TY - JOUR T1 - Data-driven robust detection of tissue/cell-specific markers JF - bioRxiv DO - 10.1101/517961 SP - 517961 AU - Lulu Chen AU - David M. Herrington AU - Robert Clarke AU - Guoqiang Yu AU - Yue Wang Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/01/11/517961.abstract N2 - Tissue/cell-specific marker genes (MGs) are defined as being exclusively and consistently expressed in a particular tissue/cell subtype across varying conditions. Detecting MGs plays a critical role in molecularly characterizing and conferring tissue/cell subtypes. Unfortunately, classic differential analysis assumes a convenient statistical distribution for the null hypothesis that however does not enforce MG definition and thus results in high false positives. Here we describe a statistically-principled method, One Versus Everyone Subtype Exclusively-expressed Genes (OVESEG) test, and propose a mixture null distribution model estimated via novel permutation schemes. Validated with realistic synthetic data sets on both type 1 error and detection power, OVESEG-test applied to two benchmark gene expression data sets detects many known and de novo subtype-specific markers. The subsequent supervised deconvolution results, obtained using MGs detected by OVESEG-test, show superior performance as compared with that by peer methods. ER -