dtangle: accurate and robust cell type deconvolution.
Bioinformatics
; 35(12): 2093-2099, 2019 06 01.
Article
em En
| MEDLINE
| ID: mdl-30407492
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 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 AND IMPLEMENTATION dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos