Single-Cell Somatic Mutation Extraction via Multi-Method Integration
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Solution Overview
Problem
Current methods face challenges in detecting somatic mutations with high precision from single-cell RNA sequencing data due to sparsity of detectable mutations and introduction of spurious and noise signals, making it difficult to achieve genotype-to-phenotype research at the single-cell level.
Innovation Solution
A method and device that process single-cell transcriptome sequencing data using multiple comparison and identification methods to filter and integrate somatic mutation sites, followed by mutation screening with quality and reproduction conditions, and employing a logistic regression model for prediction, to minimize noise and retain real mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-cell RNA sequencing is used to detect somatic mutations, then transcriptome information can be obtained, but the sparsity of detectable mutations and introduction of spurious signals reduce detection precision
Solution Approach 1:
The patent segments the mutation detection process into multiple independent stages: (1) primary mutation site identification using first comparison mode, (2) secondary identification using second comparison mode, (3) integration of results, and (4) quality filtering. This segmentation allows each stage to focus on specific aspects of mutation detection, improving overall precision while managing noise at each step.
Solution Approach 2:
The patent employs different comparison modes (first comparison mode and second comparison mode) with distinct parameters and thresholds for identifying mutation sites. By changing the detection parameters across different stages, the system can adapt to different characteristics of true mutations versus noise, thereby improving detection precision.
2Measurement precision
If multiple comparison and identification methods are used to improve detection precision, then more accurate mutation sites can be identified, but the complexity of the analysis process increases
Solution Approach 1:
The patent merges the outputs of multiple independent mutation identification methods into a unified candidate somatic mutation site list through an integration step. By combining results from different comparison modes and then applying a consolidated filtering mechanism, the system achieves high precision while managing complexity through result consolidation rather than requiring all methods to operate independently throughout the entire pipeline.
3Reliability
If strict quality filtering is applied to remove noise, then false positive mutations are reduced, but some real mutations may be lost
Solution Approach 1:
The patent performs preliminary identification of mutation sites using multiple comparison modes before applying strict quality filtering. This preliminary action creates a broader candidate list that includes both true mutations and false positives, allowing the subsequent filtering stage to operate on a more comprehensive dataset and reduce the risk of losing real mutations while still removing noise.
Solution Approach 2:
The patent implements a multi-stage filtering process where quality metrics are evaluated at different levels (individual mutation sites and across cell populations). The feedback from quality assessment at each stage informs subsequent filtering decisions, allowing the system to retain mutations that meet overall reliability criteria while removing clearly false positives, thus balancing reliability with information retention.
Data Source
AI summary
A method for extracting somatic mutations from single-cell transcriptome sequencing data includes processing single-cell transcriptome raw sequencing data with a first comparison and identification method to obtain a plurality of first somatic mutation sites, processing the single-cell transcriptome raw sequencing data with a second comparison and identification method to obtain a plurality of second somatic mutation sites, integrating the plurality of first somatic mutation sites and the plurality of second somatic mutation sites to obtain a plurality of candidate somatic mutation sites, and performing mutation screening on the plurality of candidate somatic mutation sites to obtain final somatic mutation sites.