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SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

Genomics, Proteomics & Bioinformatics 2022年第5期20卷 912-927页

作者： Alexandra J.Lee Dallas L.Mould Jake Crawford Dongbo Hu Rani K.Powers georgia doing James C.Costello Deborah A.Hogan Casey S.Greene Genomics and Computational Biology Graduate Program University of PennsylvaniaPhiladelphiaPA 19104USA Department of Microbiology and Immunology Geisel School of Medicine at DartmouthHanoverNH 03755USA Department of Systems Pharmacology and Translational Therapeutics University of PennsylvaniaPhiladelphiaPA 19104USA Wyss Institute for Biologically Inspired Engineering Harvard UniversityBostonMA 02115USA Department of Pharmacology University of Colorado School of MedicineDenverCO 80045USA Center for Health AI University of Colorado School of MedicineDenverCO 80045USA Department of Biochemistry and Molecular Genetics University of Colorado School of MedicineDenverCO 80045USA

Genome-wide transcriptome profiling identifies genes that are prone to differential expression(DE)across contexts,as well as genes with changes specific to the experimental *** genes that are specifically changed in a context of interest from common differentially expressed genes(DEGs)allows more efficient prediction of which genes are specific to a given biological process under ***,common DEGs or pathways can only be identified through the laborious manual curation of experiments,an inordinately time-consuming *** we pioneer an approach,Specific cOntext Pattern Highlighting In Expression data(SOPHIE),for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be *** apply SOPHIE to diverse datasets including those from human,human cancer,and bacterial pathogen Pseudomonas *** identifies common DEGs in concordance with previously described,manually and systematically determined common *** molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional ***’s measure of specificity can complement log2 fold change values generated from traditional DE *** example,by filtering the set of DEGs,one can identify genes that are specifically relevant to the experimental condition of ***,these results can inform future research *** scripts used in these analyses are available at https://***/greenelab/*** can access https://***/greenelab/sophie to run SOPHIE on their own data.

关键词： Neural network Transcriptomics Software Differential expression analysis Machine learning

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