netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression...

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-throughput gene expression data.
• scRNA-seq data suffer from technical "dropout events" (zero counts), complicating analysis.
• Existing methods often combine cell information in low-dimensional spaces to handle dropouts.

Purpose of the Study:

• Introduce netNMF-sc, a novel algorithm for scRNA-seq data analysis.
• Improve imputation of gene abundance and cell subpopulation clustering.
• Leverage gene-gene interaction networks for enhanced data representation.

Main Methods:

• Developed netNMF-sc using network-regularized non-negative matrix factorization.
• Incorporated prior knowledge of gene-gene interactions into the model.
• Applied the algorithm to both simulated and real scRNA-seq datasets.

Main Results:

• netNMF-sc demonstrated superior performance in cell clustering and gene-gene covariance estimation.
• Performance gains increased with higher dropout rates (>60%).
• Results showed robustness to variations in the input gene interaction network.

Conclusions:

• netNMF-sc effectively addresses dropout events in scRNA-seq data.
• The algorithm offers improved accuracy for cell subpopulation identification.
• Leveraging gene networks enhances the analysis of complex single-cell transcriptomic data.