Community assessment of methods to deconvolve cellular composition from bulk gene expression.

ABSTRACT: We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived im

SUBMITTER: White BS 

PROVIDER: S-EPMC11350143 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature