Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

• Published in: Genomics (San Diego, Calif.) Calif.), 2020-09, Vol.112 (5), p.3157-3165
• Main Authors: Abbassi-Daloii, Tooba, Kan, Hermien E., Raz, Vered, 't Hoen, P.A.C.
• Format: Article
• Language: English
• Subjects: Co-expressed genes; Differential expression; Module detection; Prior knowledge; Skeletal muscle
• ISSN: 0888-7543; 1089-8646
• DOI: 10.1016/j.ygeno.2020.05.026

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues. •Subtle differences between similar tissues may be hidden by technical variation.•Hypothesis-driven approaches complement data-driven approaches.•Optimal WGCNA settings can be found by assessing overlaps with biological networks.