Computational protocol: Detection of statistically significant network changes in complex biological networks

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Protocol publication

[…] We used the TCGA pan-glioma samples dataset including 1250 samples (463 IDH-mutant and 653 IDH-wild-type), 583 of which profiled with Agilent microarray and 667 with RNA-Seq Illumina HiSeq (REF) downloaded from the TCGA portal. The batch effects between the two platform were corrected using the COMBAT algorithm []. The final gene expression data matrix includes 12,985 genes and 1250 samples. We re-constructed two gene regulatory networks belonging to two different glioma subtypes: IDH-mutant and IDH-wild-type. Both networks were re-constructed with a four step procedure that follows ARACNe []: i) Computation of mutual information between gene expression profiles to determine interaction between Transcription Factors (TFs) and target genes []; ii) data processing inequality to filter out indirect relationships [], iii) permutation test with 1000 re-samplings to keep only statistically significant relationships. We also assembled a global glioma network using all the available 1250 transcriptional profiles using the aforementioned method. In this last case we also used intersection with transcription factor (TF) binding sites to keep only relationships due to promoter binding. We used a set of 457 TF binding sites available in the MotifDB Bioconductor package.Master Regulator Analysis (MRA) algorithm [] was applied to the global glioma network in order to compute the statistical significance of the overlap between the regulon of each TF (i.e. its ARACNe inferred targets) and the differentially expressed gene list (Wilcoxon-Mann-Whitney test FDR≤0.05) between IDH-mutant and IDH-wild-type samples. Given a gene interaction network, generated by ARACNe and a gene phenotype signature (e.g. a set of differentially expressed genes), the MRA algorithm computes for each TF the enrichment of the phenotype signature in the regulon of that TF. The regulon of a TF is defined as its neighborhood in the gene interaction network. There are two different methods to evaluate the enrichment of the signature in the regulon. One method uses the statistical Fisher’s exact test, while the other approach uses Gene Set Enrichment Analysis (GSEA). Here we used this last method.A Master Regulator (MR) gene is a TF which regulon exhibit a statistical significant enrichment of the given phenotype signature. […]

Pipeline specifications

Software tools ComBat, ARACNE
Databases TCGA Data Portal MotifDb
Application RNA-seq analysis