Cell-type-specific co-expression inference from single cell RNA-sequencing data

Abstract

The inference of gene co-expressions from microarray and RNA-sequencing data has led to rich insights on biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregated view of gene regulations that may be distinct across different cell types. The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. However, the high sequencing depth variations and measurement errors in scRNA-seq data present significant challenges in inferring cell-type-specific gene co-expressions, and these issues have not been adequately addressed in the existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, built on a general expression-measurement model that explicitly accounts for sequencing depth variations and measurement errors in the observed single cell data. Systematic evaluations show that most existing methods suffer from inflated false positives and biased co-expression estimates and clustering analysis, whereas CS-CORE has appropriate false positive control, unbiased co-expression estimates, good statistical power and satisfactory performance in downstream co-expression analysis. When applied to analyze scRNA-seq data from postmortem brain samples from Alzheimer's disease patients and controls and blood samples from COVID-19 patients and controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from other methods.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

“…The challenge in this task stems from the Granger causal relationship or time-dependent correlation [109]. A GCN can be used to analyze genes with similar functions or uncover the characteristics of genes in some diseases [110]. GCN and GRN are two different tasks because correlation does not imply causal relation [111].…”

MuDCoD: Multi-Subject Community Detection in Personalized Dynamic Gene Networks from Single Cell RNA Sequencing

“…In addition, thresholding the subject-specific co-expression networks to obtain adjacency matrices should also be tailored by taking into account the gene-gene correlation distribution characteristics of the datasets. Recent developments in testing of gene-gene correlations, like CS-CORE (Su et al, 2022), offer potential insights into binarizing co-expression networks.…”

“…While this approach is commonly practiced and appears reasonable for these datasets (Supp. Section S8), alternatives that formally test for the edges in the co-expression networks could also be employed (Su et al, 2022).…”

CosGeneGate Selects Multi-functional and Credible Biomarkers for Single-cell Analysis

“…For each cell type, we use CS-CORE 51 to infer the co-expression relation between the optimal marker genes and the other selected marker genes. The initial credible gene set of each cell type are the optimal marker gene sets.…”