Transcriptome data analysis
All gene expression data set with gene accession numbers GSE46315,
GSE85391, and GSE13715 were obtained from gene expression omnibus (GEO)
database (de Peppo et al., 2013; Gjorevski et al., 2016; Lai, Asthana,
Cheng, & Kisaalita, 2011). For microarray data sets, in addition to the
normalized data, all annotation files in the GPL formats were downloaded
from the same database. Annotated gene expression data sets were
uploaded into NetworkAnalyst web-tool (Xia, Gill, & Hancock, 2015; Zhou
et al., 2019). For unnormalized data, log2 transformation was used to
perform normalization and Limma algorithm was applied as a statistical
algorithm to calculate differentially expressed genes (DEGs) (Xia et
al., 2015; Zhou et al., 2019). As a general rule for all data sets, we
used adjusted p-value <0.05 and log2 fold
change>1 to identify DEGs. The list of highly affected
genes with adjusted p-values <0.05 and log2 fold change
>1 were used to draw Venn diagrams.
In addition to microarray data sets, we used a Mouse RNA-sequencing data
set with accession number GSE85391. Read count matrix of this study was
downloaded from GEO database (Gjorevski et al., 2016)and was analyzed
with edgeR package in R to find differentially expressed genes
(Robinson, McCarthy, & Smyth, 2010). DEGs were filtered using edgeR
adjusted p-values <0.05 and log2 fold
change>1.