Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data

Reverter, Antonio, Hudson, Nicholas J., Nagaraj, Shivashankar H., Perez-Enciso, Miguel and Dalrymple, Brian P. (2010) Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics, 26 7: 896-904. doi:10.1093/bioinformatics/btq051


Author Reverter, Antonio
Hudson, Nicholas J.
Nagaraj, Shivashankar H.
Perez-Enciso, Miguel
Dalrymple, Brian P.
Title Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
Publication date 2010-02-09
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btq051
Volume 26
Issue 7
Start page 896
End page 904
Total pages 9
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Formatted abstract
Motivation:
Although transcription factors (TF) play a central regulatory role, their detection from expression data is limited due to their low, and often sparse, expression. In order to fill this gap, we propose a regulatory impact factor (RIF) metric to identify critical TF from gene expression data.

Results:
To substantiate the generality of RIF, we explore a set of experiments spanning a wide range of scenarios including breast cancer survival, fat, gonads and sex differentiation. We show that the strength of RIF lies in its ability to simultaneously integrate three sources of information into a single measure: (i) the change in correlation existing between the TF and the differentially expressed (DE) genes; (ii) the amount of differential expression of DE genes; and (iii) the abundance of DE genes. As a result, RIF analysis assigns an extreme score to those TF that are consistently most differentially co-expressed with the highly abundant and highly DE genes (RIF1), and to those TF with the most altered ability to predict the abundance of DE genes (RIF2). We show that RIF analysis alone recovers well-known experimentally validated TF for the processes studied. The TF identified confirm the importance of PPAR signaling in adipose development and the importance of transduction of estrogen signals in breast cancer survival and sexual differentiation. We argue that RIF has universal applicability, and advocate its use as a promising hypotheses generating tool for the systematic identification of novel TF not yet documented as critical.
Keyword Gene coexpression networks
Bovine skeletal-muscle
Breast-cancer growth
Sex determination
Promoter analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article number btq051

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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Created: Wed, 21 Mar 2012, 19:39:10 EST by Susan Allen on behalf of Institute for Molecular Bioscience