SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data. Issue 4 (24th November 2020)

• Record Type: Journal Article
• Authors: Lu, Xiaoyu; Tu, Szu-Wei; Chang, Wennan; Wan, Changlin; Wang, Jiashi; Zang, Yong; Ramdas, Baskar; Kapur, Reuben; Lu, Xiongbin; Cao, Sha; Zhang, Chi
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 4(2021)
• Publication Date: 2020-11-24
• Subjects: tissue data deconvolution -- cancer microenvironment -- semi-supervised learning -- mouse omics data; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa307
• Languages: English
• ISSNs: 1467-5463

Abstract

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: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD .