Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Abstract

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

A framework using topological pathways for deeper analysis of transcriptome data

“…In this section, same pathway analysis [23] on p53 mutation dataset [34] is performed with Deep pathway analysis (DPA). The corresponding results is then compared against the APA [23], ORA [35], GSCA [36], GSNCA [37], ESEA [38], SPIA [15], PWEA [39] and DRAGEN [22].…”

“…The p53 mutation microarray dataset has been widely used as a pathway enrichment analysis benchmark, containing 33 test samples having mutated p53 and 17 wild type control samples. First, the test vs control data is processed with LIMMA [5] using the same parameter settings as [23] and the detected significantly (LIMMA p-value< 0.05) Differentially Expressed(DE) genes logFC score is used as r data for DPA. Since TP53 is known mutated in this sample, we set m = −1 for TP53 and m = NULL for all others.…”

“…Since TP53 is known mutated in this sample, we set m = −1 for TP53 and m = NULL for all others. We attempted to follow the exact same procedure used for the pathway analysis described in [23] as our objective was to make a direct comparison between APA [23] and our work DPA. The pathways having at least one target gene of p53 is labeled as 1 and the other pathways are labeled as 0 where the targets of p53 are obtained from [40].…”

“…DRAGEN by [22] detects differentially expressing genes by performing a hypothesis testing designed to figure out if linear model has identical parameters. Most recently, Altered Pathway Analysis tool (APA) by [23] aims to detect altered pathways by dynamically calculating pathway rewiring through analyzing correlation between genes, but this system does not use prior knowledge. Our work is different from these existing topology-based systems by the feature, what we call, route-based recognition capability, and using this feature we can produce deeper analysis outcomes suggesting how identified "perturbed" pathway routes may interact with each other.…”

“…Section III describes the results of our algorithm being tested using a public domain temporal microarray data set from the cell cycle experiment [24]. Afterwards we show the outcome of applying our algorithm to the p53 mutation microarray data and specifically compare our analysis outcome with the similar analysis done by [23]. Two case studies are shown to demonstrate the generality of our enhanced method.…”