A robust workflow to benchmark deconvolution of multi-omic data.

ABSTRACT:

Background

Tumour heterogeneity significantly affects cancer progression and therapeutic response, yet quantifying it from bulk molecular data remains challenging. Deconvolution algorithms, which estimate cell type proportions in bulk samples, offer a potential solution. However, there is no consensus on the optimal algorithm for transcriptomic or methylomic data.

Results

Here, we present an unbiased evaluation framework for the first comprehensive comparison of deconvolution algorithms across both omic types, including reference-based and -free approaches. Our evaluation covers raw performance, stability, and computational efficiency under varying conditions, such as gene dependencies, missing or additional cell types and diverse sample compositions. We apply this framework

SUBMITTER: Amblard E 

PROVIDER: S-EPMC12713266 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature