Construction of rat aortic smooth muscle cell line A7r5-specific gene expression matrix

The purpose of our study is to construct rat aortic smooth cell line A7r5-specific interactome by aggregating publically available microarray samples, and provided a global view of biological process and identified transcription factors that regulate the transcriptomic changes. We collected 30 samples of A7r5 cell line microarray from 2 prior studies and our own study data, GSE87439. We performed normalization for Affymetrix microarray platform using SCAN.UPC R package (Release 3.9, PMID: 22959562, https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). After normallizatin, we performed batch effect adjusting because datasets came from different laboratoriesand, and combined the datasets . The matrix data we deposited in GEO has normalized log2 signal intensity for 14,004 genes which are common across datasets. The genes were mapped to Rattus norvegicus Entrez Gene IDs. A7r5-specific interactome was assembled by Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe, PMID: 16723010). Then, we used Master Regulator Analysis-Fisher’s Extact Test (MRA-FET, PMID: 20531406) to infer transcription factors which control a gene signature changes by the effect of SCE and SchB on the TGFβ1-treated A7r5 cells (PMID: 29423034). We discovered Junb, Srebf2, Foxk2, and Irf9 as a master regulator to modulate the gene signatures on the TGFβ1-treated A7r5 cells. Our findings will provide biological insights into the pharmaceutical effect of SCE and SchB in A7r5 cells. A total of 3 datasets containing 30 microarray samples were downloaded from NCBI GEO (http://ncbi.nlm.nih.gov/geo). For Affymetrix platforms set, we used SCAN.UPC R package to nomalize the data. We mapped the probe set IDs to Rattus norvegicus Entrez Gene IDs. Significant gene signature: Limma analysis (PMID: 25605792) was used to identify genes of expression reversed by SCE or SchB efficacy on TGFβ1-treated A7r5 cells in the GSE87439 data (Please see reference paper for details). We found out SCE and SchB up- or down-effective gene set and applied these gene sets MRA-FET analysis to infer transcription factors that control the gene sets. A7r5-specific interactome and Master Regulator Analysis (MRA): From the 30 microarray samples that went through normalization and batch adjusting. Then, ARACNe built up regulatory interactions between the 14,004 genes and 891 rat transcription factors (TFs) through mutual information (MI) calculation. From the built A7r5-specific interactome, MRA-FET inferred master regulator (MR) candidates which control the SCE and SchB up- or down-effective gene set on A7r5 cells. The 30 samples of 3 dataset are summarized in the meta-data spreadsheet. The meta-data have sample names with GSE accession number, microarray platform, normalization method, original experiment type. The matrix data txt file has normalized log2 expression values of 30 samples.