典型文献
SOPHIE:Generative Neural Networks Separate Common and Specific Transcriptional Responses
文献摘要：
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 manip-ulation.Distinguishing genes that are specifically changed in a context of interest from common dif-ferentially expressed genes(DEGs)allows more efficient prediction of which genes are specific to a given biological process under scrutiny.Currently,common DEGs or pathways can only be iden-tified through the laborious manual curation of experiments,an inordinately time-consuming endeavor.Here we pioneer an approach,Specific cOntext Pattern Highlighting In Expression data(SOPHIE),for distinguishing between common and specific transcriptional patterns using a gener-ative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated.We apply SOPHIE to diverse datasets including those from human,human cancer,and bacterial pathogen Pseudomonas aeruginosa.SOPHIE identifies common DEGs in concordance with previously described,manually and systematically determined common DEGs.Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes.SOPHIE's measure of specificity can complement l0g2 fold change values generated from traditional DE analyses.For example,by filtering the set of DEGs,one can identify genes that are specifically relevant to the experimental condition of interest.Consequently,these results can inform future research direc-tions.All scripts used in these analyses are available at can access to run SOPHIE on their own data.
文献关键词：
中图分类号：
作者姓名：
Alexandra J.Lee;Dallas L.Mould;Jake Crawford;Dongbo Hu;Rani K.Powers;Georgia Doing;James C.Costello;Deborah A.Hogan;Casey S.Greene
作者机构：
Genomics and Computational Biology Graduate Program,University of Pennsylvania,Philadelphia,PA 19104,USA;Department of Microbiology and Immunology,Geisel School of Medicine at Dartmouth,Hanover,NH 03755,USA;Department of Systems Pharmacology and Translational Therapeutics,University of Pennsylvania,Philadelphia,PA 19104,USA;Wyss Institute for Biologically Inspired Engineering,Harvard University,Boston,MA 02115,USA;Department of Pharmacology,University of Colorado School of Medicine,Denver,CO 80045,USA;Center for Health AI,University of Colorado School of Medicine,Denver,CO 80045,USA;Department of Biochemistry and Molecular Genetics,University of Colorado School of Medicine,Denver,CO 80045,USA
文献出处：
引用格式：
[1]Alexandra J.Lee;Dallas L.Mould;Jake Crawford;Dongbo Hu;Rani K.Powers;Georgia Doing;James C.Costello;Deborah A.Hogan;Casey S.Greene-.SOPHIE:Generative Neural Networks Separate Common and Specific Transcriptional Responses)[J].基因组蛋白质组与生物信息学报（英文版）,2022(05):912-927
A类：
SOPHIE,curation,inordinately,cOntext,Highlighting,l0g2
B类：
Generative,Neural,Networks,Separate,Common,Specific,Transcriptional,Responses,Genome,wide,transcriptome,profiling,identifies,genes,that,are,prone,differential,expression,across,contexts,well,changes,experimental,manip,ulation,Distinguishing,specifically,changed,interest,from,common,ferentially,expressed,DEGs,allows,more,efficient,prediction,which,given,process,under,scrutiny,Currently,pathways,only,tified,through,laborious,experiments,consuming,endeavor,Here,pioneer,approach,Pattern,In,Expression,distinguishing,between,transcriptional,patterns,using,neural,network,create,background,null,distribution,generated,We,apply,diverse,datasets,including,those,human,cancer,bacterial,pathogen,Pseudomonas,aeruginosa,concordance,previously,described,manually,systematically,determined,Further,molecular,validation,indicates,detects,highly,magnitude,biologically,relevant,measure,specificity,complement,fold,values,traditional,analyses,For,example,by,filtering,identify,condition,Consequently,these,results,inform,future,research,direc,tions,All,scripts,used,available,access,run,their,own
AB值：
0.559967
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