COTAN: scRNA-seq data analysis based on gene co-expression.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Accurate inference of gene co-expression in single-cell RNA sequencing (scRNA-seq) is essential for understanding cell identity.
• Low efficiency of scRNA-seq necessitates sensitive computational methods to analyze transcription profiles.
• Existing methods often focus on positive gene counts, potentially missing crucial information from zero counts.

Purpose of the Study:

• Introduce COTAN, a novel statistical and computational method for single-cell gene co-expression analysis.
• Provide a foundation for single-cell gene interactome analysis.
• Develop a tool to identify cell-identity markers and study gene interactions.

Main Methods:

• COTAN utilizes a generalized contingency tables framework to analyze the distribution of zero UMI counts.
• It assesses correlated or anti-correlated gene pair expression using a new correlation index with an approximate p-value.
• The method evaluates differential gene expression using a global differentiation index and facilitates gene clustering based on co-expression patterns.

Main Results:

• COTAN accurately infers gene co-expression patterns at the single-cell level.
• It provides a novel correlation index and global differentiation index for assessing gene relationships and differential expression.
• The method effectively aids in identifying cell-identity markers and studying gene interactions, as demonstrated on neural development datasets.

Conclusions:

• COTAN offers a sensitive and accurate approach to analyze scRNA-seq data, particularly by leveraging zero UMI counts.
• It serves as a valuable tool for single-cell gene interactome analysis and the identification of cell-identity markers.
• As an R package, COTAN complements existing scRNA-seq analysis workflows.