DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing
Year: 2021
Type: article
Abstract: Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.
Source: Nucleic Acids Research
DOI: 10.1093/nar/gkab031
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,800
Funders: National Institutes of Health, University of North Carolina at Chapel Hill, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Institute of Environmental Health Sciences, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Common Fund
Awards: G1017297203, G1084410269, G1133919164, Biology of Race and Progression Associated Breast Tumor Gene Expression, G1272756610, G1569999668, G1660206989, G2480835067, G3369851660, G3713631067, G39175869, G5446091787, G5543907513, G6011884240, G6920676432, Epidemiology of Breast Cancer Subtypes in African American Women: a Consortium, G8041609612, G879385275, G9000007053, G9000007592, G9000008043, G9000034599, G9000099626, G9000176900, A Modular Framework for Accurate, Interpretable, and Reproducible Analysis of Long Read RNA-Seq Data, Cancer Center Support Grant
OpenAlex ID: W3126812235