A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

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.

Automated summary

The study investigates the pitfalls and challenges of deconvolution analysis by examining various technical and biological factors using three benchmarking frameworks. These frameworks compare 11 popular deconvolution methods under 1766 conditions, providing new insights for researchers in the future application, standardization, and development of deconvolution tools on RNA-seq data.