MATTE: anti-noise module alignment for phenotype-gene-related analysis

Abstract

PurposeAlthough many transcriptome analysis methods find fundamental interactions or markers of some phenotypes, preservation of module or network is still a challenge. MethodsThe study developed a method to directly compare the transcriptome data of phenotypes and present the differences modularly, called Module Alignment of TranscripTomE(MATTE). ResultsMATTE performs better under high noise than differential co-expression(DC) clustering in the simulation experiments but still detects differential expression(DE) and DC genes. After subsequent annotation of cell types in single-cell data, MATTE obtained the best scores in both supervised and unsupervised learning, i. e. MATTE found meaningful markers. Finally, we apply MATTE in analyzing the transcriptome of Breast Cancer(BRCA). We have found five BRCA subtypes, and the characteristic of one subtype is detected in the form of a module network. ConclusionMATTE can find meaningful genes and modules, thus facilitating the downstream analysis task to obtain insight into biology.