SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data.

ABSTRACT: Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including:

SUBMITTER: Lu X 

PROVIDER: S-EPMC8294548 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature