2018
DOI: 10.1101/gr.227066.117
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Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

Abstract: Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal -regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptio… Show more

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Cited by 19 publications
(14 citation statements)
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“…We used the TF-phenotype association test procedure on a data set of 284 lymphoblastoid cell lines (LCLs) that have previously been assayed for their cytotoxic response (EC50) to each of 24 different treatments, mostly cancer drugs [17]. Gene expression and genotype data are also available for these LCLs.…”
Section: Resultsmentioning
confidence: 99%
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“…We used the TF-phenotype association test procedure on a data set of 284 lymphoblastoid cell lines (LCLs) that have previously been assayed for their cytotoxic response (EC50) to each of 24 different treatments, mostly cancer drugs [17]. Gene expression and genotype data are also available for these LCLs.…”
Section: Resultsmentioning
confidence: 99%
“…Drug response data were derived from dosage–response curves of 24 cytotoxic treatments published in Hanson et al [17]: 6MP, 6TG, ARAC, arsenic, carboplatin, CDDP, cladribine, docetaxel, doxorubicin, epirubicin, everolimus, fludarabine, gemcitabine, hypoxia, metformin, MPA, MTX, NAPQI, oxaliplatin, paclitaxel, radiation, rapamycin, TCN, and TMZ. The phenotype, called EC50, represents the concentration at which the drug reduces the population of LCL cells to half of the initial population.…”
Section: Methodsmentioning
confidence: 99%
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“…Transcriptome datasets, usually from time-series samples, have enabled us to infer gene expression networks using various statistical and machine learning ML-based algorithms ( Dewey and Galas, 2010 ). The inferred GRNs are complementary to gene networks obtained from other types of data: transcription factor networks based on high-throughput methods to examine the interaction between transcription factors (TFs) and DNA-binding sites on target genes ( Ikeuchi et al, 2018 ), and gene networks genetically determined using large-scale populations and mutant panels ( Fuxman Bass et al, 2015 ; Hanson et al, 2018 ).…”
Section: Introductionmentioning
confidence: 99%