EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.

Details

Serval ID
serval:BIB_AF1B21C1943C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.
Journal
Methods in molecular biology
Author(s)
Racle J., Gfeller D.
ISSN
1940-6029 (Electronic)
ISSN-L
1064-3745
Publication state
Published
Issued date
2020
Peer-reviewed
Oui
Volume
2120
Pages
233-248
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Gene expression profiling is nowadays routinely performed on clinically relevant samples (e.g., from tumor specimens). Such measurements are often obtained from bulk samples containing a mixture of cell types. Knowledge of the proportions of these cell types is crucial as they are key determinants of the disease evolution and response to treatment. Moreover, heterogeneity in cell type proportions across samples is an important confounding factor in downstream analyses.Many tools have been developed to estimate the proportion of the different cell types from bulk gene expression data. Here, we provide guidelines and examples on how to use these tools, with a special focus on our recent computational method EPIC (Estimating the Proportions of Immune and Cancer cells). EPIC includes RNA-seq-based gene expression reference profiles from immune cells and other nonmalignant cell types found in tumors. EPIC can additionally manage user-defined gene expression reference profiles. Some unique features of EPIC include the ability to account for an uncharacterized cell type, the introduction of a renormalization step to account for different mRNA content in each cell type, and the use of single-cell RNA-seq data to derive biologically relevant reference gene expression profiles. EPIC is available as a web application ( http://epic.gfellerlab.org ) and as an R-package ( https://github.com/GfellerLab/EPIC ).
Keywords
Cell fraction predictions, Computational biology, Gene expression analysis, Immunoinformatics, RNA-seq deconvolution, Tumor immune microenvironment
Pubmed
Create date
05/03/2020 15:10
Last modification date
21/03/2020 6:26
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