(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices.

Autor: Chowdhury, Hussain Ahmed; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar

Publication year: 2020

IEEE/ACM transactions on computational biology and bioinformatics

issn:1557-9964 1545-5963

doi: 10.1109/TCBB.2019.2893170

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Abstract:

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Language: eng

Rights:

Pmid: 30668502

Tags: Humans; Animals; Transcriptome/genetics; RNA-Seq; Oligonucleotide Array Sequence Analysis; *Gene Expression Profiling/methods/standards; Gene Regulatory Networks/genetics

Link: https://pubmed.ncbi.nlm.nih.gov/30668502/