Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

COTAN: scRNA-seq data analysis based on gene co-expression / Giulia Galfrè, Silvia; Morandin, Francesco; Pietrosanto, Marco; Cremisi, Federico; Helmer-Citterich, Manuela. - In: NAR GENOMICS AND BIOINFORMATICS. - ISSN 2631-9268. - 3:3(2021). [10.1093/nargab/lqab072]

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

Francesco Morandin;
2021

Abstract

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.
COTAN: scRNA-seq data analysis based on gene co-expression / Giulia Galfrè, Silvia; Morandin, Francesco; Pietrosanto, Marco; Cremisi, Federico; Helmer-Citterich, Manuela. - In: NAR GENOMICS AND BIOINFORMATICS. - ISSN 2631-9268. - 3:3(2021). [10.1093/nargab/lqab072]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2896221
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