A data classification method and system for single-cell sequencing
By constructing cell topology maps and performing dynamic trajectory inference, combined with fuzzy clustering and characteristic gene screening, the problem of difficult to capture dynamic changes in cell state and parameter sensitivity in existing methods is solved, and more refined cell state annotation and heterogeneity assessment are achieved.
CN119724350BActive Publication Date: 2025-08-22刘国荣
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Patent Information
- Application Number
- CN202411638407.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-17
- Publication Date
- 2025-08-22
- Estimated Expiration
- 2044-11-17
AI Technical Summary
Technical Problem
Existing single-cell sequencing data analysis methods are difficult to capture the dynamic changes in cell state, the clustering results are sensitive to algorithm parameters, difficult to deal with complex cell population structure, and lack quantitative assessment of cell heterogeneity.
Method used
By constructing a cell topology map, cell division rate and RNA rate are calculated, dynamic trajectory inference is performed, and cell state annotation is achieved by combining fuzzy clustering and characteristic gene screening.
Benefits of technology
Accurately reflect the connection relationship and dynamic changes between cells, reduce sensitivity to parameters, provide quantitative evaluation of cellular heterogeneity, and improve the accuracy of cell state annotation.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention relates to the field of bioinformatics technology, and in particular to a data classification method and system for single-cell sequencing. The method comprises the following steps: obtaining an original gene expression matrix; performing gene data preprocessing on the original gene expression matrix to obtain a preprocessed gene expression matrix; constructing a cell topology map based on the preprocessed gene expression matrix to obtain a cell topology map; calculating cell division rates based on the cell topology map and the preprocessed gene expression matrix, and performing RNA rate calculation to obtain RNA rate data; adding node rates to the cell topology map based on the cell division rate data and the RNA rate data to obtain an enhanced cell topology map. The present invention better captures the dynamic changes of cell states, reduces sensitivity to parameters, processes complex cell population structures, and provides quantitative cell heterogeneity assessment indicators by constructing a dynamic map and analyzing its changing patterns.
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