Affiliation:
1. School of Computer Science, Tel Aviv University, Tel Aviv, Israel
Abstract
It is becoming clear that bulk gene expression measurements represent an average over very different cells. Elucidating the expression and abundance of each of the encompassed cells is key to disease understanding and precision medicine approaches. A first step in any such deconvolution is the inference of cell type abundances in the given mixture. Numerous approaches to cell-type deconvolution have been proposed, yet very few take advantage of the emerging discipline of deep learning and most approaches are limited to input data regarding the expression profiles of the cell types in question. Here we present DECODE, a deep learning method for the task that is data-driven and does not depend on input expression profiles. DECODE builds on a deep unfolded non-negative matrix factorization technique. It is shown to outperform previous approaches on a range of synthetic and real data sets, producing abundance estimates that are closer to and better correlated with the real values.
Reference19 articles.
1. Şenbabaoğlu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger rna signatures. Genome Biol. 2016;17(1):1–25.
2. Nadel BB, Oliva M, Shou BL, Mitchell K, Ma F, Montoya DJ, Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples. Brief Bioinform. 2021;22(6):bbab265. arXiv:https://academic.oup.com/bib/article-pdf/22/6/bbab265/42242154/bbab265.pdf, doi:10.1093/bib/bbab265.
3. Cobos FA, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun. 2020;11(1):1–14.
4. Wang X, Park J, Susztak K, Zhang NR, Li M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019;10(1):1–9.
5. Patrick E, Taga M, Ergun A, Ng B, Casazza W, Cimpean M, Deconvolving the contributions of cell-type heterogeneity on cortical gene expression. PLOS Comput Biol. 2020;16(8):e1008120.