33 results about "Differential expression analysis" patented technology

The application belongs to the technical field of biological detection, and discloses a screening method of respiratory tract infection related biomarkers based on macro-transcriptomics. The application performs macro-transcriptome sequencing on respiratory tract infection samples with different clinical phenotypes, performs data quality control, alignment, transcript quantification, retains pathogen and host information, and then identifies genes stably expressed or significantly changed in different groups by combining differential expression analysis and co-expression analysis, obtains potential biomarkers, and obtains the biomarkers by taking the intersection genes of three machine learning algorithms of LASSO algorithm, random forest model and SVM model. The application provides a screening method of biomarkers for rapid and accurate identification of respiratory tract infection.

The invention discloses a screening method of a biomarker for auxiliary diagnosis of small cell lung cancer, and relates to the technical field of biomedicine. The method comprises: acquiring multiple groups of samples; the plurality of groups of samples comprise exosome RNA transcriptome sequencing data of a plurality of SCLC patients and a plurality of healthy controls; each piece of exosome RNA transcriptome sequencing data comprises a plurality of RNA characteristics; carrying out technical quality filtering on RNA characteristics in all exosome RNA transcriptome sequencing data, carrying out differential expression analysis on the filtered characteristics, and determining candidate RNA sets of the SCLC patient and the healthy contrast; performing feature selection on the candidate RNA set through three complementary feature selection modes, and screening an optimal exosome RNA marker combination from different quantities of RNA feature combinations through 20 times of iteration and 10-fold nested cross validation; and the optimal exosome RNA marker combination comprises LINC00989, CXCL5, MAP3K7CL and TUBB1 (Tumor Umbrella Blanket B1). The optimal exosome RNA marker combination screened by the method is beneficial to diagnosis of small cell lung cancer.

The invention discloses a heart failure key gene identification method and system based on a spline regression model and overall entropy change. The method comprises the following steps of: 1, preprocessing data; 2, analyzing time sequence differential expression; 3, constructing a dynamic co-expression network; 4, network overall entropy change analysis; and 5, screening the heart failure key gene. Compared with an existing analysis scheme in which time sequence differential expression analysis and network topology analysis are mutually independent, a coherent technical system from'capturing gene expression time sequence trend 'to'quantifying gene network overall structure evolution' and then'screening key genes' is constructed. The thought jumps out of the limitation of a single method dimension, and more fitting and more systematic analysis of the continuous nonlinear evolution process of the heart failure are realized. Meanwhile, the spline regression model is matched with dynamic network overall entropy change analysis, so that the synergistic effect of effectively screening and comprehensively analyzing differential genes is achieved.

The invention discloses a one-stop tumor RNA-seq analysis system which comprises an original data quality control module, a sequence comparison and quantification module, a fusion gene analysis module, a variable shear analysis module, a differential expression analysis module, a function enrichment analysis module, an immune pathway module, a data management and visualization module and a containerization safety deployment module. The system receives an FASTQ format of original RNA-seq data, carries out quality evaluation and filtering, generates high-quality clean reads, generates gene expression quantitative data through sequence alignment, carries out differential expression, function enrichment and other analysis on the gene expression quantitative data, and carries out data management, visualization and safe deployment on an analysis result. The analysis system is high in data analysis efficiency and credible in analysis result, so that scientific researchers and biomedical workers can perform tumor RNA-seq data analysis more conveniently.

The invention discloses a breast cancer heterogeneity analysis system based on unicellular omics and Mendel randomization, and relates to the technical field of biological information. Comprising a data acquisition module, a single cell data processing module, a differential expression analysis module, a Mendel randomization analysis module, a survival analysis module, a function enrichment module, a drug prediction module and an output module. The system is used for integrating multiple omics data and identifying causal driving genes in breast cancer malignant epithelial cell subgroups. According to the system, unicellular omics and Mendel randomization are combined, specific causal inference of breast cancer heterogeneity cell types is achieved, key genes are identified, part of the genes are positively or negatively correlated with risks, and clinical significance is verified through survival analysis; the system provides new biomarkers and targets for accurate treatment of breast cancer, and has the advantages of high precision, strong repeatability and large clinical transformation potential.

The application discloses a paclitaxel treatment response prediction method and system for triple-negative breast cancer patients, and the method comprises the following steps: obtaining single-cell transcriptome data, bulk transcriptome data and paclitaxel sensitivity data of triple-negative breast cancer patients before and after treatment and preprocessing; constructing a regression model of immune microenvironment and tumor response, calculating the response score of immune cell subgroups, screening key immune cell subgroups related to paclitaxel treatment response, performing differential expression analysis on the pre-treatment sample, constructing a paclitaxel IC50 prediction model, and dividing the sample into a high-sensitivity group and a low-sensitivity group; constructing a treatment response prediction model and outputting a prediction result. The technical scheme integrates the dynamic changes of the immune microenvironment, the transcriptome characteristics and the drug sensitivity data, realizes accurate prediction of the paclitaxel treatment response of the triple-negative breast cancer patients before treatment, and provides a scientific basis for formulating an individualized treatment plan.

The embodiment of the invention discloses a gene expression analysis method and device, electronic equipment and a readable storage medium, and belongs to the technical field of bioinformatics and the field of data processing. The method comprises the following steps: based on a polymerization expression value of a gene in a target cell type of a to-be-detected sample and a benchmark expression value of a gene in a target cell type of a reference sample, determining a first difference of the target gene in the target cell type in the to-be-detected sample relative to the target gene in the reference sample; determining a second difference of the target genes in the target cell type according to the first difference of the target genes in the target cell type and the first difference of the target genes in different cell types of the to-be-detected sample; and performing gene differential expression analysis based on the second difference of the target gene. Based on the embodiment of the invention, the influence of a batch effect on gene expression analysis can be effectively reduced.

The invention discloses application of porcine membrane protein EPG5 in identifying and distinguishing porcine Y sperms. According to the new application of the porcine membrane protein EPG5 provided by the invention, the porcine X and Y sperm membrane proteins are subjected to proteomics analysis through a DIA proteomics technology, the porcine membrane protein EPG5 differentially expressed in the Y sperm membrane protein is screened out, the expression of the protein on the Y sperm is obviously higher than that of the X sperm, and the protein can be used as a molecular marker to identify and distinguish the porcine X and Y sperms. Research shows that the X and Y sperms of the pig can be accurately identified and distinguished through differential expression analysis of the porcine membrane protein EPG5, and the result is consistent with the sorting result of a flow cytometry. The invention provides new application of the porcine membrane protein EPG5 in identifying and distinguishing the porcine Y sperms, protein resources are expanded, more and better methods and selections are provided for identifying and distinguishing the porcine Y sperms, sperm sex sorting is performed through differential expression of the porcine Y sperm membrane protein EPG5, and the method has the advantages of low cost, small damage, high efficiency, simplicity and convenience and good application prospect.

This invention provides a method and system for processing hepatocellular carcinoma data. The method utilizes the CIBERSORT tool combined with weighted gene co-expression network analysis to screen a gene set positively correlated with M2 macrophage infiltration. Through differential expression analysis and prognostic correlation analysis, target genes with prognostic value are identified from the gene set. A dataset containing the target genes is acquired, and a comprehensive machine learning algorithm is used to generate several corresponding prognostic prediction models for each dataset. The C-index of the dataset in the corresponding prognostic prediction model is calculated, and target prognostic prediction models are selected based on the C-index and the complexity of the prognostic prediction model. Compared to the traditional method of selecting an algorithm for modeling at the beginning of the study, the final selected target prognostic prediction model reflects more objective and realistic prediction results. Furthermore, the prediction accuracy of this target prognostic prediction model is improved.

This invention discloses a method for verifying TBCB glioblastoma and cell proliferation, belonging to the fields of bioinformatics and tumor molecular biology. S1: Constructing a TBCB knockdown model and verifying its effectiveness; S2: Obtaining transcriptome data from the knockdown group and control group using RNA-seq; S3: Obtaining a set of DEGs through differential expression analysis; S4: Performing disease enrichment analysis based on DisGeNET to obtain significantly enriched disease entries and screening for GBM / glioma-related entries; S5: Verifying the expression differences and clinical relevance of TBCB and GBM based on public databases (one or more of GEO / TCGA / CGGA); S6: Conducting an EdU incorporation experiment in U87 cells to quantitatively verify the effect of TBCB knockdown on cell proliferation. The verification method of this invention is more directly disease-targeting: by using DisGeNET disease enrichment, the DEGs set is directly mapped to specific disease entries such as "glioma / glioma," avoiding a focus solely on pathway levels.

The application discloses a screening method of biomarkers for assisting in diagnosing small cell lung cancer, and relates to the technical field of biomedicine. The method acquires multiple groups of samples; the multiple groups of samples include exosome RNA transcriptome sequencing data of multiple SCLC patients and multiple healthy controls; each exosome RNA transcriptome sequencing data includes multiple RNA features; technical quality filtering is performed on the RNA features in all exosome RNA transcriptome sequencing data, and differential expression analysis is performed on the filtered features to determine a candidate RNA set of the SCLC patients and the healthy controls; feature selection is performed on the candidate RNA set through three complementary feature selection methods, and through 20 iterations and 10-fold nested cross-validation, optimal exosome RNA marker combinations are screened from different combinations of RNA features; the optimal exosome RNA marker combinations include LINC00989, CXCL5, MAP3K7CL and TUBB1. The optimal exosome RNA marker combinations screened by the method are beneficial to the diagnosis of small cell lung cancer.

This invention discloses a prognostic model and construction method for cervical squamous cell carcinoma, belonging to the fields of bioinformatics and oncology. The construction method is based on a set of genes related to programmed cell death, integrating multi-omics data of cervical squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Cancer Genome Characterization Project (CGCI). After standardized preprocessing, differential expression analysis, bootstrap resampling combined with univariate Cox regression for initial screening, consensus screening using multiple machine learning algorithms, and finally, multivariate Cox proportional hazards regression analysis to identify three core independent prognostic genes and construct a risk scoring model. This model has undergone multi-dimensional validation and optimization, demonstrating robust predictive performance. The method of this invention is standardized and highly reproducible, and the constructed model has high accuracy and strong generalization ability, providing a reliable tool for individualized prognostic assessment and clinical decision-making for cervical squamous cell carcinoma patients.

The embodiment of the invention provides a cell subset division optimization method and device based on a single cell clustering result. The method is applied to the technical field of medical data analysis, and comprises the following steps: performing clustering analysis on single-cell RNA sequencing data according to a preset initial clustering resolution to obtain a plurality of cell subgroups after preliminary clustering; performing differential expression analysis on each cell subset obtained by preliminary clustering to obtain a differential expression gene quantity corresponding to each cell subset; and under the condition that the cell subgroups with the differential expression gene quantity lower than a preset threshold value exist in all the cell subgroups obtained by the preliminary clustering, performing automatic iteration merging on the cell subgroups obtained by the preliminary clustering until the differential expression gene quantity of all the merged cell subgroups is not lower than the preset threshold value. And outputting an optimized cell subset division result. According to the method, the accuracy of cell subset division and the efficiency of single cell data analysis are improved, and the biological rationality and interpretability of an analysis result are improved.

The application discloses a bioinformatics analysis method for identifying a brain hemorrhage prognosis-related gene target point, and the method comprises the following steps: obtaining brain hemorrhage transcriptome expression data, performing quality filtering and batch correction to generate a standardized expression matrix, performing differential expression analysis on the basis of the standardized expression matrix, screening prognosis-associated differential genes in combination with survival regression, constructing a weighted co-expression network to identify a prognosis key gene cluster, screening and verifying a core hub gene through hub degree, taking the intersection of the prognosis-associated differential genes and the core hub gene, filtering and interaction network clustering a core target gene through a pathological pathway, performing immune infiltration correlation analysis and survival analysis on the basis of the core target gene, and outputting the brain hemorrhage prognosis-related gene target point according to an immune correlation marker and a survival correlation index. The application integrates a differential analysis and a network analysis double strategy, confirms mechanism correlation through pathway verification and immune correlation analysis, and the screening result has statistical reliability and biological interpretability.

The invention relates to a bioinformatics analysis method for identifying cerebral hemorrhage prognosis related gene targets. The method comprises the following steps: acquiring cerebral hemorrhage transcriptome expression data and implementing quality filtering and batch correction to generate a standardized expression matrix; performing differential expression analysis according to the standardized expression matrix, and screening prognosis associated differential genes in combination with survival regression; constructing a weighted co-expression network to identify a prognosis key gene cluster, and obtaining a core hub gene through hub degree screening and cross-dataset verification; after intersection of the prognosis associated differential genes and the core hub genes is obtained, core target genes are recognized through pathological pathway filtering and interaction network clustering; and carrying out immune infiltration correlation analysis and survival analysis based on the core target gene, and outputting cerebral hemorrhage prognosis related gene targets according to immune correlation markers and survival correlation indexes. According to the method, dual strategies of difference analysis and network analysis are integrated, mechanism relevance is confirmed through pathway verification and immune relevance analysis, and a screening result has statistical reliability and biological interpretability.

The application discloses a PFAS type identification method and device based on a transcriptome feature gene set, and comprises the following steps: adopting linear model-based and batch correction-based differential expression analysis on pretreated transcriptome data corresponding to each PFAS to obtain a corresponding significant differential gene set; meanwhile, different machine learning algorithms are used for parallel feature mining to obtain feature importance rankings of corresponding genes; the first N genes in the feature importance rankings obtained by each machine learning algorithm are intersected with the significant differential gene set to obtain a screened feature gene set and a key feature gene set is filtered; transcriptome data of a sample to be classified is acquired, and a missing value indicator feature and a distance feature are calculated in combination with the key feature gene set corresponding to all PFAS, so that multi-dimensional features are constructed and input into a trained PFAS multi-classification prediction model to obtain multi-classification prediction probabilities. The application can conveniently and effectively improve the accuracy of PFAS type identification.

The invention relates to the technical field of biomedicine, in particular to a sepsis marker screening method based on a peripheral blood mononuclear cell gene co-expression network, and the method comprises the following steps: obtaining a sample; carrying out batch RNA sequencing, differential expression analysis, weighted gene co-expression network analysis, cross analysis and protein interaction network analysis on the sample, and screening hub genes; performing function enrichment analysis and immune cell infiltration analysis on the hub gene, and screening out a core gene; and carrying out expression verification and clinical correlation analysis on the hub gene. Starting from the overall perspective of a gene network, the screened Hub gene has higher biological significance and reliability, through cross screening of WGCNA and differential expression analysis, the range of candidate genes is greatly narrowed, the screening efficiency and accuracy are improved, bioinformatics analysis, scRNA-seq cell localization and protein level experimental verification are integrated, and the screening method has the advantages that the screening efficiency is greatly improved, and the screening cost is reduced. A complete evidence chain is formed, and the credibility of the marker is ensured.

The invention discloses a single-cell multi-omics dimension reduction method based on a Gaussian process hidden variable model, and aims to solve the problems that single-omics and multi-omics data analysis requirements are difficult to consider and high sparsity and technical noise of single-cell data cannot be effectively handled in the prior art. According to the method, a shared potential space probability generation framework is constructed, variational inference and a sparse Gaussian regression strategy are combined, data of different modalities are non-linearly mapped to a unified low-dimensional space, and meanwhile, an adjustable modal weight is introduced, so that the model can perform efficient dimensionality reduction on single-cell transcriptome and multi-omics data. When the method is used for carrying out dimension reduction on single cell sequencing data, low-dimensional representation learned by the model can be used for completing downstream tasks such as cell clustering, visualization and differential expression analysis, cell heterogeneity and potential biological regulation signals are accurately revealed, and the method has a good application prospect.

The embodiment of the application discloses a gene expression analysis method and device, electronic equipment and a readable storage medium, and belongs to the technical field of bioinformatics and the field of data processing. The method comprises the following steps: determining a first difference of a target gene in a target cell type based on an aggregate expression value of the target gene in the target cell type of a to-be-tested sample and a reference expression value of the target gene in the target cell type of a reference sample; determining a second difference of the target gene in the target cell type based on the first difference of the target gene in the target cell type and the first difference of the target gene in different cell types of the to-be-tested sample; and performing gene differential expression analysis based on the second difference of the target gene. According to the embodiment of the application, the influence of batch effects on gene expression analysis can be effectively reduced.

The invention relates to a method for determining path data influencing the progress of malignant blood particle deficiency type sepsis. The method comprises the following steps: filtering initial gene sequence data to obtain clean gene sequence data; performing gene quantitative analysis on the clean gene sequence data to obtain gene expression quantity data; performing gene differential expression analysis on the gene expression quantity data to obtain differential expression gene data; and performing function enrichment analysis on the differential expression gene data to obtain significant enrichment pathway data. By adopting the method, the path data can be determined to have novelty and specificity.

This invention discloses an analytical method integrating small RNA and DNA methylmics to study crop continuous cropping adaptation, comprising the following steps: collecting wheat samples under W1 and WM conditions; identifying 21-24nt-siRNA clusters under W1 and WM conditions; performing differential expression analysis on 24-nt siRNA clusters; for differentially expressed 24-nt siRNAs, screening for clusters overlapping with differentially methylated regions (DMRs), retaining only pairs with consistent regulatory patterns, defining them as RdDM action sites, and then predicting siRNA target genes. This analytical method can systematically identify differentially expressed siRNAs under continuous cropping conditions; combine whole-genome methylation data (WGBS) to analyze the association between siRNAs and differentially methylated regions (DMRs); identify key RdDM target genes and their functional pathways; and construct a multi-level regulatory network of siRNA–DNA methylation–gene expression, providing a theoretical basis and application foundation for crop continuous cropping adaptation and molecular improvement.

The invention discloses a human rhinovirus susceptibility evaluation method and device, electronic equipment and a storage medium, which are used for solving the technical problem that the currently adopted human rhinovirus detection method cannot evaluate the human rhinovirus susceptibility of an individual in advance. The method comprises the following steps: acquiring a baseline blood transcriptome of a subject queue and a real susceptibility condition of a human rhinovirus; performing characteristic gene screening on the baseline blood transcriptome based on differential expression analysis and multi-dimensional joint interaction analysis to obtain a characteristic genome; extracting expression quantity data of a feature genome from the baseline blood transcriptome as a feature data set, and training a double-layer susceptibility evaluation model based on the feature data set and a real susceptibility condition; acquiring a to-be-detected baseline blood transcription sample, and extracting expression quantity data of the characteristic genome from the to-be-detected baseline blood transcription sample as a to-be-detected sample; and inputting a to-be-detected sample into the double-layer susceptibility evaluation model for human rhinovirus susceptibility prediction to obtain a susceptibility evaluation result.

The invention discloses a method for identifying a COPD target RUNX2 by combining a single cell transcriptome with Mendel randomization. The method comprises two steps of single cell data analysis and Mendel randomization analysis verification. Wherein the single cell data analysis is further divided into data acquisition and preprocessing, including data quality control, integration, normalization, dimension reduction and clustering; cell type annotation; carrying out RUNX2 differential expression analysis; and performing differential cell type function analysis. Mendel randomization analysis verification is further divided into data input; reading and formatting the data; carrying out MR statistical analysis; and result visualization and interpretation are carried out. According to the invention, a single cell transcriptomics and Mendel randomization analysis combined method is created for the first time, a cell specificity abnormal up-regulation mechanism of the RUNX2 is confirmed through multiple models, inspection correction and two-dimensional verification, causal association of the RUNX2 and COPD attack is confirmed, a new way is provided for early diagnosis, risk prediction and targeted intervention of the disease, and the method has a wide application prospect. The method has scientific rationality and clinical application potential.

The invention relates to a circRNA hepatocellular carcinoma (HCC) classification method and device based on a three-dimensional genome and a storage medium, and the method comprises the following steps: obtaining circRNA expression data to be classified, and screening the circRNA expression data based on a predetermined feature group to obtain circRNA features; obtaining a plurality of candidate classification results by using a plurality of machine learning models based on the circRNA features; fusing the candidate classification results to obtain a final classification result; wherein the determination of the predetermined feature group comprises the following steps: extracting all circRNAs from a public database, and carrying out differential expression analysis and screening to obtain a first circRNA group; carrying out gene ontology pathway enrichment analysis on the first circRNA group, and screening to obtain a second circRNA group; and in combination with an IMR90 three-dimensional genome model, carrying out spatial clustering on the second circRNA group to obtain a feature group. Compared with the prior art, the method has the advantages that the spatial diversity of feature sources is improved through three-dimensional space clustering, and then the accuracy of prediction classification is improved.

The invention belongs to the technical field of depression risk identification methods, and particularly relates to a depression risk identification method based on multi-model fusion and gene feature analysis, depression-related transcriptome expression data is obtained from a public database, the transcriptome expression data comprises RNA expression matrixes of depression patients and normal controls, and the RNA expression matrixes are used for identifying the depression risk. The RNA expression matrix at least covers a sample number, a gene number, a signal intensity value and a clinical grouping tag; 2, performing validity verification on input transcriptome expression data, identifying missing values, null columns and abnormal expression quantity problems, prompting correction, outputting an expression matrix and a tag vector before standardization, performing Z-score standardization, missing value processing and ComBat batch effect correction on the expression matrix output in the step 1 in sequence, and performing Z-score standardization on the expression matrix output in the step 2; and sequentially performing differential expression analysis, statistical test screening and machine learning driven screening on the expression matrix corrected in the step 2, and obtaining an optimal feature gene subset Gfinal through intersection extraction and recursive feature elimination.

The invention provides a gene transcription molecular marker for detecting the aging degree of green vegetable seeds and application of the gene transcription molecular marker. According to the method, a green vegetable aging related gene is taken as a molecular marker, real-time fluorescent quantitative polymerase chain reaction detection is carried out on the expression level of the gene through a specific primer pair, standardized correction is carried out in combination with a reference gene ACT7, and evaluation of the aging degree of green vegetable seeds is realized. The invention further screens and verifies the molecular marker through transcriptome sequencing and differential expression analysis, and establishes an aging discrimination threshold based on absolute quantitative analysis: taking BnaCng12660D as an example, when 1000 ng total RNA is taken as a reverse transcription template, if the mRNA copy number of the target gene is greater than 1.62 * 10 < 12 > copies / [mu] L cDNA reaction system, it is determined that the seed enters an aging initial early warning stage. The invention provides a detection kit and application thereof in seed vigor evaluation. The method can realize rapid, objective and quantitative detection in the early aging stage, and is suitable for quality monitoring and production application of green vegetable seeds.

The invention discloses a training method of a prostate cancer prediction model, and relates to the technical field of prostate cancer prediction. Comprising the following steps: collecting and preprocessing data; feature selection and extraction; carrying out differential expression analysis by using a bioinformatics tool, and screening out genes, including DLL3, with significant differential expression in BPH, PCa and NEPC; a recursive feature elimination or LASSO regression method is used to screen out the feature with the most predictive power; deep learning feature extraction: extracting advanced features from the tissue image data by using a convolutional neural network or an automatic encoder; and training and verifying the model. According to the method, RNA sequencing, proteomics and histopathological image three-mode data are integrated, through differential expression analysis and an RFE / LASSO dual feature compression technology, combined modeling of key genes and image advanced features is achieved, and the subtype distinguishing capacity of the model is remarkably improved.

The invention discloses an ischemia-reperfusion acute kidney injury early risk prediction model based on ferroptosis related genes and a construction method and application thereof. The method comprises the following steps: screening five core genes: SLC39A14, TFRC, HMOX1, TXNRD1 and GCLM by using differential expression analysis in combination with a random forest algorithm and a support vector machine recursive feature elimination method; constructing a column graph model through multi-factor Logistic regression, mapping the expression levels of the five core genes into prediction scores, and generating an AKI occurrence risk score; according to the invention, the column diagram model is combined with ferroptosis and immune characteristics, and is more in line with the pathogenesis of AKI; the risk of a patient can be directly quantified, and clinical application is facilitated; through internal verification, the model has relatively high accuracy and clinical practical value.

This invention provides a CYP450 enzyme protein, its encoding gene, and its applications that can catalyze the formation of new andrographolide aglycones, belonging to the field of biology. This invention utilizes bioinformatics analysis and differential expression analysis of transcriptome data from different tissue sites to identify the gene ApCYP71BE50, which catalyzes the formation of new andrographolide aglycones from (4R,5S,9R,10S)-labda-8(17),13-dien-15,19-diol. It also verifies that the CYP450 enzyme protein encoded by the ApCYP71BE50 gene can continuously oxidize the C16 position of (4R,5S,9R,10S)-labda-8(17),13-dien-15,19-diol to form new andrographolide aglycones. This invention provides an important gene element for the biosynthesis of andrographolide and also provides a key gene locus for the molecular design breeding of andrographis paniculata.