Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.

ABSTRACT: Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mutations, and DNA methylation. Here we proposed the machine learning Multi-omics Tumor Purity prediction (MoTP) algorithm to estimate tumor purity based on multiple types of omics data. MoTP utilizes the Bayesian Regularized Neural Networks as the prediction algorithm, and Consensus Tumor Purity Estimates as labels. We trained MoTP using multi-omics data (mRNA, microRNA, long non-coding RNA, and DNA methylation) across 21 TCGA solid cancer types. By testing MoTP in TCGA validation sets, TCGA test sets, and eight datasets outside the TCGA cancer cohorts, we showed that although MoTP could achieve excellent performance in predicting tumor purity

SUBMITTER: Lu Q 

PROVIDER: S-EPMC11826339 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature