform. This not only enriches the bioinformatics methodology system but also provides important technical support for precision medicine practice, demonstrating significant scientific value and application prospects.

[0039] Core analytical tools and technologies Seurat is a widely used R package for single-cell RNA sequencing data analysis, designed to provide users with comprehensive quality control, data analysis, and exploration capabilities. Seurat's primary goal is to help researchers identify and interpret cellular heterogeneity from single-cell transcriptome data, thereby revealing its biological significance. Furthermore, Seurat can integrate various types of single-cell data, including data from different experimental conditions and data generated from different technology platforms, thus providing a powerful tool for biological research.

[0040] Seurat's core functionalities include data preprocessing, normalization, dimensionality reduction, clustering, differential gene analysis, and cell type labeling. These functions enable researchers to extract meaningful biological information from complex single-cell data. Seurat also supports efficient data visualization, allowing researchers to visually observe and analyze the structure and characteristics of single-cell transcriptome data.

[0041] Several key analytical techniques in Seurat include Quality Control (QC), Principal Component Analysis (PCA), nonlinear dimensionality reduction, and cell clustering. These techniques provide essential tools for understanding and analyzing single-cell data and form the foundation for in-depth single-cell transcriptomics research.

[0042] In the analysis of single-cell RNA sequencing data, quality control (QC) is the primary step to ensure data reliability and accuracy. Due to the high throughput and complexity of single-cell sequencing technology, the data often contains a certain amount of noise and bias. These noises and biases can originate from multiple sources, such as sequencing depth, cell state, and technical issues. Therefore, we need a more comprehensive understanding of the sequencing data.

[0043] A notable characteristic of single-cell RNA sequencing data is the "dropout" phenomenon, where many genes have an expression value of zero. This is due to limitations in sequencing depth, which prevents the capture of transcripts of low-abundance genes, or because these genes are indeed not expressed in the cell. The dropout phenomenon makes single-cell RNA sequencing data a sparse matrix, and the large number of zero values ​​can affect the results of data analysis. Therefore, these zero values ​​must be processed using appropriate preprocessing methods.

[0044] (1) Changes in cell state In single-cell sequencing data, cells exhibit diverse biological states, with some potentially dying or undergoing degeneration. Dead or dying cells produce abnormal gene expression patterns and are typically excluded from the target population for analysis because they may introduce unwanted noise or errors. Therefore, removing low-quality cells is a crucial step in data cleaning.

[0045] (2) Two-cell contamination In single-cell RNA sequencing, doublets or multiplets can occur, where two cells are mistakenly captured in the same well and sequenced together. This leads to abnormal gene expression data, manifested as higher gene expression levels because the RNA information from both cells is included. Normally, a single cell expresses between 3000 and 4000 genes; therefore, an abnormally high gene expression count usually indicates a data quality problem.

[0046] Therefore, effective quality control is crucial. To address the aforementioned issues, Seurat provides a variety of quality control metrics (QCmetrics) to help users identify and remove low-quality cells. Commonly used QCmetrics include: ① Number of genes detected (nFeature_RNA): The number of genes detected in each cell. Low-quality cells typically have very few genes.

[0047] ② Total RNA Count (nCount_RNA): The total number of RNA molecules in a cell. Low or high RNA abundance in cells may indicate a problem.

[0048] ③ Mitochondrial gene proportion (percent.mt): The proportion of mitochondrial genes expressed. Since mitochondrial genes are released in large quantities when cells die or are damaged, increasing the proportion of mitochondrial genes, this indicator can be used to identify dead cells or low-quality cells.

[0049] ④ Hemoglobin gene percentage (percent.hb): The percentage of hemoglobin genes, which is usually used in blood samples to identify abnormalities in hemoglobin expression.

[0050] Dimensionality reduction and clustering methods Principal Component Analysis (PCA) is a classic linear dimensionality reduction method. Its core idea is to map the original high-dimensional data to a new set of linearly independent variables called principal components (PCs) through orthogonal transformations. In this process, PCA uses the direction with the largest variance in the original data as the first principal component, the direction with the second largest variance as the second principal component, and so on. Because the principal components are independent of each other and retain the most significant trends in the data, PCA minimizes redundant dimensions while preserving useful information.

[0051] Single-cell RNA sequencing data is characterized by high dimensionality, sparsity, and high noise. For example, a typical data matrix may contain thousands to tens of thousands of genes, while the number of cells may reach tens or even hundreds of thousands. Directly performing clustering or dimensionality reduction visualization in such a high-dimensional data space often faces the curse of dimensionality, which is not only computationally expensive but also prone to amplifying noise. Therefore, in single-cell analysis workflows, PCA is widely used for data dimensionality reduction, providing an efficient and stable data representation for subsequent clustering analysis, visualization, and proximity calculation.

[0052] In Seurat, Principal Component Analysis (PCA) is typically applied after screening for highly variable genes (HVGs). HVGs reflect significant biological differences between different cells, so screening them before dimensionality reduction helps reduce interference from noisy genes on principal components. PCA results not only include the proportion of variance explained by each principal component but also allow for visualization using Elbow plots to determine the number of principal components to retain. Generally, retaining the first 10 to 50 principal components required for downstream analysis is sufficient for most biological problems. Furthermore, the significance of PCA in single-cell analysis extends beyond dimensionality reduction. Principal component space can also reveal potential segregation trends between different cell populations. For example, in tumor single-cell transcriptomics, malignant cells and immune cells often show clear separation in PC space, while cells in the tumor microenvironment exhibit a certain degree of continuity. Therefore, PCA is not only a mathematical tool but also provides researchers with preliminary biological structural information, laying the foundation for subsequent clustering, pseudo-temporal analysis, and trajectory analysis.

[0053] Clustering is one of the core tasks in single-cell data analysis. Its purpose is to group cells with similar lineages or functions into clusters to identify cell types, cell states, or potential biological subpopulations. Due to the high heterogeneity and continuous variation of single-cell data, traditional center-based clustering methods (such as K-means) often perform poorly when handling this type of data. To address this, Seurat employs a graph-based clustering strategy, drawing on graph theory to achieve an unsupervised clustering process more suitable for single-cell data.

[0054] Specifically, Seurat first calculates the Euclidean distance matrix between cells in the dimensionality-reduced space after PCA, and then constructs a cell adjacency graph using the K-nearest neighbors (KNN) algorithm. In the KNN graph, each node represents a cell, and each edge represents the nearest neighbor relationship between cells. The value of K is usually set according to the data scale; for example, small-scale experiments may use K≈10, while large-scale datasets may use K≈50. The construction of the KNN graph helps to preserve the data topology, so that dense regions in the representation space are represented in the form of tight connections in the graph.

[0055] Building upon the KNN graph, Seurat further computes Shared Nearest Neighbor (SNN) relationships, weighting the strength of associations between nodes using Jaccard similarity to form a more robust cellular relationship network. Subsequently, Seurat employs modular optimization algorithms to partition the network into communities, with Louvain and Leiden being two common approaches. The Louvain algorithm identifies highly interconnected node groups by maximizing network modularity; it has low time complexity and is suitable for large-scale datasets, but it may introduce disconnected communities. The Leiden algorithm improves upon Louvain by reconnecting and refining partitions, enhancing the stability and biological plausibility of the results, and has become the mainstream clustering method in single-cell analysis.

[0056] The advantage of using graph theory for clustering lies in its ability to identify cell populations with complex topological structures and capture continuous patterns of change in the data. For example, processes such as immune cell differentiation and tumor cell evolution often appear as continuous spectra in the data, and graph methods can identify subpopulations without forcibly defining hard boundaries. Furthermore, graph-based clustering results can be seamlessly integrated with tasks such as cell annotation and trajectory analysis, providing a solid foundation for subsequent analysis.

[0057] In the field of single-cell RNA sequencing data analysis, nonlinear dimensionality reduction techniques are widely used to construct data visualizations in low-dimensional space, thereby helping researchers observe the relationships, distribution trends, and population structure among cells. Compared with linear dimensionality reduction methods such as PCA, nonlinear dimensionality reduction is better suited to handling complex nonlinear structures in high-dimensional space, thus better revealing the substructures within cell populations. Currently, the most widely used nonlinear dimensionality reduction methods mainly include two categories: t-distributed stochastic neighbor embedding (t-SNE) and unified manifold approximation and projection (UMAP).

[0058] First proposed by van der Maaten and Hinton, t-SNE's core idea is to preserve local neighborhood structure. The algorithm first calculates the similarity distribution between cells in a high-dimensional space, typically generated based on a Gaussian kernel function. Then, t-SNE establishes a new similarity distribution in a low-dimensional space using a t-distribution, optimizing by minimizing the Kullback-Leibler divergence between the two distributions. During this process, cells that are close together in the high-dimensional space will still cluster in the low-dimensional space, while more distant cells will be pushed apart. Therefore, t-SNE is particularly suitable for visualizing complex cell populations, clearly presenting the separation structure between different cell subpopulations in two-dimensional or three-dimensional space. From a practical application perspective, t-SNE's advantage lies in its sensitivity to local structure, but it also suffers from high computational cost, difficulty in preserving global data relationships, and sensitivity to parameters such as perplexity. Furthermore, instability may occur between different runs, meaning the displayed results have a certain degree of randomness.

[0059] Compared to t-SNE, UMAP is a more modern manifold learning technique. Its theoretical foundation stems not only from the manifold assumption but also incorporates topological concepts. UMAP models local structure through nearest-neighbor graph construction and reconstructs spatial relationships in low-dimensional space by optimizing the graph layout. Unlike t-SNE, UMAP emphasizes preserving a balance between local and global structure, thus appearing more natural when displaying continuous changes in cell lineages (e.g., immune cell differentiation trajectories). Furthermore, UMAP offers significant advantages in computational efficiency, handling larger datasets and exhibiting better robustness. Due to its speed, interpretability, and stable visual effects, UMAP has become the mainstream choice for single-cell visualization, and its application in Seurat and Scanpy systems is now the default configuration.

[0060] In summary, both t-SNE and UMAP play important roles in single-cell analysis. The former excels at highlighting local differences between cell populations, while the latter performs better in presenting continuous spectral structures and is suitable for large-scale datasets. Researchers can flexibly choose the appropriate dimensionality reduction method based on the analytical objectives and the characteristics of the experimental data.

[0061] Batch effect integration method In multi-sample single-cell studies, data integration is an unavoidable step, and the batch effect is one of the most concerning factors during the integration process. The batch effect refers to expression differences caused by non-biological factors, which may stem from experimental conditions, sample preparation procedures, sequencing platforms, reagent batches, or even operator differences. For example, even if the subjects are from the same donor population, slight differences in tissue dissociation methods between two laboratories can lead to a significant increase in stress-response gene expression in one sample. Similarly, differences in sequencing depth or capture efficiency can introduce technical bias. The presence of the batch effect can severely interfere with subsequent analyses, such as misinterpreting technical errors as biological changes during clustering, annotation, or time-fitting analyses. Therefore, in multi-batch data integration, preserving genuine biological differences while avoiding interference from technical errors is a crucial issue in single-cell analysis.

[0062] It is important to note that not all cross-sample differences should be considered batch effects. Some phenotypes may have inherent biological significance, such as sex, tissue origin, or pathological condition. If the research objective is to compare differences across conditions, overcorrection can mask the target variation. Therefore, whether batch effects need to be addressed depends on the research question and experimental design. For example, when studying the vaccine immunization process, inter-donor variation may be considered noise and needs to be integrated; while when studying sex differences, sex variation should be preserved.

[0063] To address the batch effect problem, a series of linear embedding model integration methods have been proposed in the single-cell domain. These methods typically rely on Singular Value Decomposition (SVD) or similar linear embedding processes to project data from different batches into a shared latent space. Within this space, local adaptive correction is achieved by finding nearest neighbor cells across batches. Typical methods include Mutual Nearest Neighbors (MNN), Harmony, and Scanorama. MNN, which achieves pairing correction by finding the nearest neighbor cells between different batches, is an early representative of this approach. Harmony further employs an iterative optimization strategy, continuously adjusting the data distribution in the latent space to align samples from different batches while maintaining cell type differences. Compared to MNN, Harmony has better scalability and can handle more complex experimental designs, such as integrating samples from multiple donors or tissues. Scanorama, another method, achieves cross-dataset integration through feature matching-based concatenation, demonstrating particularly strong performance in cross-platform data merging.

[0064] As single-cell research deepens, batch integration is no longer limited to the transcriptome level but extends to multimodal integration scenarios, such as the combined analysis of scRNA and scATAC. These scenarios rely more heavily on the expression capabilities of the embedding model. Currently, these integration methods have become an indispensable part of the single-cell experimental workflow, providing a reliable foundation for subsequent clustering, annotation, and transcriptional analysis.

[0065] Reference dataset In single-cell transcriptome analysis, the appropriate selection and use of reference datasets are fundamental to algorithm evaluation, analytical method development, and platform validation. This invention relates to several representative data resources, including the Peripheral Blood Mononuclear Cells (PBMC) dataset provided by 10X Genomics, The Cancer Genome Atlas (TCGA), the CellChatDB cell communication analysis database, and the PanglaoDB single-cell expression resource. These data resources cover a wide range of applications, from basic immune cell type analysis to large-scale tumor expression profiling, providing rich data support for the development and advanced functional validation of single-cell sequencing platforms.

[0066] PBMCs are one of the most commonly used standard datasets for single-cell RNA sequencing analysis because they contain various immune cell types, such as T cells, B cells, natural killer cells, and monocytes, making them well-suited for demonstrating cellular heterogeneity analysis workflows. The free PBMC dataset provided by 10XGenomics is a classic example, especially the version containing 2700 single cells. This dataset was originally sequenced using the Illumina NextSeq500 sequencing platform and has been promoted as an example dataset in the official tutorials of analysis tools such as Seurat, serving as a standard case study for introducing single-cell techniques and validating methods.

[0067] In this dataset, gene expression in each cell is identified using a unique cell barcode and UMI tag. Preprocessing yields a gene-cell expression matrix. The high heterogeneity of the PBMC dataset makes it suitable for evaluating the performance of different dimensionality reduction, clustering, and cell annotation methods, such as t-SNE and UMAP visualization, and Seurat's graph-based clustering. Local or online tutorials typically begin with this dataset, including steps such as quality control, screening for highly variable genes, principal component analysis, and interpretation of clustering results. Essentially, this dataset provides algorithm developers and bioinformatics learners with a stable, readily available, and biologically meaningful analytical foundation.

[0068] It is worth noting that with the development of sequencing technology, 10XGenomics has released more PBMC-related datasets, such as multi-donor PBMC samples containing more than 5,000 cells and CITE-seq data on protein expression. Although this invention mainly uses the classic 2,700 PBMC dataset for method examples, these extended datasets can also be used for testing and comparison of larger-scale or more complex analysis workflows.

[0069] TCGA is one of the most important international cancer omics research projects, jointly launched in 2006 by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). Its aim is to systematically reveal the molecular characteristics of major human malignancies through large-scale sequencing and the integration of related multi-omics data. TCGA contains a wealth of data, including clinicopathological information, single-virus mutations (SNVs), gene expression, microRNA expression, DNA methylation, copy number variations (CNVs), and other data types, covering dozens of human cancers and their subtypes.

[0070] TCGA data is stored and shared through the U.S. National Cancer Data Portal, allowing researchers to download data at different stages according to project needs: from raw sequencing data (FASTQ, etc.) to aligned BAM files, and then to processed levels including standardized expression matrices and clinical annotation information. Due to its large sample size, complete clinical annotations, and rigorous quality control processes, TCGA data has become an important data source for cancer biology research, especially in areas such as survival analysis, molecular subtyping, and the mining of pathology-related expression patterns.

[0071] While TCGA does not directly provide single-cell data in the development of single-cell transcriptome platforms, it plays a crucial role in the design and validation of the "pan-cancer analysis" module. By leveraging TCGA's bulk RNA sequencing expression profiles and clinical information, findings at the single-cell level can be correlated with the macroscopic expression behavior of the entire tumor population, thereby assessing the commonalities and differences of certain gene clusters or cell type populations across different tumor types. For example, when constructing the pan-cancer functional module of a single-cell platform, TCGA's overall tumor expression data can be used to compare cell population characteristics, further exploring the contribution of the single-cell level to cancer pathology.

[0072] Therefore, TCGA, as a large-scale data resource spanning multiple cancer types and omics dimensions, is not only a benchmark library for medical statistics and tumor biology research, but also provides empirical support for multi-omics joint analysis, cell type functional interpretation, and platform function expansion.

[0073] CellChatDB is the core ligand-receptor interaction database of CellChat, a cell communication analysis tool. It was manually compiled and constructed using extensive literature evidence. Cell-to-cell communication is crucial for understanding biological processes such as tissue function and immune network regulation, and ligand-receptor interaction databases are the fundamental resources for this analysis. CellChatDB integrates multiple authoritative resources, such as the KEGG signaling pathway database, and supplements it with information on extracellular matrix (ECM) receptor interactions and co-stimulatory / co-inhibitory membrane receptors, providing robust data support for more comprehensive cell communication analysis. It contains over 3300 validated molecular interaction pairs, covering multiple signaling pathway categories. These include paracrine / autocrine signals, ECM-receptor interactions, cell-contact-related interactions, and non-protein signals (such as metabolic signals and synaptic signals). This rich coverage of signal types means that researchers can extract communication mechanisms applicable to different tissues and environments from the database, not just classic hormone or cytokine signals.

[0074] In the analysis of single-cell datasets, CellChatDB is frequently used in cell communication modules. It constructs intercellular communication networks by mapping ligand and receptor expression data from single-cell expression profiles to a list of interaction pairs in the database. These networks can then be used to create visualizations such as communication maps and heatmaps, allowing analysis of how different cell types influence each other's functions through signal transduction. For example, in the tumor microenvironment, the frequency and intensity of ligand-receptor interactions between immune cells and tumor cells can reveal mechanisms of immune escape or inflammatory responses. Therefore, CellChatDB plays a crucial role in analyzing cell function and interactions.

[0075] PanglaoDB is a comprehensive database focused on single-cell RNA sequencing data, aiming to provide the scientific community with a convenient platform for single-cell expression data. This database collects a large amount of human and mouse single-cell data from publicly published studies and processes it through a unified, standardized workflow, enabling data from different platforms and protocols to be explored and compared within the same framework.

[0076] PanglaoDB currently contains over 1300 single-cell RNA sequencing samples, covering more than 250 tissue types and data from various experimental conditions, with a total data volume of over 5.5 million cells, making it one of the largest integrated single-cell expression databases available in public resources. The database offers various functions, including gene- or cell-type-based searches, dataset browsing, and tool support, enabling researchers to easily find expression patterns, marker genes, or cell-type distributions of interest. PanglaoDB not only provides expression matrix data but also integrates a cell marker gene library. This library, manually verified and sourced from literature, covers thousands of gene-cell-type associations, providing important references for automated cell-type annotation and data exploration.

[0077] As a comprehensive single-cell resource library, PanglaoDB is of great value for inter-tissue comparison, marker gene mining, and cell function research. It is an important reference set for artificial annotation of cells based on marker genes.

[0078] Cell annotation methods With the expanding applications of single-cell RNA sequencing technology in immunology, tumor biology, and developmental research, accurately identifying the type of each cell has become a core issue in downstream analysis. Different cell types possess different functional states and molecular characteristics, and these differences are often closely related to physiological processes, tissue homeostasis, and disease development. Therefore, cell annotation not only helps explain the heterogeneity observed in single-cell data but also provides fundamental biological support for subsequent differential gene analysis, cell communication analysis, and pseudo-temporal analysis.

[0079] Currently, cell annotation methods can be broadly categorized into three types: the first is traditional manual annotation methods based on marker genes, relying on specific gene expression patterns and immunological knowledge; the second is automated annotation methods based on reference datasets, typically represented by SingleR and SCINA; and the third is models based on machine learning or deep learning, such as Celltypist and TOSICA, which identify cell types by training classifiers. In recent research, some tools have begun to incorporate large-scale language models for cell annotation, utilizing literature knowledge and database information to assist in inferring cell identity and giving cell annotations better interpretability.

[0080] Given that the cell annotation module integrated in this invention platform employs different types of algorithm mechanisms, this invention will focus on introducing the SingleR method based on the reference expression matrix and the Celltypist method based on the machine learning model, and discuss their applicable scenarios and advantages in single-cell analysis workflows.

[0081] SingleR is an automated annotation tool for single-cell RNA sequencing data. Its core idea is to use an existing labeled reference expression matrix to perform similarity matching on unknown cells, thereby predicting their cell type. Compared to traditional manual annotation methods that rely on marker genes, SingleR reduces reliance on domain-specific knowledge and improves the automation and objectivity of the cell annotation process. Therefore, since its inception, SingleR has been widely used in cell type prediction tasks for immune cells, tumor cells, and even mixed multi-tissue samples.

[0082] SingleR's core algorithm relies on a "reference expression profile matching" strategy: given a reference dataset with cell type annotations and a query dataset to be annotated, the tool first calculates the expression similarity between the two. To avoid the influence of individual gene expression noise on the results, SingleR typically uses statistical features of the gene set (such as average or median expression) and quantifies the expression consistency between cells using measures such as Spearman or Pearson correlation coefficients. Based on this, the tool searches for the closest expression pattern in the reference dataset for each cell to be annotated and returns the corresponding cell type as the annotation label. To improve the robustness of predictions, SingleR employs a stepwise fine-tuning strategy: genes with low discriminative power are removed based on the initial matching results, the recalculation of the relevance of the expression matrix is ​​performed, and a secondary determination is made for the candidate cell types. This process reduces misjudgments when cell types are highly similar or share marker genes, thus improving annotation accuracy.

[0083] SingleR's advantages lie in its ability to use publicly available databases as reference sources without requiring model training, such as ImmGen, Blueprint+Encode, and HumanPrimaryCellAtlas, giving it strong versatility and adaptability. Due to its speed and lack of need for marker gene selection, this tool is particularly suitable for large-scale immune cell annotation tasks. However, SingleR also has certain limitations, such as its dependence on the quality and coverage of the reference data. When a certain cell type is not present in the reference data, the tool may generate incorrect labels or struggle to distinguish similar cell subpopulations.

[0084] Celltypist is an automated cell type annotation tool based on machine learning methods. Its goal is to train a classification model using large-scale single-cell expression data, enabling rapid and scalable prediction of unknown cell types. Compared to tools that focus on matching reference expression patterns, Celltypist formalizes the cell annotation problem into a supervised learning task through statistical modeling and machine learning techniques, enabling it to efficiently classify large sample data and possess strong generalization capabilities.

[0085] From an algorithmic perspective, Celltypist employs an optimized logistic regression model as its core classification tool, trained using stochastic gradient descent (SGD). Logistic regression, a linear classification algorithm, can handle expression matrices with high-dimensional input features, while single-cell RNA sequencing data itself possesses thousands or even tens of thousands of feature dimensions. Therefore, this design achieves a balance between performance and efficiency. Simultaneously, Celltypist utilizes sparsity strategies and regularization techniques during training to improve the model's tolerance to noisy and low-expression genes, enabling it to identify cell subtypes in complex tissue samples.

[0086] A key feature of Celltypist is its pre-trained model ecosystem. The team behind this tool has built large-scale classification models based on publicly available single-cell sequencing resources for immune cells, covering everything from major immune cell types to fine-grained subpopulations, such as T cell subtypes, B cell developmental stages, and myeloid cell lineages. These pre-trained models significantly lower the barrier to entry for users, allowing researchers to obtain high-quality annotation results without having to re-annotate complex immune cell atlases. Furthermore, the tool supports users uploading their own expression matrices for model training, enabling it to adapt to cell classification needs under specific tissue, disease, or experimental conditions, thus enhancing its adaptability in research environments.

[0087] Overall, Celltypist represents an important direction in the evolution of single-cell annotation tools towards a supervised learning paradigm. Its efficient model inference capabilities, scalable model architecture, and deep coverage of the immune cell domain make it a promising tool for studying tissue immune atlases and disease microenvironments, providing powerful support for the automated annotation of complex biological samples.

[0088] Introduction to Artificial Intelligence Methods Machine learning (ML) is a technological system that automatically learns patterns from data to make predictions or decisions. Its core idea is to train models using samples so that they can still generalize reasonably well to unseen data. As an important branch of artificial intelligence, machine learning has played a crucial role in many fields, including signal processing, computational biology, natural language processing, recommender systems, and computer vision.

[0089] From a model paradigm perspective, machine learning can be broadly categorized into three main types: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning trains models using input-label pairs, with typical tasks including classification and regression. Common algorithms include logistic regression, support vector machines (SVM), random forests, and gradient boosting trees. Logistic regression excels at handling linearly separable problems, offering interpretable parameters and low computational cost. Support vector machines achieve classification by maximizing margins, maintaining good performance even with high-dimensional features. Ensemble learning models like random forests enhance robustness through decision tree combinations, enabling them to handle complex nonlinear feature structures. Unsupervised learning is used to find internal structural patterns in data, such as K-Means, spectral clustering, principal component analysis (PCA), and hierarchical clustering, exploring structure through data distribution relationships. Semi-supervised learning leverages a large amount of unlabeled data to assist a small number of labeled samples in learning, addressing learning needs in scenarios where labeling is costly or difficult.

[0090] From an optimization perspective, machine learning training typically involves updating model parameters through loss functions and optimization algorithms, with gradient descent and its variants being the most commonly used optimization strategies. While traditional batch gradient descent theoretically converges stably, it is computationally expensive on large datasets; therefore, stochastic gradient descent (SGD) or mini-batch gradient descent (Mini-batchSGD) is more frequently used in practice. Furthermore, adaptive optimization algorithms such as Adam and RMSProp further accelerate model convergence by adjusting the learning rate.

[0091] As model and data scales continue to grow, machine learning is gradually expanding towards deep learning. However, classic machine learning methods are still widely used due to their good interpretability, low computational requirements, and stability, especially in research scenarios that require structured data analysis, strong model controllability, or limited sample size.

[0092] Retrieval-Enhanced Generation (RAG) is a hybrid artificial intelligence framework that combines information retrieval and generative models. Its core idea is to retrieve relevant information from external knowledge bases before or during content generation to supplement knowledge not included or not fully reliable in the model's own parameters. Compared to traditional pure generative models, this method significantly improves the accuracy, controllability, and timeliness of the model in knowledge-intensive tasks.

[0093] The basic process of a Relational Acyclic Graph (RAG) typically includes two key stages: retrieval and generation. In the retrieval stage, the input is first encoded as a vector representation and then matched against pre-stored documents or structured information in a knowledge base to select content relevant to the current question. In the generation stage, the model uses the retrieval results as supplementary information to generate answers or content through a language model. The inclusion of retrieval results provides information support for the output, rather than relying solely on the model's parametric memory, thus avoiding "illusionary" content generation caused by missing training data.

[0094] The retrieval module typically relies on vector retrieval techniques, including traditional sparse retrieval methods such as TF-IDF and BM25, as well as deep learning-based dense retrieval methods, such as using BERT or Sentence-Transformer to encode semantic vectors for similarity searching. Compared to sparse retrieval, dense retrieval performs better in semantic matching and contextual understanding, making it particularly suitable for long texts or domains requiring specialized knowledge. Vector retrieval is often combined with efficient indexing structures such as FAISS and HNSW to support real-time retrieval of large-scale knowledge bases. The generation module mostly employs pre-trained language models, such as GPT and T5. Because the RAG mechanism decouples the knowledge base from the model parameters, the model does not need to encompass all factual content during pre-training, allowing for a more flexible balance between parameter size and knowledge coverage. More importantly, the knowledge base can be dynamically updated, giving the system excellent knowledge timeliness without the need to retrain or fine-tune the generation model.

[0095] Compared to standalone end-to-end generative models, retrieval augmentation frameworks (RAGs) are more transparent and interpretable, allowing researchers to explicitly trace content sources and improve the credibility and traceability of results. Therefore, RAGs are considered a crucial approach to addressing the knowledge reliability issue in generative models and are becoming an important component of the large-scale language model ecosystem.

[0096] Few-shot learning (FSL) is a machine learning method that aims to train and infer models using a very small amount of labeled data, contrasting with traditional supervised learning that relies on a large number of labeled samples. In practical applications, many fields cannot conduct large-scale training due to high data acquisition costs, difficult labeling, or naturally scarce samples, such as medical diagnosis, rare event detection, security, or financial scenarios. Therefore, few-shot learning has become an important way to improve the adaptability of models.

[0097] The development of few-shot learning has mainly gone through three directions: transfer-based learning, data-based learning, and meta-learning-based learning. The first type of method relies on transfer learning, which uses a model pre-trained on large-scale data as a feature extractor and then transfers knowledge to new tasks with insufficient data. Common strategies include fine-tuning the pre-trained model and freezing some layers to train only high-level classifiers, thereby reducing dependence on the number of samples. The second type of method emphasizes data-level compensation, increasing sample diversity through data augmentation and generative models to synthesize samples. Models such as generative adversarial networks and variational autoencoders perform well in scenarios with scarce samples because they can expand the data distribution space without increasing the cost of real sampling. The third type of method belongs to meta-learning, which is "learning how to learn." Its core idea is to enable the model to quickly adapt to new tasks by training across multiple few-shot tasks. Typical models such as MAML (Model-Agnostic Meta-Learning) learn at the gradient optimization level, while metric-based meta-learning methods achieve recognition by constructing class prototypes through a metric space. This type of method is particularly suitable for tasks with many label types but very few samples per class.

[0098] In this invention, few-shot learning has a direct application need. Cell types exhibit long-tailed distributions, and some cell subpopulations occur with extremely low frequencies, while large-scale annotation processes are costly. Therefore, few-shot methods have potential value in tasks such as cell type prediction, subpopulation identification, and even cross-species annotation. Simultaneously, data generation and transfer learning frameworks can also help researchers transfer knowledge across different experimental platforms, tissue sources, or species, thereby improving the model's generalization performance.

[0099] Transfer learning is a machine learning method that improves the performance of a target task by leveraging knowledge learned from the source task and source data. In traditional machine learning, models typically assume that training and test data come from the same distribution. Transfer learning, however, breaks this limitation, enabling models to transfer knowledge across data distributions, tasks, and even domains. Therefore, transfer learning is of significant value in scenarios with inconsistent data distributions, insufficient labeled samples, or high acquisition costs.

[0100] The core mechanisms of transfer learning include the transfer object, transfer method, and transfer scenario. The transfer object can include features, model parameters, structural representations, and tasks. Transfer methods mainly include parameter fine-tuning, feature reuse, domain adaptation, and relational transfer. Domain adaptation addresses situations where there are statistical distribution differences between the source and target domains, such as improving cross-domain generalization ability through distribution alignment, minimizing distance metrics, or adversarial training techniques. Transfer scenarios can be categorized into cross-task, cross-domain, and cross-modal transfer, with different forms adapting to different practical needs.

[0101] In single-cell transcriptome analysis, transfer learning is primarily manifested in cell type identification and state prediction across research groups, experimental platforms, or patients. For example, once a model learns the expression characteristics of T cells in liver cancer patient samples, it can be transferred to another group of liver cancer patient data to identify the corresponding T cells, thus avoiding the need for repeated, costly annotation processes. Furthermore, once a model learns the characteristics of T cells in HBV-positive liver cancer patients, it can be transferred to other types of liver cancer patients, regardless of whether they are infected with HBV, thereby achieving cross-condition cell identification capabilities. These types of problems essentially fall under the category of cross-domain or cross-condition transfer, helping to overcome the model generalization barriers caused by the heterogeneity of biological data.

[0102] This invention discusses the core technologies required for building a single-cell analysis platform, focusing on the Seurat framework, including quality control, dimensionality reduction analysis, graph-based clustering methods, and batch effect elimination. Subsequently, it introduces cell annotation methods closely related to the platform's functionality, elaborating on the theoretical mechanisms, advantages, and applicable scenarios of the reference data-based SingleR tool and the machine learning-based Celltypist tool, providing an algorithmic foundation for subsequent module development. Furthermore, this invention introduces the basic principles of artificial intelligence methods such as machine learning, few-shot learning, and transfer learning, and their potential value in complex data environments, briefly discussing their applicability in single-cell scenarios.

[0103] Overall architecture design After introducing the relevant theories and key technological background of single-cell transcriptome analysis, this invention will focus on the system implementation of a single-cell analysis platform. This invention aims to construct an interactive analysis platform for researchers and clinical data analysis users, realizing an integrated workflow from data upload, processing, analysis to visualization, thereby lowering the operational threshold of single-cell RNA sequencing analysis and improving analysis efficiency. This invention will sequentially introduce the platform's overall architecture, system construction process, and implementation details of basic functional modules, demonstrating the implementation strategy of the cell annotation module, laying the foundation for subsequent expansion of advanced analysis functions.

[0104] This invention constructs an interactive analysis platform based on a front-end and back-end separation architecture. The overall system structure is as follows: Figure 1 As shown, the platform uses a browser as the primary interaction interface. The front-end interface enables user identity management, project configuration, data uploading, parameter selection, and result display, providing a user-friendly experience for users without programming skills. The back-end handles business logic processing and analysis task scheduling, distributing analysis requests to different computing modules and ultimately returning analysis and visualization results. Furthermore, the platform is equipped with a storage module to record user data, project information, runtime parameters, and analysis results, ensuring the experimental process is traceable and repeatable, demonstrating strong data management capabilities.

[0105] The platform is structured in four layers. In the view layer, the user interface is built using the Vue.js framework based on JavaScript. A component-based structure allows for the separation and management of different functional modules, such as project management, data upload, parameter configuration, and visualization. This enables the system interface to dynamically update based on user actions, improving platform interactivity and efficiency. The control layer uses the Flask framework to implement web services and task processing interfaces. It interacts with the front end via HTTP requests while maintaining decoupling from the analysis modules. Upon receiving a task, the control layer passes the data to the algorithm layer for processing, including data preprocessing, normalization, high-variance gene identification, cell clustering, dimensionality reduction analysis, and subsequent analysis workflows. In the data layer, the system uses a relational database, MySQL, to store user behavior-related information, including user accounts, project information, task status, and runtime parameters. Single-cell sequencing data and analysis result files are stored in the file system to accommodate the storage needs of large-scale matrix data, visualization files, and intermediate results. This separation of the database and file system not only improves the flexibility of the storage structure but also avoids performance issues caused by mixing structured and unstructured data.

[0106] Overall, this platform architecture follows modular and loosely coupled design principles. The front-end handles interactive presentation, the back-end handles logic processing and analysis scheduling, and the data layer handles recording, forming an integrated toolchain for single-cell transcriptome analysis for biomedical users. This architecture provides a solid technical foundation for the implementation of subsequent basic functions and the expansion of advanced analysis modules.

[0107] Front-end implementation and user interface After clarifying the overall system architecture and core module division, this invention further systematically constructed and engineered the single-cell transcriptome analysis platform. This platform adopts a front-end / back-end separation model to improve system maintainability and scalability. The construction process follows modular design principles, implementing each layer separately and connecting them through interfaces, enabling effective collaboration and task scheduling between different functional modules.

[0108] The platform's front-end is implemented using the Vue.js framework, primarily handling user interaction and visualization. Vue.js's lightweight, component-based, and responsive design facilitates the construction of complex interfaces while maintaining high execution efficiency. At the interaction level, to improve page responsiveness and data rendering performance, the front-end employs a virtual DOM mechanism, allowing updates to analysis results to be directly mapped to the interface display, facilitating real-time feedback and graphical visualization. In terms of project structure, the front-end uses VueCLI to initialize the project and Webpack for dependency management and compilation.

[0109] The front-end routing uses VueRouter for page navigation and module organization, supporting users to switch between different task pages while maintaining the state in the browser's single-page mode. For cross-component data sharing and unified state management, the system uses Vuex as a global state management solution, centrally storing key data such as user information, project configuration, and task status, avoiding redundant logic generated by multi-layer component communication, and improving system maintainability.

[0110] The UI components are developed using a component-based approach. The platform front-end is divided into several core page modules, including user management pages, project and data management pages, parameter configuration pages, and result display pages. (1) User Management Page: Used for login and registration, identity authentication, and personal information protection. The user account system supports independent management of different projects, ensuring user task isolation and data security. After successful login, the system enters the main control panel and displays a list of manageable projects.

[0111] (2) Data Upload Page: Used for project initialization and raw data import. Users can upload single-cell sequencing data files that meet the format requirements through form controls. During the upload phase, the system only performs basic checks (such as suffix checking, file size limit, and format confirmation) and does not involve analysis logic processing to avoid blocking front-end interaction.

[0112] (3) Parameter configuration page: used to select the parameters required for the analysis process, including filtering threshold, dimensionality reduction method, clustering method, cell annotation mode, etc. The front end sends the parameters set by the user to the back end via JSON structure. The front end does not participate in the specific calculation, but only undertakes parameter collection and verification, avoiding coupling of analysis logic and reducing the complexity of the front end.

[0113] (4) Results Display Page: This page displays the analysis output and visualizations. The system supports displaying multiple views at once, such as dimensionality-reduced scatter plots, gene expression heatmaps, clustering results, or differential expression results. Users can choose to browse directly or download the output files via the download button for secondary analysis or local archiving.

[0114] The platform's user workflow is designed following a step-by-step approach, enabling users without a bioinformatics background to successfully complete analysis tasks. A typical workflow is as follows: ① Users log in to their accounts to access the system; ② Create a project and upload the raw single-cell sequencing data; ③ Set the analysis parameters in the parameter configuration interface; ④ Submit the project task and wait for backend processing; ⑤ After completion, enter the results display interface to view the graphs and statistical output; ⑥ Save the charts or export the results file as needed.

[0115] Component-based design not only improves code reusability but also facilitates subsequent expansion and feature replacement. Furthermore, to reduce style development costs and maintain layout consistency, the system integrates the ElementUI component library for building commonly used interface components such as forms, tables, pagination, dialog boxes, and buttons, ensuring consistent interactive behavior and visual standards across different modules. Through step-by-step interfaces and modular task organization, the platform's front-end significantly lowers the barrier to entry for single-cell transcriptome analysis, allowing users to complete standard analysis workflows without writing code, while reserving sufficient interface space for future advanced feature expansions.

[0116] Backend Implementation and Data Management The platform's backend uses Flask as its main framework, responsible for handling business logic, task scheduling, and interaction with the computing modules. Flask is lightweight, has a clear structure, and offers ample room for expansion, facilitating the later integration of analytics components and multi-language computing modules. In the typical request processing path, Flask receives parameters and task information from the frontend, verifies them, and then distributes them to the appropriate analytics routines or task queues for execution. Finally, it outputs the results to the file system or database for the frontend to read.

[0117] At the implementation level, the backend comprises two core modules: a control layer and a computation layer. The control layer is primarily responsible for receiving requests and returning responses, including file upload interfaces, parameter configuration interfaces, task submission interfaces, and result retrieval interfaces. To avoid frontend blocking of analysis and computation, the control layer does not directly execute computations but adopts an asynchronous task design, encapsulating the computation process within the computation layer. The computation layer is responsible for executing logic related to the single-cell analysis workflow, such as data preprocessing, dimensionality reduction, clustering, and differential analysis modules. Since some processing involves statistical analysis and graphical output, the computation layer integrates both Python and R execution environments. The Python module is mainly used for data transformation and pipeline scheduling, while the R module is used to execute tools such as Seurat. These two are decoupled through subprocess or script interface calls. During the visualization result generation stage, analysis graphs are output to the file system as images or interactive objects, which the frontend can obtain through a download interface.

[0118] Another key aspect of the backend design lies in the task execution model. Single-cell analysis is a computationally intensive task with a long execution time; therefore, the system employs an asynchronous execution approach. After a user submits a task, the backend immediately returns a task number, which the frontend can then use to query the progress via polling or a status interface, without blocking the user interface. Once the computation is complete, the backend writes the results to the corresponding directory and updates the database status to indicate success or failure. For the R script execution portion, an independent process isolation method is used to prevent analysis errors or crashes from affecting the main service.

[0119] The platform's data management is handled jointly by the database system and the file system. The database stores structured metadata, while user-uploaded data files and analysis outputs are stored in the file system. This hybrid structure avoids performance pressure caused by large files entering the database, while ensuring the traceability and status logging of user projects. MySQL, a relational database management system, is chosen because of its high maturity, clear syntax, low deployment and maintenance costs, and support for user access control and pre-emptive transaction control, making it suitable for multi-user analysis platform scenarios.

[0120] The database table design revolves around the "user-project-task" relationship, forming a three-level association structure. The user layer, as the top-level entity, corresponds to the platform's user account system; the project layer manages each analysis process and file space; and the task layer details the execution records of individual analysis steps. The user table stores platform identity information, including login credentials, registration time, and account status. This table uses an auto-incrementing primary key as a unique identifier and employs hash storage on the password field to ensure security, avoiding the storage of plaintext credentials. The project table records basic metadata for each project, such as project name, creation time, species information, data path, and running status identifier. It also includes foreign keys to bind to the user table to support one-to-many relationships. Project data space and database records coexist; when a user deletes a project, the system synchronously cleans up the corresponding file directory to prevent residual files from occupying disk space. The task table tracks analysis operations, including fields such as task type, parameter configuration, submission time, execution result, visualization file path, and running log path.

[0121] The platform implements several auxiliary mechanisms at the database level. Both task and project records include timestamp fields, displayed in reverse chronological order for easy user location of recent tasks. Parameter fields are stored in JSON format, eliminating the need for fixed field templates for different analysis types and facilitating future expansion to other analysis tasks. To support multi-user scenarios, the database incorporates access control; the platform's query interface verifies user identity, preventing users from accessing project or task records created by other accounts. Furthermore, the system employs a soft-delete strategy: when the front-end performs a deletion operation, only the status field is updated, triggering an asynchronous background file cleanup task to prevent long-running deletions that could block the user's view.

[0122] In terms of file management, the platform divides the file system into a raw file area, an intermediate file area, and a results area. The raw file area stores user-uploaded expression matrices or raw data files, the intermediate file area stores temporary data generated during analysis, and the results area stores graphs, tables, and downloadable data packages. Only data in the results area is retained long-term; intermediate files are automatically cleaned up after analysis is completed or the project is deleted to avoid space accumulation. For naming, files use a combination of "project ID + task ID + timestamp" to prevent overwriting conflicts and facilitate comparison of results by users.

[0123] Through the joint design of the database and file system, the platform achieves project-level data isolation, task-level traceability, and long-term result retention, meeting the multi-step processes and reproducibility requirements commonly encountered in single-cell analysis. Furthermore, this design reserves room for future expansion by introducing task queues, parallel computing, or container-based execution environments without altering the existing database model.

[0124] Basic Analysis Module Implementation The platform's basic analysis modules primarily implement the core steps in the single-cell transcriptome data analysis workflow. These functions cover the general workflow for most single-cell analysis tasks, providing the necessary input foundation and structured output for subsequent advanced analysis modules (such as gene enrichment analysis, transcription factor analysis, cell communication analysis, pseudo-temporal analysis, and copy number variation analysis).

[0125] In single-cell transcriptome analysis, data preprocessing and quality control are the first modules executed, and their results directly affect the accuracy of subsequent normalization, dimensionality reduction analysis, and cell annotation. This platform follows the basic workflow of mainstream analysis frameworks (such as Seurat and Scanpy), and incorporates task decomposition and user interface design based on specific needs to achieve automated processing and visual feedback.

[0126] The platform allows users to set thresholds for common QC variables, including the number of genes detected per cell and the total number of UMIs. (Count depth) and mitochondrial gene proportion, etc. Generally, dead cells will show a higher proportion of mitochondrial expression, while empty droplets or low-quality cells will show excessively low gene counts or count depths; in addition, double-cell contamination may lead to abnormally high count depths. To avoid accidental deletion due to biological factors, this platform allows simultaneous observation of the distribution of multiple indicators and visually presents QC variables through violin plots, allowing users to make judgments before filtering. This visual diagram is generated by the backend R module, and the frontend is responsible for rendering and exporting.

[0127] After a task is submitted, the platform performs filtering based on the user-defined thresholds and generates corresponding QC visualization results. Regarding specific filtering strategies, the platform provides default QC thresholds to adapt to common datasets (such as PBMC), and also allows users to customize thresholds for different tissue sources or sequencing platform scenarios. For example, in tumor samples, some immune cells may naturally exhibit high mitochondrial content; in this case, users can disable the filtering function for this indicator to avoid accidental deletion. To enhance flexibility, the system allows users to view the filtered cell statistics before submitting the next task, enabling parameter adjustments and reruns.

[0128] After quality control is completed, the platform enters the feature gene screening stage. Single-cell transcriptome data contain a large number of genes with low or almost unchanged expression levels. These genes not only contribute limitedly to downstream structural learning but also increase the complexity of dimensionality reduction and clustering calculations. To improve analysis efficiency and modeling quality, it is usually necessary to first identify a group of genes with high variability as feature genes to participate in subsequent analysis. This step has become a standard module in mainstream single-cell analysis workflows, with tools such as Seurat, Scanpy, and CellRanger all providing similar interfaces.

[0129] In the standardization process, we employ a global scaling method to normalize the differences in total expression levels among different cells, followed by a log transformation to reduce skewness in the expression distribution. Users can use z-score standardization to improve the ability of subsequent PCA to capture differential features.

[0130] In the identification of highly variable genes, the platform employs a screening strategy based on gene normalized variance. The core idea is to calculate the relationship between the variance and mean of gene expression across different cells, and then eliminate statistical bias caused by low-expression genes through normalization. Since single-cell data exhibits significant dropout, directly using the raw variance often underestimates the contribution of highly expressed genes. Therefore, we use a normalized variance method based on the mean-variance relationship, retaining the top-ranked genes as the feature gene set. The platform defaults to screening 2000 feature genes, a commonly used scale in current bioinformatics analysis, but users can adjust this scale according to data volume or research objectives.

[0131] This platform encapsulates the highly variable gene screening module as a reusable task node and enforces it as a mandatory step before data standardization and dimensionality reduction to ensure the stability of subsequent principal component analysis (PCA) and clustering tasks. At the underlying execution level, the platform calls the FindVariableFeatures function provided by Seurat to calculate gene variability, supporting switching between different method types. Parameter configurations are passed from the front-end panel to the back-end, which executes the calculation and outputs a list of feature genes. The execution results are stored as an RDS file.

[0132] After identifying highly variable genes, the platform enters the dimensionality reduction analysis phase. This module standardizes the expression matrix, extracts feature variables, and performs dimensionality reduction projection to provide a structured feature space for subsequent clustering and cell annotation. The underlying analysis tools rely on Seurat's mainstream processing workflows, including principal component analysis (PCA) and visualization dimensionality reduction methods (such as t-SNE and UMAP). We have uniformly encapsulated these computational steps and implemented task scheduling and parameter configurability, enabling the workflow to be triggered by users on the front end and the results to be visualized.

[0133] In the dimensionality reduction analysis module, the platform uses PCA to extract linear principal components to obtain a stable and interpretable feature space. Principal component visualization is presented in the form of scree plots, loading plots, and projected scatter plots, facilitating user assessment of the number and contribution of principal components. For subsequent clustering and visualization, the platform supports further invocation of two nonlinear dimensionality reduction methods, t-SNE and UMAP, to improve the ability to distinguish local structures. From an engineering perspective, the platform decouples linear and nonlinear dimensionality reduction, enabling different task nodes to record parameter settings and execution results through a database, supporting task backtracking and horizontal comparison. The dimensionality reduction results are stored in a data matrix format for subsequent use in the clustering module. For users without modeling experience, the platform provides default configurations, allowing them to complete the analysis process without understanding the algorithm details. To improve the overall user experience, intermediate results are retained between the standardization and dimensionality reduction tasks, allowing users to adjust subsequent analysis parameters without repeatedly executing preceding tasks.

[0134] After completing the screening of highly variable genes and dimensionality reduction feature extraction, the platform enters the cell clustering stage. The goal of clustering is to identify cell populations with similar expression patterns, providing a foundation for subsequent cell type annotation. Currently, graph-based clustering methods are commonly used in single-cell analysis workflows, as they have good adaptability to complex nonlinear data and have become the default strategy for tools such as Seurat, Scanpy, and Phenograph.

[0135] Methodologically, the platform first calculates the Euclidean distance between cells in the PCA space and constructs a KNN graph based on the distance matrix. Each cell is connected to its K nearest neighbors, with the value of K automatically adjusted according to the data scale, typically between 5 and 50. To improve the structural resolution of the graph, we further introduce the concept of shared nearest neighbors, using the Jaccard coefficient to weight the edge weights. This process helps reduce erroneous connections caused by noise, making the graph structure more reflective of local relationships, thereby improving the clustering effect.

[0136] After obtaining the graph structure, the platform uses a modular optimization algorithm for community partitioning. The backend defaults to the Leiden algorithm, which combines convergence speed and connectivity advantages. Compared to the Louvain algorithm, it avoids isolated nodes and disconnected communities, making it more suitable for large-scale single-cell datasets. The clustering granularity is controlled by the `resolution` parameter, which is set to 0.5 by default on the platform and is configurable on the front end, allowing users to adjust the clustering resolution according to their research objectives. For example, resolution can be increased when exploring cell subpopulations, while it can be decreased when observing macroscopic tissue structures. In terms of engineering implementation, the clustering module is encapsulated as an independent task node. Its input includes a dimensionality-reduced matrix and a KNN graph, and it is bidirectionally linked to the front-end parameter panel. The final output includes three types of data: cell cluster labels, cluster visualization graphs, and cluster statistics.

[0137] Cell annotation module implementation After obtaining the cell clustering results, the platform proceeds to the Differentially Expressed Genes (DEG) analysis stage. The goal is to identify genes with significant expression differences among different cell populations, thereby providing a basis for subsequent cell type annotation, functional studies, and pathway enrichment. Differential gene analysis has become one of the most biologically interpretable modules in single-cell analysis; for example, it is relied upon for scenarios such as identifying immune cell subsets and distinguishing between tumor and normal tissue cell subtypes.

[0138] Currently, mainstream tools in the single-cell domain include the Wilcoxon rank-sum test, t-test, negative binomial model, and generalized linear model. Considering the significant dispersion and over-dispersion characteristics of single-cell count data, the platform backend defaults to using a negative binomial distribution-based modeling approach for differential analysis to suit the statistical characteristics of transcript counts. For RNA-seqbulk data, tools such as DESeq2 provide complete modeling workflows; while for single-cell data, Seurat's built-in FindMarkers interface is more commonly used for differential calculations, offering various method options to adapt to different data characteristics.

[0139] This platform designs the differential expression analysis task as an independent module, which can be triggered by the user from the clustering module. The task input consists of cell labels and an expression matrix. Users can select the comparison method on the front-end interface, including single-group versus other groups, one-to-one comparisons, and user-defined groupings. After calculation, the system outputs statistical information on differentially expressed genes (such as gene number, significance ratio, LogFC distribution, etc.). The results are stored in both tabular and graphical formats, facilitating direct use by users for cell annotation or enrichment analysis.

[0140] After completing clustering and differential expression analysis, the platform enters the cell annotation stage. The goal of cell annotation is to assign a biological identity to each cell population, such as T cells, B cells, dendritic cells, or tumor cells. Compared to traditional RNA sequencing analysis, single-cell data is characterized by high granularity and strong heterogeneity; therefore, the annotation stage plays a central role in the entire analysis process. It not only affects subsequent biological interpretation but also directly relates to whether users can derive usable conclusions from the data. This invention introduces several methods used in the platform. After annotation, the platform provides scatter plots and cell proportion bar charts on the front end, allowing users to complete the entire annotation process without coding and intuitively view the annotation results.

[0141] The platform first integrated SingleR, an automated annotation tool based on a reference dataset, for cell type identification. SingleR's core approach is to compare user data with a reference expression matrix labeled with cell type tags, assigning a label to each cell based on the similarity of expression patterns. This method does not rely on a list of labeled genes, making it user-friendly even for those without a biological background. We encapsulated SingleR as an independent backend task node and provided built-in human and mouse immune cell reference datasets. Users only need to select the reference source and submit the task on the front end, without needing to write R scripts or adjust numerous parameters. After task execution, SingleR returns cell labels, which we uniformly convert to JSON format and record in the database for easy integration with other modules.

[0142] In addition to annotation methods that rely on reference expression matrices for comparison, the platform also integrates Celltypist, a cell annotation tool based on machine learning models. Unlike SingleR, Celltypist predicts cell types by training a classification model, thus adapting to more complex cell subtype structures and possessing model portability and reusability. Its core algorithm employs a logistic regression classifier, optimized using stochastic gradient descent, achieving stable convergence on high-dimensional sparse expression matrices and adapting to the statistical characteristics of single-cell transcriptomics.

[0143] Celltypist includes 54 built-in reference models and operates similarly to SingleR. Another approach allows users to upload custom training data to train classifiers tailored to specific research objectives. For example, researchers can upload T-cell data from tumor tissue to train a model, which can then be used for T-cell annotation tasks on other patient samples. This approach embodies the idea of ​​transfer learning: the model does not require training data to cover all future samples; it only needs to capture the key distinguishing features of a particular cell type for transferable use. Furthermore, the tool is compatible with few-shot learning scenarios, requiring only a small number of labeled cells to complete effective model training, which is of practical value when clinical samples are expensive and labeled data is scarce.

[0144] The platform separates Celltypist's training and prediction into two independent modules. The training module receives user-uploaded data and provides label information and hyperparameter setting interfaces. After training, the model file is stored in a unified format, and the trained model appears in a dropdown menu for users to select, avoiding the overhead of repeated training. The prediction module uses the trained model or the platform's built-in model to predict the labels of cells in the user's project.

[0145] The platform provides an annotation model management page in the front-end interface, where users can view trained models or switch model sources. Annotation results are displayed as a visual scatter plot on the front end. By introducing Celltypist, the platform's annotation capabilities have been expanded from static reference comparisons to a trainable model system, making the annotation process scalable, transferable, and available for low-sample conditions, meeting the needs of closed-loop data use in scientific research and clinical practice.

[0146] In addition to methods based on reference data alignment and machine learning models, this platform also introduces a cell annotation function based on a large language model (LLM). LLM is used to handle the matching relationship between cell marker genes and biological knowledge, inferring cell type through marker genes. This type of method does not rely on a fixed annotation database or training dataset and has cross-species and cross-tissue scalability. We referenced the marker gene-driven annotation method proposed in the AICelltype framework and performed corresponding integration and optimization at the engineering level.

[0147] This type of method first extracts a list of marker genes for each cell cluster from the differential expression module, then constructs descriptive cues, and inputs these marker genes into the LLM for pattern matching and semantic reasoning. The model provides candidate labels for cell types based on its internally accumulated biological knowledge and corpus information, while also providing explanations or confidence information for the candidate matches. This process is essentially a knowledge-based reasoning decision-making process. Its advantage lies in its independence from the training set coverage and the requirement that the model has seen samples of the same tissue or disease, thus exhibiting good generalization ability.

[0148] This platform divides this module into three stages: marker gene generation, LLM inference invocation, and result parsing and visualization fusion. Marker genes are provided by the preceding differential gene analysis module. The LLM invocation stage supports model switching; users can choose a built-in model or a custom model interface, with Qwen2.5 as the default. Result parsing is responsible for converting the LLM output into structured labels and completing the visualization.

[0149] This analysis method is suitable for users with a biological background but no programming experience or lack of data annotation resources. However, it must be noted that the answers generated by large models may be misleading. Therefore, in subsequent work, this invention will continue to optimize this aspect through RAG.

[0150] This invention focuses on the construction and basic functional implementation of a single-cell transcriptome sequencing analysis platform, introducing the platform's overall architecture design, main module division, and basic analysis workflow. The platform achieves a complete chain of task management, data processing, and result display through a front-end interactive interface, back-end computing services, and a database storage system, and supports users to complete core analysis operations in a code-free manner. At the basic functional level, this invention implements mainstream single-cell analysis workflows such as quality control, high-variance gene screening, standardization and dimensionality reduction, cell clustering, and differential gene analysis. It also integrates three cell annotation methods, including annotation strategies based on reference data, model training, and large language models, improving the interpretability and applicability of the analysis results.

[0151] Advanced Functionality Overall Process After completing the basic analysis functions, this invention will further introduce the platform's advanced analysis modules and system optimization. Compared to the basic workflow, advanced functions focus more on the analysis of cellular functional states, intercellular interactions, and disease-level biological mechanisms, including gene enrichment analysis, transcription factor analysis, cell communication analysis, copy number variation analysis, pseudo-time series analysis, and pan-cancer analysis modules. This invention will also introduce how to integrate public databases, support comparison of user data with external data, and improve user experience and analytical depth through parameter optimization, task tracing, and visualization. This invention expands from a basic tool into an analytical system with research support capabilities, laying the foundation for subsequent result validation and application promotion.

[0152] After implementing basic functions and expanding modules, this platform has formed a complete processing architecture covering the entire process of single-cell transcriptome analysis, such as... Figure 2 As shown in the diagram, the entire process begins with data upload from the user's end, where the user can submit a sparse expression matrix file and project-related information. Subsequently, the system enters the data processing stage, including basic analysis steps such as cell filtering, standardization, feature gene identification, dimensionality reduction, and clustering. These modules are executed sequentially and output the low-dimensional spatial expression structure of cells and cluster labels.

[0153] After establishing the basic expression structure, the platform enters the cell annotation stage. To adapt to the cell type identification needs under different experimental backgrounds, the platform integrates three complementary annotation methods: SingleR based on the reference expression matrix, Celltypist based on transfer learning and few-sample adaptation, and AIcelltype based on a large model. In addition, the platform also supports generative annotation of cell types based on knowledge retrieval enhancement models to improve the automated annotation capability in complex tissue samples.

[0154] After cell annotation is completed, the platform provides two main types of output for subsequent analysis: one is a set of marker genes based on clustering results, and the other is cell type tags. The marker gene output is used to perform GO enrichment analysis, characteristic gene analysis, and significant difference analysis, while the cell type tags serve as input for advanced modules, used for biological interpretation tasks such as inter-cell communication analysis, copy number variation analysis, pseudo-temporal analysis, and pan-cancer data comparison analysis.

[0155] Through the collaborative flow of data between the aforementioned modules, the platform completes a closed-loop analysis from raw input to biological interpretation, demonstrating high applicability and scalability, and laying the foundation for subsequent functional optimization and application promotion.

[0156] Specific advanced analysis functions After completing basic analysis and cell annotation, users typically need to further explore deeper questions such as intercellular relationships, functional enrichment, biological trajectories, and disease associations. To this end, this platform extends the basic modules with a series of advanced analytical functions and integrates and engineers various analytical tools to support a wider range of scientific research applications.

[0157] Gene enrichment analysis aims to identify overrepresented functional categories based on a list of significantly expressed genes, further understanding the biological functions of specific cell populations, such as whether they are involved in a signaling pathway, metabolic process, or immune response. It is a crucial step in downstream analysis of single-cell data. This platform primarily supports two methods: GO (Gene Ontology) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment. GO enrichment further includes three dimensions: biological process (BP), molecular function (MF), and cellular component (CC). The GO ontology has a hierarchical structure, supporting top-down classification descriptions and accommodating multi-scale expression of cellular states and functional processes.

[0158] This platform employs two commonly used strategies: hypergeometric test and Fisher's exact test, combined with the false discovery rate (FDR) for multiple hypothesis correction to ensure the statistical reliability of the enrichment results. For the KEGG rich set, this platform uses a gene set-based path mapping method to map differentially expressed genes onto metabolic and signaling pathway maps, thereby helping to interpret the dynamic changes of cells under pathological or microenvironmental conditions.

[0159] This platform encapsulates the R-side enrichment analysis interface, integrating packages such as clusterProfiler into the backend computation module. This allows users to complete analysis tasks without needing to be proficient in R, achieving a complete workflow from "differential gene output + enrichment task submission + result visualization." To reduce the burden on users in parameter configuration, the platform presets default parameters such as species information, background gene set source, and statistical correction methods, but advanced users are also allowed to manually customize them. Output results are displayed on the front end as bar charts.

[0160] Transcription factor analysis aims to infer regulatory network structures from single-cell transcriptome data to identify potential dominant regulators and their corresponding target gene modules, and to explain the formation and transition of cellular states through regulatory activity patterns. Since single-cell RNA-seq measures gene expression outcomes rather than directly observed regulatory events, transcription factor regulation analysis requires constructing an indirect inference framework from expression data to regulatory network structures. This framework must reveal the potential connections between transcription factors and target genes, and capture the dynamic changes in these relationships at the single-cell level. SCENIC is a representative method in this category. Its design is based on gene co-expression and regulatory element enrichment signals to construct cell-specific regulatory modules and assess the activity patterns of regulatory factors, thereby providing an interpretable perspective on regulatory networks.

[0161] The SCENIC workflow can be summarized into three interconnected stages: co-expression-based regulatory module inference, regulatory element enrichment filtering, and cell-specific regulatory activity scoring. In the first stage, the algorithm uses the co-expression characteristics of genes in the expression data to infer potential regulatory relationships. Specifically, for each candidate transcription factor (TF), SCENIC calculates its co-expression relationship with the entire gene set and constructs a TF-target gene candidate set using tree-based regression frameworks such as GRNBoost2 or GENIE3. These models treat the expression of a particular gene as an interpretable variable and the expression of all TFs as predictive variables, constructing a set of regression equations to assess the contribution of each transcription factor to the change in gene expression. A preliminary regulatory candidate network can be formed using feature importance or regression weight indices.

[0162] After obtaining the initial TF-target gene pairs, the second phase of SCENIC introduces a regulatory element-based filtering mechanism to enhance the biological reliability of regulatory relationships. The basic assumption of this phase is that while co-expression can provide potential association clues, true transcriptional regulatory relationships should be reflected in sequence-level binding potential. To this end, SCENIC searches for the binding site motif of the TF in the upstream regulatory region of each candidate TF-target gene pair. This process typically relies on a pre-constructed transcription factor binding motif database (e.g., the cis-Target library). When a candidate module is enriched with significant TF binding motifs in its regulatory region, that module is more likely to represent a true regulatory unit and is thus retained as the final regulatory module. After this filtering step, each TF is associated with a "regulon" consisting of its high-confidence target genes. This structure includes both the list of TFs and their target genes and the expression characteristics of these gene sets in specific cell clusters or cellular states.

[0163] In the third stage, SCENIC maps each regulatory subset back to a single-cell expression matrix to assess its regulatory activity in each cell. This step uses strategies such as AUCell (Area Under the Curve for gene set scoring) to quantitatively evaluate the expression enrichment of each regulatory subset in a single cell. Specifically, the algorithm calculates the positional ranking of a cell's target gene set in the expression profile and defines the activation score of that regulon within that cell based on this ranking. A higher activity score means that the overall target gene expression of that regulatory unit tends to be at a higher level, which is biologically interpreted as a higher "active state" of the target transcription factor. By performing this scoring process on all cells and all regulatory units, a regulatory activity matrix is ​​obtained, with rows corresponding to regulons and columns corresponding to cells, and each cell representing the activity level of that regulon in that cell.

[0164] The core of these three steps lies in integrating covariant expression structures, genomic regulatory potential, and cellular expression patterns into a unified analytical framework. This allows transcription factor networks to reflect both cross-cellular commonalities and reveal dynamic activity changes at the single-cell level. Unlike analyses based solely on differential expression, regulatory network inference provides deeper mechanistic insights, helping to identify core regulators driving cellular state transitions and their target genes. It also provides more biologically interpretable input signals for downstream analyses such as temporal regression and cell branching determination.

[0165] In the platform's implementation, SCENIC analysis is encapsulated as an executable pipeline. After the user inputs the expression matrix and gene annotation information, the platform sequentially performs three calculations: co-expression network inference, regulatory element filtering, and regulon activity scoring, and stores the intermediate results of each step in a structured manner. The results include not only the TF-target gene network but also the regulon activity matrix and its enrichment profile in different cell populations, facilitating further visualization, functional annotation, or serving as auxiliary feature input for other analysis modules (such as pseudo-temporal or communication analysis).

[0166] This invention analyzes the regulatory activity of transcription factors. Activity scores for each regulon are calculated based on the SCENIC workflow and integrated into the platform for display. (Bubble chart, e.g.) Figure 13 (A) provides an overview of regulon activity distribution across different cell types; bubble size indicates the positive percentage, and color intensity represents the average activity level; heatmaps (such as...) Figure 13 Figure (B) shows the average regulatory patterns and clustering relationships among different cell populations, indicating that immune cells with similar functions share certain consistency in regulatory characteristics. Taking the transcription factor IRF7 as an example, its regulon activity is distributed in the UMAP space as shown below. Figure 13 As shown in (C), it is relatively high in some monocytes and dendritic cells; violin diagram (as shown) Figure 13 (D) further reveals the activity differences among different cell types. The overall results are consistent with known immunological characteristics, indicating that this module can reflect the regulatory state at the cellular level well.

[0167] Cell-cell communication analysis aims to determine the existence of potential signal transduction events between cell populations using transcriptome data. Single-cell data provides the necessary resolution for this research, enabling researchers to identify signaling pathway regulation during immune cell activation, tumor cell immune escape, or development. This platform uses CellChat as its core analytical tool, which infers intercellular communication networks based on a ligand-receptor (L–R) interaction model and utilizes the manually curated CellChatDB database as prior knowledge, covering both human and mouse species. This database includes information on multi-subunit ligands, receptor complexes, and cofactors, thus allowing for a relatively accurate assessment of signal transduction efficiency.

[0168] During computation, CellChat first integrates cell population expression data, performs aggregated expression calculations for each ligand-receptor complex, and introduces a Michaelis-Menten-like model to consider the nonlinear relationship between expression levels and binding efficiency. Subsequently, CellChat assesses the significance of each communication pathway through random permutation tests and aggregates multiple ligand-receptor pairs to the signaling pathway level, thereby enabling comparative analysis of pathway activity. Finally, the system uses network analysis methods to quantify the differences between cells acting as signal senders, receivers, mediators, or modulators.

[0169] This platform encapsulates the CellChat workflow in the backend, automating the four stages of "data input—model calculation—network visualization—interactive display." Users do not need to install any dependencies; they only need to select cell annotation results as input to initiate a task. In the results display stage, this platform focuses on integrating commonly used CellChat graphs: donut plots are used to display the overall interaction network between cell populations; the width or curvature of the donuts represents the total communication strength of cell populations as senders or receivers, while the chords represent the magnitude of signal intensity between different cell populations, facilitating the identification of dominant communication nodes; heatmaps use rows and columns to represent sending and receiving cell populations, with color intensity reflecting communication strength or the number of LR pairs, thus quickly locating active cell pairs. The platform provides image export capabilities and allows users to click on pathway names to view participating ligands, receptors, and their expression levels, enhancing the interactive experience.

[0170] This feature marks a significant step forward for the platform, moving from routine expression analysis to system-level biological interpretation, providing crucial support for studying the behavior of complex cellular networks.

[0171] Copy number variation (CNV) is a key genetic characteristic of tumor cells, manifesting as large-scale amplification or deletion of chromosomal segments. While single-cell transcriptome sequencing measures gene expression rather than genomic sequence, studies have shown that synchronous increases or decreases in gene expression within contiguous chromosomal regions often correspond to underlying copy number alterations. Therefore, by calculating the continuity of expression across the genome, CNV patterns at the single-cell level can be indirectly inferred. Compared to bulk DNA sequencing, single-cell CNV inference offers advantages in distinguishing clonal structures and heterogeneity, enabling the identification of malignant cell populations and differentiating tumor-associated immune cells or stromal components, making it an indispensable module in tumor single-cell analysis.

[0172] In this platform, we have integrated the InferCNV tool to perform CNV inference analysis. The core idea of ​​InferCNV is to compare the observation group to be analyzed with a group of stable, normal cells (reference group) to identify regions of abnormal expression. The algorithm can be summarized in four steps: the user selects the reference cell type, such as T cells, B cells, or fibroblasts; InferCNV calculates the fold change in expression of the observed cells relative to the reference cells, and then smooths the matrix along the chromosome direction using a moving window to reduce gene-level noise; the smoothed matrix is ​​standardized and denoised to bring the expression baseline back to zero; and a heatmap is used to display the inferred amplified or deleted regions, aiding in the identification of tumor subclones. This function requires the user to explicitly specify the reference population. This design avoids false positives caused by automatic inference errors, improving the interpretability and controllability of the analysis, and is particularly suitable for mixed cell populations from solid tumor samples.

[0173] The platform's backend is responsible for InferCNV task management and computation scheduling. After selecting an analysis project in the interface, users need to specify the observed cell category and reference cell category used for CNV inference. The frontend packages the content and sends it to the backend. The backend starts the analysis process based on an independent operating environment, stores intermediate and result files in the working directory, and finally returns the visualization output to the frontend for display. At the output display level, we use a CNV heatmap. In this graph, rows represent single cells, and columns represent genes arranged by chromosome; red indicates relatively increased expression (suggesting amplification), and blue indicates decreased expression (suggesting deletion). The platform places reference cells at the top of the heatmap to verify whether their expression pattern is neutral, thereby assessing the rationality of the reference selection. Furthermore, the clustering results of the observed cell portion can be used to identify different tumor subclones. The platform also supports displaying data by cell type or user-specified groups, thereby resolving the differential patterns of CNVs between different cell populations and providing auxiliary evidence for malignant cell identification.

[0174] Pseudotime analysis is a computational method used to infer the continuous trajectory of cell state changes from single-cell transcriptome data. Its goal is to sequence cells at different stages according to biological processes, reconstructing dynamic processes from discrete, static data. Unlike real time, pseudotime expresses relative positions rather than absolute time scales, making it more suitable for analyzing continuous biological processes such as development, differentiation, activation, disease evolution, or treatment response. In these scenarios, tissue sampling often fails to accurately capture the temporal dimension, and pseudotime analysis can compensate for these limitations in experimental design to some extent. Pseudotime analysis can identify intermediate and transitional cell states, reveal the branching structure of cell fate differentiation, infer potential driver genes, and construct terminal state models for different lineages, providing candidate information for subsequent mechanistic studies. In tumor research, pseudotime analysis can be used to observe the evolution of tumor cells along a state spectrum, such as the evolution from inactive immune cells to a depleted state, or the transition from stem tumor cells to invasive and proliferative states, thus serving as an important supplement to tumor heterogeneity analysis.

[0175] This platform integrates Monocle3 to perform trajectory inference tasks. Compared with earlier versions, Monocle3 places greater emphasis on graph structure learning in its algorithm, describing cell state changes by fitting a principal graph in the low-dimensional space of UMAP. Its basic process can be divided into four steps: (1) Dimensionality reduction: Projecting cell expression data into a low-dimensional space through PCA+UMAP to form a conditional space for trajectory learning; (2) Partitioning: Identifying cell populations that are not connected in the low-dimensional space and constructing trajectories for each; (3) Principal graph learning: Fitting the principal graph skeleton that runs through the center of cell distribution using the SimplePPT algorithm; (4) Ranking: The user selects the trajectory starting point based on prior knowledge, and the algorithm calculates the path length from each cell to the starting point as the pseudo-temporal value. The advantage of this framework lies in its scalability and expressive power of trajectory structure, making it suitable for complex multi-branch problems.

[0176] The platform provides a complete runtime interface for Monocle3. Users do not need to run R scripts directly; instead, they can submit tasks by selecting the analysis project, specifying the cell population, and the trajectory start point in the front end. The back end performs distributed task scheduling, calls the R environment to run Monocle3, and stores the results in the task directory. In large-scale data scenarios, the platform reduces the cost of redundant computation by caching dimensionality reduction results and partitioning information, and sets up separate computing nodes for trajectory learning and sorting processes to improve task throughput. Simultaneously, the platform performs standardized preprocessing of the input format, including standardization by expression matrix row (gene) × column (cell) format, selection of a subset of cells by the user, and incorporation of the user's previous annotation information for trajectory labeling, ensuring consistency between the analysis process and the user's analysis context. Users select the project to be analyzed on the platform, specify the cell type or cluster for the trajectory, submit the task, and wait for the computation to complete. The platform provides online viewing and local download of the visualization results and supports linkage with other analysis modules, enabling trajectory inference to be integrated into a more complete biological interpretation chain. At the visualization level, the platform's main outputs include a pseudo-time series color scale map, a trajectory structure map mapped by cell type, and a gene trend map showing changes along the pseudo-time series. The pseudo-time-series color-coded map reflects the trajectory's framework, branching points, and terminal points through the trajectory itself. A color gradient shows the relative progress of each cell along the trajectory, with darker colors typically representing earlier stages and lighter colors representing later stages. Trajectory maps displayed by cell type are used to verify whether the trajectory conforms to biological logic; for example, stem cell populations are located at the trajectory's starting point, while terminal effector cells are located at the differentiation's end. For users wishing to further identify driver genes, the platform also provides gene module analysis along the pseudo-time-series changes. Moran's index is used to screen genes with dynamic expression patterns, and trend curves are plotted to observe the expression trends as the trajectory changes. This is of significant value for screening potential regulatory genes and analyzing pathways.

[0177] Pan-cancer analysis is a comparative analysis method across multiple cancer types, aiming to identify common patterns and individual differences among different cancers at the molecular level. With the deepening of research on tumor phenotypes and microenvironments, researchers have shifted from a localized perspective focusing on a single cancer type to the integration of cross-cancer data to explore common disease mechanisms, sources of heterogeneity, and potential therapeutic targets. Therefore, the platform integrates a pan-cancer analysis module into its advanced functions to support users in interpreting results at a broader scale.

[0178] TCGA data resources support multi-layered pan-cancer research, such as differential gene expression analysis, methylation level comparison, correlation of immune infiltration indicators, and survival analysis. For data preprocessing, we employ a unified processing workflow for TCGA RNA-seq data, including RPKM / TPM-based expression standardization, sample metadata parsing, and the organization of tumor and normal tissue classification labels to ensure data comparability across different cancer types. Simultaneously, the platform integrates expression matrices from different cancer types (such as BRCA, LIHC, LUAD, KIRC, etc.) in TCGA into a unified data structure and caches common public indicators on the backend to improve query efficiency. At the algorithm level, the differential analysis module supports commonly used statistical methods, such as differential tests based on linear models or non-parametric tests, while the survival analysis section provides Kaplan-Meier survival curves and Cox proportional hazards models to adapt to different research needs.

[0179] At the user interaction level, the pan-cancer analysis module provides a relatively complete operation process. After logging in, users can select "pan-cancer analysis" in the analysis interface and enter gene symbols of interest (such as CD8A, GZMB, PDCD1, etc.) or immune cell type-related markers. Subsequently, users can select the analysis dimensions they are interested in, such as differences in expression between tumor and normal tissues, comparison of methylation levels, correlation with immune infiltration, or survival analysis. After setting the parameters, the task can be submitted to the backend for calculation. Upon completion of the task, the platform will generate a set of standardized visualization results, including but not limited to: box plots, heatmaps, forest plots, and correlation matrices. These visualizations have clear biological implications. Expression differential box plots can demonstrate the expression differences of target genes between tumors and normal tissues in different cancer types, helping to determine whether they exhibit a broadly significant upregulation or downregulation pattern. Epigenetic heatmaps can be used to show the association between target gene expression and CpG methylation sites, thus indicating whether it is subject to epigenetic regulation. Immune infiltration correlation heatmaps reflect the degree of association between target genes and immune cell infiltration indicators (such as CD8+ T cells, macrophages, etc.), thus indicating the degree of immune involvement. Survival forest plots show the association between gene expression and prognosis in different cancer types, including hazard ratios (HR) and confidence intervals, facilitating the assessment of its potential clinical value. This invention places particular emphasis on the traceability and reproducibility of the task when designing this module. All analysis parameters and version information are recorded in the task table, allowing users to review analysis conditions and compare results at any time. Furthermore, all chart results support online preview and local download, facilitating use in graphic reports, grant applications, or publication support materials.

[0180] Gene enrichment analysis: Based on the hypergeometric test or Fisher's exact test, GO functional or KEGG pathway enrichment analysis is performed on the differentially expressed gene list. Specifically, the background gene set is defined as all genes detected in the expression matrix, with a total number of N genes. For a specific functional gene set to be tested, the number of genes contained in the background set is M, and the target gene set contains a total of n genes, of which k genes simultaneously belong to the target set. Based on this, a statistical test is constructed: the hypergeometric test is used to calculate the probability P of observing at least k genes simultaneously falling into both sets, using the following formula:

[0181] The smaller the P-value, the more significant the enrichment. The multiple P-values ​​generated by the functional item test are corrected for false discovery rate to generate corrected P-values. Finally, a list containing enriched items, gene counts, enrichment ratios, P-values ​​and corrected P-values ​​is output, and an enrichment bar chart is automatically generated. Cell communication analysis: Based on a ligand-receptor interaction database, this study infers and quantifies signaling communication networks between different cell types by aggregating the expression levels of ligands and receptors in aggregated cell populations. Specifically, for each ligand-receptor pair (L, R) in the database, the potential communication strength between the sending cell population i and the receiving cell population j is calculated, and the average expression level of ligand L in the sending population i is calculated. and the average expression level of receptor R in receiver group j Using geometric mean To comprehensively characterize the co-expression level of ligand-receptor; a Michaelis-Menten-like equation model is used to convert aggregate expression levels into communication probabilities to simulate the saturation effect of signal binding, the expression being:

[0182] in, It is an inferred communication probability or strength. It is the half-maximal effect constant. The Hill coefficient is used; a null distribution is constructed by randomly permuting cell type labels, and the empirical P-value of the observed values ​​is calculated; the communication strength of all ligand-receptor pairs belonging to the same signaling pathway is integrated to obtain the intercellular communication network at the pathway level. The results are visualized in the form of a circular network diagram and heatmap to show the global interaction patterns and key signaling pathways. Copy number variation analysis: By comparing gene expression in the observed cell population with that in the reference cell population, smoothing the expression along the chromosome direction using a moving window, and identifying genomic regions with continuously increasing or decreasing expression, copy number variations are inferred. Specifically, a group of cells with known genomic stability is designated as the reference cell population, and the remaining cells to be analyzed are designated as the observed cell population. For each observed cell c and each gene g, the logarithmic ratio of its expression level relative to the average expression level of the reference cell population is calculated.

[0183] in, It represents the expression level of gene g in cell c. It is its average expression level in the reference group. It is a preset minimal constant; along the physical location of the chromosome, a sliding window of fixed width is used to... The values ​​are smoothed to obtain the smoothed expression offset. The smoothed data is segmented using a thresholding method or a Gaussian mixture model to divide the genomic regions into different copy number states: Amplification: Missing: ;neutral: ,in The threshold was set; the analysis results were finally presented in the form of a heatmap, with rows representing cells, columns arranged according to genomic location, and colors visually displaying the inferred copy number variation regions; Pseudo-temporal analysis: A master path graph is constructed for a selected cell population in a reduced-dimensional space. The path length from each cell to a specified starting point is calculated as the pseudo-temporal value, and the trend of gene expression along the pseudo-temporal sequence is analyzed. Specifically, on a user-selected subset of cells, a nearest neighbor graph is constructed based on the reduced-dimensional space. Nodes in the graph represent cells, and edges connect each cell to its K nearest neighbors. Based on this graph, a simplified skeleton structure, called the master path graph, is learned using a minimum spanning tree or inverse graph embedding algorithm. This graph contains branch points and leaf nodes, representing the main paths and fate decision points of cell state transitions. After the user specifies one or a group of cells as the trajectory starting point, the system calculates the shortest path distance from each cell node to the root node in the graph. This distance is defined as the pseudo-temporal value for that cell. The expression level of each gene is smoothly fitted to the pseudo-temporal value, and the significance of its expression trend is evaluated. The output includes the pseudo-temporal value for each cell, the trajectory graph structure, and a list of trending genes, visualized in the form of a pseudo-temporal projection map and a gene expression trend map. Pan-cancer analysis: Integrating public databases of gene expression across multiple cancer types, this feature allows users to input target genes and perform cross-cancer expression differential, epigenetic association, immune invasion correlation, and survival association analyses. The specific process is as follows: Within each cancer type, the expression difference of the gene between tumor tissue and adjacent normal tissue is calculated, and the p-value is calculated using the Wilcoxon rank-sum test. Logarithmic folding changes are also calculated, and the results are presented as box plots of cross-cancer expression differences. Within each cancer type, the Spearman correlation coefficient between target gene expression and the methylation level of CpG sites in its promoter region is calculated, and the strength of the correlation is displayed as a heatmap. Within each cancer type sample, the Spearman correlation coefficient between target gene expression and the estimated invasion fraction of various immune cells is calculated, and the results are presented as a correlation heatmap. Within each cancer type, patients are divided into high and low expression groups based on the target gene expression level. A Cox proportional hazards regression model is used to calculate the hazard ratio and its confidence interval, and the Log-rank test is used to compare survival differences. The results are summarized and presented as Kaplan-Meier survival curves and forest plots.

[0184] It should be noted that the pan-cancer analysis function of this invention mainly demonstrates the platform's analytical capabilities for large-scale datasets such as TCGA and its scalability for cross-cancer research. The platform also supports further cross-validation of user results using public databases as external evidence.

[0185] Data validation and comparative analysis To enhance the reliability and interpretability of the analysis results, this platform provides a complete data verification and comparative analysis module, supporting cross-comparison between user-owned data and public multi-queue data.

[0186] The backend integrates a large-scale public database, including tens of millions of single-cell transcriptome data resources covering the immune system, tumor tissue, and multiple organ sources. One of the most important resources is TCGA (The Cancer Genome Atlas), which covers more than 30 types of cancer and over 10,000 tumor samples, including multimodal data such as transcriptomics, mutations, methylation, clinical prognosis, and immune infiltration. This large-scale integration allows users to directly align their own data with external background data, enabling multi-layered validation scenarios that are difficult to achieve with traditional analysis tools. These include: the consistency of expression of key genes or cell subpopulations in large tumor cohorts; the universality or specificity of differentially expressed events in a multi-cancer context; associations with immune infiltration, clinical prognosis, or treatment response; and cross-sample, cross-cancer, and cross-technology platform stability validation of biomarkers. These validations do not require users to download, organize, or manually process public databases. Instead, the platform modules enable rapid access, comparison, and return of background evidence, greatly reducing the barrier to entry and improving work efficiency.

[0187] The core foundation of this validation module comes from the TCGA data system integrated into the platform. TCGA covers more than 30 types of tumors and contains tens of thousands of samples. Its data types involve multimodal features such as mRNA expression, mutation, methylation, immune infiltration estimation, and survival clinical information. Users can perform comprehensive validation processes across samples, cancer types, and technologies. For example, if CD8+ T cell marker genes (such as CD8A, GZMB, or CXCL13) are significantly upregulated in a specific cancer at the single-cell level, the system can automatically search whether they show similar trends in multiple cancer types and identify co-occurrence patterns related to immune infiltration or T-cell activation. This "background verification" process usually requires researchers to download TCGA data and process it manually in traditional analyses, which is complex and time-consuming. This platform has built it into a standardized analysis link, significantly reducing the barrier to entry.

[0188] This module not only supports expression-level comparisons but can also be expanded to a wider range of biological validation scenarios. For example, it can perform cross-cancer differential expression comparisons to identify whether the signal is tumor type specific or pan-cancer consistent; validate the immune infiltration background by combining indicators such as ESTIMATE, TIMER, or MCP-counter to assess its association with immune scores; perform clinical prognostic association analysis by validating whether target genes affect overall survival (OS) or progression-free survival (PFS) through survival models; and validate the association between microenvironment composition and the target gene, to determine whether certain TME-related genes show a significant trend only in immune-enriched cancer types.

[0189] This validation mechanism is particularly important in tumor immunology research. For example, immune-depleted genes (such as PDCD1, LAG3, HAVCR2, and TIGIT) identified from single-cell data of liver cancer generally align with cancers exhibiting high immunogenicity in the TCGA pan-cancer study. Furthermore, some genes show a negative correlation with patient prognosis. This evidence significantly enhances the scope and interpretability of research conclusions. For tumor-specific genes or stromalmakers that influence the microenvironment, this module also helps eliminate sample bias or cancer type randomness, leading to higher reliability of the analysis results.

[0190] System optimization and user experience improvement While the platform's advanced functions have been successfully implemented, issues remain in practical applications, such as complex operation procedures and a high barrier to interpreting analysis results. To further simplify operation steps, improve analytical accuracy and stability, and continuously optimize the overall user experience, this invention has undertaken a series of targeted improvements and optimizations in areas such as data processing, module linkage, and interactive interfaces. These improvements have made the platform more functional and easier to use, laying the foundation for its future widespread application.

[0191] To balance accuracy and scalability of analysis results, this platform offers a high degree of parameter openness across all modules. Users can either run standard workflows directly using system-preset recommended parameters or adjust key parameters according to data characteristics and research objectives. The platform allows users to select quality control thresholds, clustering algorithms, and cell annotation methods to improve the model's adaptability to specific data. At the visualization level, the platform also supports various user-defined options. Users can adjust color scale gradients, font sizes, axis ranges, label density, and legend styles to make the results more suitable for paper illustration, presentations, or internal discussions. Thanks to the task table mentioned earlier, SCSEQ can save the parameters configured by the user for each submitted task. This allows users to retrospectively analyze and compare results obtained under different parameter settings. For users unfamiliar with bioinformatics analysis, the system provides default parameters for most analysis workflows to simplify operation, and the platform also includes built-in manuals and tutorials for guidance.

[0192] For modules involving model training (such as automated cell annotation), the platform allows users to fine-tune the model by uploading custom training sets or weights, enabling transfer applications tailored to specific tissues or disease types. This mechanism extends the platform from a fixed tool to a trainable system, better adapting to the needs of different researchers.

[0193] In single-cell analysis, data processing and advanced analysis often involve dozens of asynchronous steps, and different algorithms are highly sensitive to parameter configurations. Without a systematic recording and task scheduling mechanism, users will face significant difficulties in repeating experiments, comparing parameters, archiving papers, or collaborating across projects. Therefore, this platform implements a task management and result traceability module specifically for single-cell analysis scenarios, enabling users to fully trace and reconstruct the entire analysis process.

[0194] In the task management interface, the analysis process is presented in a tree structure. The root node corresponds to the original project data, the second-level nodes represent the classification results of different cell annotation models, and the third-level nodes correspond to specific high-level task types, such as GO enrichment, inter-cell communication analysis, copy number variation analysis, pseudo-temporal analysis, or pan-cancer analysis. This structure reflects the actual dependencies in the single-cell analysis workflow. For example, calculating marker genes depends on annotation models and clustering, while pseudo-temporal analysis depends on marker gene selection, providing users with a clear traceability path. Each task node records its running parameters, submission time, running status, and downloadable results. The task table at the bottom of the interface provides fine-grained information; parameter fields are directly extracted from the database, fully storing the user-input gene list, group selection, model settings, thresholds, or algorithm options. This recording method ensures experimental reproducibility, facilitating subsequent collaborative research or the compilation of supplementary materials for papers. Users do not need to record scripts or re-find parameters to reconstruct the task's running context.

[0195] The platform supports parallel scheduling of multiple tasks. Users can submit multiple analysis tasks simultaneously without waiting for the current task to complete, and can log back in to view the results after disconnecting the browser or shutting down the computer. This asynchronous execution mechanism reduces the cost of interactive waiting and better meets the needs of "queue-style" analysis in real-world research environments.

[0196] This module enables the analysis process to be traceable, reproducible, and manageable, thereby improving the platform's engineering maturity and providing a foundation for subsequent model optimization, automated analysis, and process version control.

[0197] To meet the needs of high-dimensional data visualization, result comparison, and detailed exploration in single-cell analysis, the platform integrates an interactive plotting component system (primarily based on ECharts with a custom rendering module) on the front end, allowing users to browse and interpret large-scale results without relying on local software. Different analysis results employ the most suitable graphical representation method; for example, quality control uses violin plots, clustering results use scatter plots, marker gene results support bubble charts, intercellular communication analysis provides pie charts and heatmaps, and pan-cancer validation supports box plots and forest plots. Users can switch between different display methods according to task type and result characteristics, avoiding information loss caused by a single graph.

[0198] Interactivity is a key feature of this module. For example, in markerdotplot, users can hover to view information such as cell type, gene category, average expression level, and detection rate; in cell-to-cell interactions, users can hover to view the interaction frequency of a specific cell population, or choose to view only the cells of interest. For some charts, the platform allows users to manually adjust the visibility parameters, with results updated in real time, facilitating localized debugging and parameter comparison. For modules requiring background calculations (such as CellChat, InferCNV, Monocle3, etc.), users can directly load the corresponding visualizations after completing the task, without needing to reopen or download files. This mode reduces repetitive operations, improves analysis efficiency, and enhances the overall user experience.

[0199] In the cell type annotation module of the platform, we introduced a retrieval-enhanced large language model mechanism to improve the model's performance in areas such as biological terminology, marker gene interpretation, and tissue origin inference. Simply relying on a general large language model for cell annotation often results in insufficient knowledge coverage, while retrieval enhancement can significantly improve the model's specialized understanding and contextual consistency. The system integrates a curated single-cell annotation database (such as PanglaoDB) in the backend, containing records of tissue, cell type, and their marker gene information. After users upload data and complete clustering and marker calculation, the system retrieves the closest reference records for the cluster to be annotated, sorts them, and provides the results as contextual hints to the model for generating annotation suggestions.

[0200] The platform separates retrieval, ranking, and model inference into independent modules, making them reusable and supporting the replacement of different model backends. Users do not need to understand the underlying retrieval logic; they only need to submit their test data to receive candidate annotation labels and explanations from the model. It is important to emphasize that these results are essentially suggestions, not mandatory labels. The platform retains a manual review path, allowing users to modify, overwrite, or ignore the annotations provided by the model. Furthermore, annotation quality depends on the accuracy of upstream clustering and markers; the model performs better when marker signals are clear, and conversely, ambiguity may occur. Therefore, this module primarily serves an auxiliary role, accelerating the manual annotation process and reducing initial screening costs.

[0201] This invention focuses on the advanced functional expansion of a single-cell transcriptome analysis platform, highlighting the improvements in both the breadth and depth of its functionality. The invention first implements several advanced analysis modules, including gene enrichment, transcription factor analysis, intercellular communication analysis, copy number variation analysis, pseudo-temporal analysis, and pan-cancer analysis, covering multi-scale perspectives from the cellular to the tissue and even cancer type levels. These modules are uniformly integrated into the platform through standardized interfaces, supporting asynchronous execution, parameter configuration, and batch task management, and providing visualization and result export capabilities, significantly lowering the technical barrier to high-dimensional analysis.

[0202] Subsequently, this invention introduces the platform's data verification mechanism and public data comparison capabilities. In particular, by introducing TCGA, it enables rapid matching and verification of user-owned data with pan-cancer backgrounds, which helps improve the explanatory power of the results. Simultaneously, we have further improved functions such as task management, adjustable parameters, interactive visualization, and large-model-assisted annotation, making the analysis process more convenient and scientific.

[0203] Test environment and data With the overall architecture design and functional module implementation of the platform system largely completed, it is necessary to systematically verify its performance, functional completeness, and user experience. This invention constructs real-world analysis task scenarios to test and discuss the platform's basic functions, advanced analytical capabilities, user-friendliness, and interactivity, thereby evaluating its practical application value in single-cell transcriptome data analysis. Simultaneously, this invention will conduct a comparative analysis with existing mainstream tools to further illustrate the platform's differentiated advantages and applicable scope, providing a basis for subsequent promotion, application, and research expansion.

[0204] The development and verification of this invention platform were completed based on existing computing and software environments. On the hardware side, the platform's backend is deployed on server nodes, which possess multi-core CPUs and large-capacity memory, supporting concurrent execution of multiple tasks and large-scale data analysis tasks. During the platform development phase, the system operating environment was configured with commonly used bioinformatics analysis tools and dependencies, including Python and R language environments and related software packages. Tools such as Seurat, CellChat, InferCNV, and Monocle were installed to complete basic workflows and advanced downstream analysis tasks in single-cell sequencing data analysis.

[0205] In terms of software environment, this platform adopts a front-end and back-end separation architecture. The front-end uses the Vue framework to implement the user interface and interaction logic, while the back-end uses the Flask framework to handle task scheduling, parameter management, and analysis module calls. MySQL is used as the database to store user, project, and task information, supporting multi-task record tracing and analysis result tracking. To ensure environment consistency and dependency controllability, it is recommended that users deploy the platform in an Anaconda environment to improve package compatibility and operational stability. The project source code is currently hosted on GitHub.

[0206] During the experimental validation phase, the platform used publicly available PBMC single-cell sequencing data for functional testing. All analysis tasks were completed within the platform interface, eliminating the need for manual command-line commands. Throughout the process, each module successfully generated visualization results, validating the platform's stability and usability in both the computing environment and presentation aspects.

[0207] Basic analysis function verification To verify the platform's analytical capabilities in real-world scenarios, this invention selected the publicly available PBMC single-cell sequencing dataset for a full-process analysis test, including data import, basic analysis, cell type annotation, and downstream functional module invocation. All steps were completed through platform interface configuration and task submission, generating corresponding visualization results.

[0208] In the basic functional validation, we first uploaded PBMC2700 data to the platform and completed preliminary analysis using the default parameters built into SCSEQ. The platform automatically performed data quality control, normalization, feature gene identification, and dimensionality reduction. During the quality control phase, nFeature_RNA, nCount_RNA, and percent.MT metrics were used to evaluate the integrity and reliability of the single-cell data. The visualization results are presented in violin plot format (e.g., ...). Figure 3 This visually displays the distribution of data before and after filtering, helping users determine whether the data meets the requirements for downstream analysis.

[0209] Subsequently, the platform constructed a KNN diagram based on the principal component analysis (PCA) results and completed cell population segmentation at the default clustering resolution. UMAP / t-SNE dimensionality reduction diagram (e.g.) Figure 4 The study revealed nine distinct cell clusters with clear boundaries between them. The overall distribution of these clusters was consistent with the biological characteristics of PBMC samples, demonstrating the stability and visualization capabilities of the platform in cluster analysis.

[0210] During cell type annotation, we used the "Immune_All_Low" reference model built into CellTypist. The platform automatically identified twelve major cell types, including B cells, CD16+ NK cells, classical and non-classical monocytes, dendritic cells, and regulatory T cells. The annotation results are displayed in UMAP plots using color-coded markers (e.g., ...). Figure 5 The data processing platform analyzed the proportion of each cell type within the total cell population, providing a reliable foundation for subsequent differential expression analysis and the use of downstream functional modules. The entire process requires no manual programming; users can complete data processing and visualization through the interface, demonstrating the platform's user-friendliness and ease of use.

[0211] The platform calculated the marker genes for each cell population and displayed the expression levels and proportions of these genes in different cell types using a bubble chart (e.g., ...). Figure 6 The graph simultaneously encodes the expression ratio (dot size) and expression level (color gradient), facilitating rapid identification of cell type-specific genes. For example, CD8A and CD8B are highly expressed in the Tcm / Naivecytotoxic T cell population, CD79A and VPREB3 are significantly enriched in the B cell population, while GZMB is mainly found in the Tem / Trmcytotoxic T cell population. The distribution characteristics of these marker genes are consistent with the classic biological description of immune cells and corroborate the aforementioned annotation results. Furthermore, users can select any gene through the interface to view its expression map, thus supporting more downstream analysis scenarios. Overall, the marker display module not only provides biological evidence for annotation but also enhances the platform's usability in cell function interpretation and result verification.

[0212] Advanced analytics functionality verification After completing basic steps such as cell filtering, dimensionality reduction, clustering, and cell annotation, we will continue to use the advanced analysis modules provided by the platform to conduct in-depth analysis of PBMC samples. This part of the analysis is no longer limited to cell type structure, but further focuses on cell functional status, interactions, potential differentiation trends, and correlations with public tumor databases, thereby validating the platform's practicality in complex scenarios.

[0213] This invention first performs GO enrichment analysis. The operation involves the user selecting the target cell population (B cells in this experiment) on the interface, and the platform automatically extracts functional modules from the differentially expressed genes and visualizes them using bar charts. In the results, we filter relevant biological processes and display the top ten based on gene count values. The bar charts show clearly defined immune function entries, such as "cytoplasmic translation" and "leukocyte-cell-cell adhesion." These entries are highly consistent with actual situations, experimentally validating the reliability of the platform's enrichment tool and demonstrating that the marker strategy can accurately capture the functional characteristics of B cells. Regarding visualization interpretation, the bar charts (such as...) Figure 7 The horizontal axis represents the number of genes in the target pathway, and the vertical axis represents GO entries. Compared to simply outputting an enrichment table, the graph helps users quickly locate immune-related modules and infer their physiological roles.

[0214] This invention then uses the CellChat module to verify intercellular communication capabilities. The platform allows users to specify any cell type as sender or receiver; the system infers the interaction network based on the CellChatDB ligand-receptor library and outputs a circular network diagram (e.g., Figure 8 In the pie chart, the width of the rings reflects the intensity of signal transmission or reception, while the lines represent the amount of interaction between cells. In PBMC samples, we focused on the communication patterns of B cells. The results showed strong interaction signals between B cells and populations such as Tcm / NaivehelperT cells and Classicalmonocytes, consistent with the classic synergistic mechanism of the immune system.

[0215] Copy number inference was performed using the InferCNV module. The user selects the reference group (Tcm / Naivecytotoxic Tcells, DCs, and Megakaryocytes / platelets in this experiment), and the remaining cells serve as the observation group for inference of expression in continuous genomic regions. Results are presented as heatmaps (e.g., Figure 9 The data is presented with the horizontal axis arranged according to chromosome order and the vertical axis representing single cells. Reference cells, which show overall neutral expression, are placed at the top, while the observation group exhibits continuous red patches in certain chromosomal regions, suggesting potential CNV increase events. Since PBMC data should not normally contain significant genomic instability, this result serves as a negative control to confirm the correctness of the module's operation and lays a foundation for subsequent tumor sample analysis.

[0216] The pseudo-time series analysis was performed using the Monocle3 tool. We selected three cell populations that were abundant and immunophysiologically relevant in the data: B cells, Tcm / Naivehelper T cells, and Classicalmonocytes. After the user specifies the trajectory starting point in the platform, embedded trajectory plots and pseudo-time series gradient plots can be generated (e.g., ...). Figure 10 The results showed that cells were distributed in a continuous state in the UMAP space, with the pseudo-temporal color extending from dark to light, suggesting the existence of potential immune activation pathways. Simultaneously, the platform supports plotting gene expression trends along the pseudo-temporal sequence (e.g., ...). Figure 11 We selected genes such as ISG15, RPL22, SH3BGRL3, CD52, and IFI6. The trend graph shows that some IFN-induced genes increase with the pseudo-time sequence, which is consistent with the antiviral response characteristics that PBMCs may exhibit under stimulation.

[0217] To validate whether single-cell hierarchical discovery has cross-cancer significance, we used TCGA public data for external background validation. We selected the CD8A gene for testing, which is commonly used to assess CD8+ T cell infiltration levels. In pan-cancer box plots (such as...) Figure 12 The results show that CD8A expression is higher in tumor tissue than in normal tissue across multiple cancer types, suggesting the presence of CD8+ T cell aggregation in the tumor microenvironment. We then present the heatmap and forest plot outputs. The CpG methylation heatmap suggests that CD8A expression may be influenced by epigenetic regulation in some cancer types, while the macro-immune infiltration heatmap reveals its potential association with the M1 / M2 macrophage score. Furthermore, the forest plot shows that CD8A has a better survival associated signal in several immunosensitive cancer types, possessing extended significance related to immunotherapy.

[0218] The advanced analysis modules described above validate the platform's functional completeness in the context of immune data and demonstrate that users can perform cross-scale analysis, extending from the cellular function level to the tissue and cancer type levels. The platform's visualization module provides a complete results presentation path, making the analysis process and interpretation more operable and interpretable.

[0219] Comparison with existing tools Compared to existing single-cell analysis platforms, the key feature of this platform lies not in the performance of a single algorithm, but in its comprehensive integration of the analysis workflow and its scalable engineering design. Most current online or toolbox-style platforms only cover basic functions such as quality control, dimensionality reduction, clustering, and basic annotation, often leaving tasks such as inter-cell communication, copy number variation, pseudo-temporal analysis, and pan-cancer background validation to users to handle locally, or requiring the use of third-party software. This platform integrates these modules uniformly and provides task management, parameter configuration, interactive visualization, and model training interfaces, enabling users to complete the entire process from input to validation in a single environment.

[0220] On the other hand, this platform introduces a replaceable algorithm mechanism, allowing users to choose custom methods in many stages, rather than using a fixed pipeline. Furthermore, we've incorporated large-scale model assistance at the cell annotation level, integrated the TCGA public database at the validation level, and provided survival analysis, immune scoring, and methylation association analysis at the pan-cancer analysis level, expanding the analytical dimensions from single-cell to tissue and cancer type levels. Compared to current common web-based platforms (such as ezSingleCell, ICARUS, ASAP, and alona), this platform emphasizes three key characteristics: end-to-end, cross-scale, and verifiable. It integrates a wider range of tools, supporting routine analyses in immune, developmental, and disease research, as well as complex scenarios such as tumor immunology, cell communication, and trajectory inference, enhancing data interpretation capabilities and application depth.

[0221] This invention systematically validates the platform's functionality through practical examples. From experimental environment configuration to the analysis workflow of specific samples, both basic and advanced functional modules were tested. Results show that the platform can successfully complete basic analysis steps such as quality control, dimensionality reduction, clustering, and cell annotation, as well as higher-level analysis tasks such as gene enrichment, transcription factors, cell communication, copy number variation, pseudo-temporal analysis, and pan-cancer analysis. Furthermore, visualization and data interpretation examples demonstrate that even users lacking programming experience can obtain complete analysis results. In addition, a horizontal comparison with existing tools shows that the platform has significant advantages in functional integration, analytical depth, and validation dimensions, especially in achieving cross-level validation from single-cell to tissue scale in tumor research scenarios. The results of this invention demonstrate that the platform possesses stable operation, reproducible output, and the ability to expand to multiple scenarios, meeting the main needs of research users for single-cell data analysis.

[0222] With the widespread adoption of single-cell transcriptome sequencing technology, the ability to complete the entire analysis process from data preprocessing to downstream result interpretation in a non-programming environment has become a key factor affecting its further promotion in the biomedical research field. To address this issue, this invention constructs and implements a single-cell analysis platform for research users. The platform integrates mainstream analysis methods and models on the backend, and provides interactive visualization and parameter management mechanisms on the frontend. Furthermore, it achieves a high degree of engineering implementation through task scheduling, model encapsulation, and database management, providing a low-threshold and scalable solution for the computational analysis of single-cell sequencing data.

[0223] Compared with existing online analysis tools, the features of this invention platform are mainly reflected in three aspects: (1) The analysis chain is more complete, covering the process from data import, quality control, dimensionality reduction clustering, cell type labeling, to functional enrichment, cell communication, copy number variation, pseudo-temporal analysis, and pan-cancer background verification; (2) It supports the coexistence of multiple algorithm modules, facilitating subsequent method replacement and upgrades without changing the way the terminal is used; (3) It provides cross-level verification capabilities, enabling single-cell level discoveries to rely on public data for background expansion and reliability verification. Through actual case testing, the platform can stably output analysis results, demonstrating strong practicality and promotion potential.

[0224] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0225] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A cloud platform for single-cell sequencing data analysis, characterized in that, include: View layer, control layer, calculation layer, and data layer; The view layer is used to provide a user interface, receive project configuration parameters, analysis task instructions and single-cell sequencing data files input by the user, and display analysis results and visualizations. The control layer is communicatively connected to the view layer and is used to receive requests from the view layer, verify and encapsulate the project configuration parameters and single-cell sequencing data, distribute the encapsulated tasks to the computing layer, and receive the processing results returned by the computing layer and forward them to the view layer. The computing layer is communicatively connected to the control layer and includes multiple independently encapsulated bioinformatics analysis modules for executing corresponding analysis processes according to task instructions distributed by the control layer. The analysis process includes at least data preprocessing, normalization, dimensionality reduction, clustering, differential expression analysis, and cell annotation. The data layer is communicatively connected to the control layer and the computing layer, and is used to store user accounts, project metadata, task parameters and task status information in a relational database, and to store the raw single-cell sequencing data files uploaded by users, intermediate data files generated by the computing layer and final result files in a file system.

2. The single-cell sequencing data analysis cloud platform according to claim 1, characterized in that, The view layer is built on the Vue.js framework and includes a user management page, a data upload page, a parameter configuration page, and a result display page. The control layer is implemented based on the Flask framework, providing file upload interface, parameter configuration interface, task submission interface and result acquisition interface, and uses an asynchronous task model to process requests from the view layer; In the data layer, the relational database is MySQL, and its table structure is designed based on a three-level association model of user-project-task. Project metadata and task parameters are stored in JSON format.

3. The single-cell sequencing data analysis cloud platform according to claim 1, characterized in that, In the data layer, the file system is divided into a raw file area, an intermediate file area, and a result area. The naming of files follows the combination rule of project ID + task ID + timestamp. The control layer is also used to trigger an asynchronous cleanup task of the corresponding directory in the file system when a user deletes an item or task.

4. The single-cell sequencing data analysis cloud platform according to claim 1, characterized in that, The bioinformatics analysis module in the computational layer includes: Quality control module: used to filter single-cell sequencing data based on set thresholds, including the number of genes detected in each cell, the total number of RNA molecules, and the proportion of mitochondrial genes; High-variability gene screening module: used to identify high-variability genes from filtered data. It adopts a strategy based on gene normalized variance to calculate the relationship between the mean and variance of gene expression and retain the gene set with the highest variance ranking. Dimensionality reduction module: used to perform principal component analysis on the highly variable gene expression matrix and further perform nonlinear dimensionality reduction, the nonlinear dimensionality reduction method includes t-distributed random neighborhood embedding and unified manifold approximation and projection; Clustering module: Used to construct a cell adjacency graph in the dimensionality-reduced feature space based on the K-nearest neighbor algorithm, and to perform community partitioning on the graph using a modular optimization algorithm to obtain cell cluster labels.

5. The single-cell sequencing data analysis cloud platform according to claim 4, characterized in that, The bioinformatics analysis module in the computational layer also includes a cell annotation module, which integrates at least one of the following methods: The annotation method based on the reference expression matrix matches the expression profile of the cells to be annotated with the reference expression matrix with cell type labels, and assigns a label to each cell. The annotation method based on machine learning models uses a pre-trained or user-uploaded training classification model to predict the cell expression matrix to obtain cell type labels. An annotation method based on a large language model inputs a list of marker genes obtained from differential expression analysis into the language model, and generates candidate cell type labels through model inference.

6. The single-cell sequencing data analysis cloud platform according to claim 1, characterized in that, The computing layer also includes a high-level analysis module, which includes: Gene enrichment analysis module: Used for GO functional or KEGG pathway enrichment analysis of differentially expressed gene lists based on hypergeometric test or Fisher's exact test; the specific process is as follows: Define the background gene set as all genes detected in the expression matrix, with a total number of N genes; for a certain functional gene set to be tested, the number of genes contained in this set in the background set is M, and the total number of genes contained in the target gene set is n, of which k genes simultaneously belong to the functional gene set; based on this, a statistical test is constructed: the hypergeometric test is used to calculate the probability P of observing at least k genes simultaneously falling into both sets, the formula is: The smaller the P-value, the more significant the enrichment. The multiple P-values ​​generated by the functional item test are corrected for false discovery rate to generate corrected P-values. Finally, a list containing enriched items, gene counts, enrichment ratios, P-values ​​and corrected P-values ​​is output, and an enrichment bar chart is automatically generated. Cell communication analysis module: Based on a ligand-receptor interaction database, this module infers and quantifies signaling communication networks between different cell types by aggregating the expression levels of ligands and receptors in aggregated cell populations. Specifically, for each ligand-receptor pair (L, R) in the database, the potential communication strength between the sending cell population i and the receiving cell population j is calculated, and the average expression level of ligand L in the sending population i is calculated. and the average expression level of receptor R in receiver group j Using geometric mean To comprehensively characterize the co-expression level of ligand-receptor; a Michaelis-Menten-like equation model is used to convert aggregate expression levels into communication probabilities to simulate the saturation effect of signal binding, the expression being: in, It is an inferred communication probability or strength. It is the half-maximal effect constant. The Hill coefficient is used; a null distribution is constructed by randomly permuting cell type labels, and the empirical P-value of the observed values ​​is calculated; the communication strength of all ligand-receptor pairs belonging to the same signaling pathway is integrated to obtain the intercellular communication network at the pathway level. The results are visualized in the form of a circular network diagram and heatmap to show the global interaction patterns and key signaling pathways. Copy number variation analysis module: By comparing gene expression in the observed cell population with that in the reference cell population, smoothing the expression along the chromosome direction using a moving window, identifying genomic regions with continuously increasing or decreasing expression, and inferring copy number variations; the specific process is as follows: a set of cells with known genomic stability is designated as the reference cell population, and the remaining cells to be analyzed are designated as the observed cell population. For each observed cell c and each gene g, the logarithmic ratio of its expression level relative to the average expression level of the reference cell population is calculated. in, It represents the expression level of gene g in cell c. It is its average expression level in the reference group. It is a preset minimal constant; along the physical location of the chromosome, a sliding window of fixed width is used to... The values ​​are smoothed to obtain the smoothed expression offset. The smoothed data is segmented using a thresholding method or a Gaussian mixture model to divide the genomic regions into different copy number states: Amplification: Missing: ;neutral: ,in The threshold was set; the analysis results were finally presented in the form of a heatmap, with rows representing cells, columns arranged according to genomic location, and colors visually displaying the inferred copy number variation regions; The pseudo-temporal analysis module constructs a master path graph for a selected cell population in a reduced-dimensional space, calculates the path length from each cell to a specified starting point as the pseudo-temporal value, and analyzes the trend of gene expression along the pseudo-temporal sequence. Specifically, on a user-selected subset of cells, a nearest neighbor graph is constructed based on the reduced-dimensional space. Nodes in the graph represent cells, and edges connect each cell to its K nearest neighbors. Based on this graph, a simplified skeleton structure, called the master path graph, is learned using a minimum spanning tree or inverse graph embedding algorithm. This graph contains branch points and leaf nodes, representing the main paths and fate decision points of cell state transitions. After the user specifies one or a group of cells as the trajectory starting point, the system calculates the shortest path distance from each cell node to the root node in the graph; this distance is defined as the pseudo-temporal value for that cell. The expression level of each gene is smoothly fitted to the pseudo-temporal value, and the significance of its expression trend is evaluated. The output includes the pseudo-temporal value for each cell, the trajectory graph structure, and a list of trending genes, visualized in the form of a pseudo-temporal projection map and a gene expression trend map. The pan-cancer analysis module integrates a public database of gene expression across multiple cancer types, allowing users to input target genes and perform cross-cancer expression differential, epigenetic association, immune invasion correlation, and survival association analyses. The specific process is as follows: Within each cancer type, the expression difference of the gene between tumor tissue and adjacent normal tissue is calculated, the p-value is calculated using the Wilcoxon rank-sum test, and the logarithmic folding change is calculated. The results are presented as a cross-cancer expression differential box plot. Within each cancer type, the Spearman correlation coefficient between target gene expression and the methylation level of CpG sites in its promoter region is calculated, and the strength of the correlation is displayed as a heatmap. Within each cancer type sample, the Spearman correlation coefficient between target gene expression and the estimated invasion fraction of various immune cells is calculated, and the results are presented as a correlation heatmap. Within each cancer type, patients are divided into high and low expression groups based on the target gene expression level. The hazard ratio and its confidence interval are calculated using a Cox proportional hazards regression model, and survival differences are compared using a Log-rank test. The results are summarized and displayed as Kaplan-Meier survival curves and forest plots.

7. The single-cell sequencing data analysis cloud platform according to claim 6, characterized in that, In the dimensionality reduction module, principal component analysis maps the original data to a set of linearly independent principal components through orthogonal transformation. The direction of the first principal component is the direction with the largest variance of the original data. The t-SNE method is optimized by calculating a Gaussian kernel-based similarity distribution P in high-dimensional space, establishing a t-distribution-based similarity distribution Q in low-dimensional space, and minimizing the Kullback-Leibler divergence between the two. The KL divergence calculation formula is as follows: in, This represents the similarity between cell i and cell j in high-dimensional space. This represents the similarity between corresponding values ​​in a low-dimensional space. The differential expression analysis was performed using a model based on the negative binomial distribution.

8. The single-cell sequencing data analysis cloud platform according to claim 4, characterized in that, The computational layer also includes a batch effect integration module for correcting multi-sample single-cell sequencing data. The module integrates at least one of the following methods: a correction method that pairs the nearest neighbor cells between different batches, or a method that iteratively optimizes and projects data from different batches into a shared latent space and aligns them.

9. The single-cell sequencing data analysis cloud platform according to claim 1, characterized in that, The control layer is also used to implement task management and result traceability functions. The analysis process is recorded in a tree structure, with the root node corresponding to project data, child nodes corresponding to different cell annotation model results, and grandchild nodes corresponding to specific high-level analysis tasks. Each task node records its running parameters, submission time, running status, and result storage path. The platform allows users to submit multiple analysis tasks simultaneously and schedule them asynchronously.

10. The single-cell sequencing data analysis cloud platform according to claim 1, characterized in that, The result display page of the view layer integrates interactive drawing components, supporting online rendering and interactive operation of quality control violin plots, dimensionality-reduced scatter plots, clustering result plots, marker gene bubble plots, gene enrichment bar plots, transcription factor analysis result plots, cell communication network plots, copy number variation heatmaps, pseudo-time series trajectory plots, and pan-cancer analysis box plots.

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