J Biomed Inform - A comparative study of covariance selection models for the inference of gene regulatory networks.

Tópicos

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Resumo

TIVATION: The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology.RESULTS: In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'l(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that l(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling.AVAILABILITY: Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip.

Resumo Limpo

tivat infer reverseengin gene regulatori network express data descript complex depend structur among gene open issu modern molecular biologyresult paper compar three regular method covari select infer gene regulatori network develop circumv problem rais number observ n smaller number gene p examin approach provid three altern estim invers covari matrix pinv method base moorepenros pseudoinvers b rcm method perform correl regress residu c lc method maxim proper regular loglikelihood function extens simul studi show lc outperform two method predict partial correl estim highest valu sensit infer condit depend gene even number observ avail applic method infer gene network isoprenoid biosynthesi pathway arabidopsi thaliana allow enlighten negat partial correl coeffici two hub two isoprenoid pathway import provid evid crosstalk gene plastidi cytosol pathway appli gene express data relat signatur hras oncogen human cell cultur method reveal gene pvalu direct interact hras share rasrespons bind site transcript factor rreb result suggest transcript activ gene mediat common transcript factor downstream ras signalingavail softwar implement method form matlab script avail httpusersbacnritissiaiesinacovselmodelscodeszip

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