Journal article

dtangle: accurate and robust cell type deconvolution

Gregory J Hunt, Saskia Freytag, Melanie Bahlo, Johann A Gagnon-Bartsch

Bioinformatics | Oxford University Press (OUP) | Published : 2019

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 11 benchmark datasets showing that dtangle is competitive with published deconvoluti..

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Grants

Awarded by NHMRC


Awarded by National Science Foundation


Funding Acknowledgements

Work by S.F. and M.B. was supported by the Victorian Government's Operational Infrastructure Support Program and Australian Government NHMRC IRIIS. M.B. is funded by NHMRC Senior Research Fellowship 110297 and NHMRC Program Grant 1054618. G.H. and J.G. were supported by the National Science Foundation grant no. DMS-1646108.