CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data

• Published in: PLoS computational biology Vol. 15; no. 12; p. e1007510
• Main Authors: Kang, Kai, Meng, Qian, Shats, Igor, Umbach, David M, Li, Melissa, Li, Yuanyuan, Li, Xiaoling, Li, Leping
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2019; Public Library of Science (PLoS)
• Subjects: Biology; Biology and Life Sciences; Cell Line; Computational Biology - methods; Computer applications; Computer Simulation; Databases, Nucleic Acid - statistics & numerical data; Deconvolution; Environmental health; Estimates; Flow cytometry; Fluorescence; Gene expression; Gene Expression Profiling - statistics & numerical data; Gene sequencing; Genomes; Health sciences; Heterogeneity; Humans; Laboratories; Leukocytes - classification; Leukocytes - metabolism; Medicine and Health Sciences; Methods; Pattern Recognition, Automated; Physical Sciences; Research and Analysis Methods; Ribonucleic acid; RNA; Sequence Analysis, RNA - statistics & numerical data; Signal transduction; Tissues; Transcriptome; Unsupervised Machine Learning; North Carolina; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007510

Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address tissue heterogeneity have drawbacks. Experimental techniques, such as fluorescence-activated cell sorting, and single cell RNA sequencing are expensive. Computational approaches that use expression data from heterogeneous samples are promising, but most of the current methods estimate either cell-type proportions or cell-type-specific expression profiles by requiring the other as input. Although such partial deconvolution methods have been successfully applied to tumor samples, the additional input required may be unavailable. We introduce a novel complete deconvolution method, CDSeq, that uses only RNA-Seq data from bulk tissue samples to simultaneously estimate both cell-type proportions and cell-type-specific expression profiles. Using several synthetic and real experimental datasets with known cell-type composition and cell-type-specific expression profiles, we compared CDSeq's complete deconvolution performance with seven other established deconvolution methods. Complete deconvolution using CDSeq represents a substantial technical advance over partial deconvolution approaches and will be useful for studying cell mixtures in tissue samples. CDSeq is available at GitHub repository (MATLAB and Octave code): https://github.com/kkang7/CDSeq.