Abstract
Abstract
Background
Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions.
Results
To systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions.
Conclusions
We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.
Funder
National Institute of General Medical Sciences
Cancer Prevention and Research Institute of Texas
Houston Endowment
Chao Family Foundation
Huffington Foundation
Publisher
Springer Science and Business Media LLC
Cited by
60 articles.
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