Fig. 1
From: Improved biomarker discovery through a plot twist in transcriptomic data analysis
Flow diagram of the three most used methods in the literature to analyze transcriptomic data plus one method (#4) proposed. Method #1 Analysis of the differentially expressed genes (DEGs) between two conditions followed by volcano plot visualization, functional enrichment analysis (FEA), and biomarker identification. #2 Construction of a gene co-expression network by using the WGCNA package (Langfelder and Horvath, [17]) in R, following the general WGCNA guidelines to perform FEA, identify modules and genes related to a trait of interest (Zhang and Horvath, 2005). #3 To filter the transcriptome by DEGs and conduct WGCNA using the filtered dataset and continue with FEA. In this study, we propose method #4 to use WGCNA and filter the output of the selected modules by DEGs to follow with FEA and biomarker discovery