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 https://github.com/greenelab/generic-expres-sion-patterns.Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.