Deconvolution of heterogeneous tumor samples using partial reference signals.

ABSTRACT: Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of

SUBMITTER: Qin Y 

PROVIDER: S-EPMC7728196 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature