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2016 ; 32
(18
): 2809-16
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Integrated gene set analysis for microRNA studies
#MMPMID27324197
Garcia-Garcia F
; Panadero J
; Dopazo J
; Montaner D
Bioinformatics
2016[Sep]; 32
(18
): 2809-16
PMID27324197
show ga
MOTIVATION: Functional interpretation of miRNA expression data is currently done
in a three step procedure: select differentially expressed miRNAs, find their
target genes, and carry out gene set overrepresentation analysis Nevertheless,
major limitations of this approach have already been described at the gene level,
while some newer arise in the miRNA scenario.Here, we propose an enhanced
methodology that builds on the well-established gene set analysis paradigm.
Evidence for differential expression at the miRNA level is transferred to a gene
differential inhibition score which is easily interpretable in terms of gene sets
or pathways. Such transferred indexes account for the additive effect of several
miRNAs targeting the same gene, and also incorporate cancellation effects between
cases and controls. Together, these two desirable characteristics allow for more
accurate modeling of regulatory processes. RESULTS: We analyze high-throughput
sequencing data from 20 different cancer types and provide exhaustive reports of
gene and Gene Ontology-term deregulation by miRNA action. AVAILABILITY AND
IMPLEMENTATION: The proposed methodology was implemented in the Bioconductor
library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of
reproducibility all of the scripts are available at
https://github.com/dmontaner-papers/gsa4mirna CONTACT: : david.montaner@gmail.com
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics
online.
free
free
free
