Single-cell clustering method and system based on consistency matrix score

By using consistency matrix scoring and SCM-tom distance metric, the optimal preprocessing and dimensionality reduction methods are identified, solving the flexibility and accuracy issues of single-cell clustering algorithms and achieving more efficient single-cell data clustering.

CN116741267BActive Publication Date: 2026-02-13SHANDONG UNIV
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Patent Information

Application Number
CN202310713091.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-06-15
Publication Date
2026-02-13
Estimated Expiration
2043-06-15

AI Technical Summary

Technical Problem

Existing single-cell clustering algorithms suffer from limitations such as simplistic and inflexible data preprocessing and dimensionality reduction methods on different datasets, lack of reasonable metrics, and coarse Euclidean distance calculations leading to the loss of effective distance information, making accurate clustering difficult.

Method used

A consistency matrix scoring method is adopted, which identifies the optimal combination of preprocessing and dimensionality reduction by f-value scoring. The SCM-tom distance metric is designed, and the optimal distance is obtained by utilizing the topological information between cells to construct the final clustering result.

Benefits of technology

It improves the accuracy of single-cell clustering, solves the sensitivity problem of different datasets, and enhances the clustering effect, which is significantly better than traditional methods.

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Abstract

The application belongs to the field of single cell clustering methods, and provides a single cell clustering method and system based on consistency matrix scoring, multiple consistency matrices are obtained after combined dimension reduction based on gene expression data, each consistency matrix is clustered to obtain a corresponding clustering result; the f-value of each consistency matrix is calculated by using a scoring method in combination with the consistency matrix and the corresponding clustering result, the consistency matrix corresponding to the highest f-value score is the optimal consistency matrix; based on the obtained optimal consistency matrix, a distance matrix between cells is constructed, and the final clustering result is obtained by using hierarchical clustering on the distance matrix between cells. The indirect distance between cells is fully utilized, and the clustering effect is improved.
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