dtangle: accurate and robust cell type deconvolution
- Author(s)
- Hunt, GJ; Freytag, S; Bahlo, M; Gagnon-Bartsch, JA;
- Details
- Publication Year 2019-06,Volume 35,Issue #12,Page 2093-2099
- Journal Title
- Bioinformatics
- Publication Type
- Journal Article
- Abstract
- Motivation: Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA). Results: We propose dtangle, a new cell type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell type proportions using publicly available, often cross-platform, reference data.We evaluate dtangle on eleven benchmark data sets showing that dtangle is competitive with published deconvolution methods, is robust to outliers and selection of tuning parameters, and is fast. As a case study, we investigate the human immune response to Lyme disease. dtangle's estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status. Availability: dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io). Supplementary Information: Supplementary data are available at Bioinformatics online.
- Publisher
- Oxford Academic
- Research Division(s)
- Population Health And Immunity
- PubMed ID
- 30407492
- Publisher's Version
- https://doi.org/10.1093/bioinformatics/bty926
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2018-11-20 09:12:49
Last Modified: 2019-07-24 02:27:56