SDMCC: Sample-wise Debiased Multilevel Contrastive Clustering for Single-cell Gene Expression Data

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell gene expression profiling offers high-resolution tissue analysis.
• Accurate cell clustering is crucial for identifying cell types and heterogeneity.
• Existing contrastive clustering methods struggle with data noise, sparsity, and batch effects.

Purpose of the Study:

• To develop a novel algorithm for robust single-cell clustering.
• To overcome limitations of existing methods, including batch effects, information fusion, and centroid instability.
• To improve the accuracy and biological interpretability of single-cell data analysis.

Main Methods:

• Proposed sample-wise debiased multilevel contrastive clustering with shrinkage risk regularization (SDMCC).
• Implemented sample-wise correction and batch-size adaptation modules to mitigate data distortion.
• Introduced multilevel contrastive learning integrating instance-level and cluster-level information.
• Incorporated shrinkage risk regularization to prevent trivial solutions.

Main Results:

• SDMCC demonstrated superior clustering accuracy on multiple single-cell datasets.
• The method effectively identified fine-grained cellular variations and maintained coarse-grained semantic consistency.
• SDMCC uncovered biologically meaningful patterns in gene expression data.

Conclusions:

• SDMCC offers a robust solution for single-cell clustering challenges posed by noise and sparsity.
• The algorithm enhances the identification of cell types and characterization of cellular heterogeneity.
• SDMCC shows significant potential for advancing biomedical research through improved single-cell data analysis.