Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes

Abstract

Co-expression analysis has been employed to predict gene function, identify functional modules, and determine tumor subtypes. Previous co-expression analysis was mainly conducted at bulk tissue level. It is unclear whether co-expression analysis at the single-cell level will provide novel insights into transcriptional regulation. Here we developed a computational approach to compare glioblastoma expression profiles at the single-cell level with those obtained from bulk tumors. We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics. The co-expressed genes identified in bulk tumors tend to have similar biological functions, and are enriched for intrachromosomal interactions with synchronized promoter activity. In contrast, single-cell co-expressed genes are enriched for known protein-protein interactions, and are regulated through interchromosomal interactions. Moreover, gene members of some protein complexes are co-expressed only at the bulk level, while those of other complexes are co-expressed at both single-cell and bulk levels. Finally, we identified a set of co-expressed genes that can predict the survival of glioblastoma patients. Our study highlights that comparative analyses of single-cell and bulk gene expression profiles enable us to identify functional modules that are regulated at different levels and hold great translational potential.

Deeper insights into long-term survival heterogeneity of Pancreatic Ductal Adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

“…However TSPAN8 promote cancer cell stemness via activation of Hedgehog signaling 37 Systems biology approaches can provide immediate functional insights by revealing interactions between genes 39 . A motivation for WGCNA is that genes functioning together are regulated or co-expressed together 40 . Ballouz and cauthor 41 suggested a minimal of 20 samples to predict meaningful functional connectivity.…”

“…Two genes, NOSTRIN and ADGRG6, were shared by 66% of LTS, and have been reported before to be associated with PDAC survival. 40,53 Drugs bind to their target proteins and perturb the transcriptome of a cancer cell 54 . In our study, analytic functional analysis of individual PEEPs helped to decode homogeneity patterns within LTS.…”

Annotating the Function of Protein-coding Genes Based on Gene Ontology Terms of Neighboring Co-expressed Genes

“…For H. sapiens, Wang and colleagues recently compared the expression profiles of bulk tissue of glioblastoma patients to expression profiles at single-cell level [35]. Interestingly, they found that coexpression in bulk samples was stronger associated with similar gene function than that in single cell samples.…”

“…Also genes in eukaryotic genomes have been reported to have a tendency to cluster when showing similar expression, and the genes in these clusters tend to have related functions [28][29][30][31][32][33]. Wang and colleagues, as well as Barkai and colleagues showed that if two eukaryotic genes have the same expression levels in different conditions, they are likely to be members of the same protein complex or to participate in the same biological pathways [34,35]. Also, Lee and Sonnhammer reported that genes involved in the same biochemical pathways tend to gather in various eukaryotic genomes [31].…”

Applications of community detection algorithms to large biological datasets

“…More recently, clustering analysis was used to identify and characterize cell types in various tissues and tumors in the colon 19 , brain 20 , blood 12 , and lung 21 , with the overall aim of finding key stem and progenitor cell populations involved in tissue development, repair, and tumorigenesis. Another application is to find sets of coordinately regulated genes in order to find gene modules 11,22,23 . Such clusters of genes (or other features such as single-nucleotide polymorphism (SNPs) 24 ) can be further analyzed by gene set enrichment approaches to identify gene annotations 25 (e.g.…”