Likelihood-based deconvolution of bulk gene expression data using single-cell references.

ABSTRACT: Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample that obscure whether observed differences are actually caused by changes in the expression levels themselves or are simply a result of differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory a

SUBMITTER: Erdmann-Pham DD 

PROVIDER: S-EPMC8494215 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature