DOI: 10.1101/463828Jan 27, 2019Paper

Comprehensive evaluation of computational cell-type quantification methods for immuno-oncology

BioRxiv : the Preprint Server for Biology
Gregor SturmTatsiana Aneichyk

Abstract

MotivationThe composition and density of immune cells in the tumor microenvironment profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining, or single-cell sequencing is often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. ResultsWe developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of [~]11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and [~]1,800 samples f...Continue Reading

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Datasets Mentioned

BETA
GSE93722

Methods Mentioned

BETA
RNA-seq
FACS
scRNA-seq
fluorescence-activated cell sorting

Software Mentioned

bioconda
R
STAR
quanTIseq
SRA
EPIC
CIBERSORT
conda
GitHub
MATLAB

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