Faculty, Staff and Student Publications
Language
English
Publication Date
11-22-2024
Journal
Briefings in Bioinformatics
DOI
10.1093/bib/bbaf056
PMID
39950745
PMCID
PMC11826339
PubMedCentral® Posted Date
2-14-2025
PubMedCentral® Full Text Version
Post-print
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 based on a single omics data type, the integration of multiple single omics data-based predictions can enhance the prediction performance. Moreover, we demonstrated the robustness of MoTP by testing it in datasets with Gaussian noise and feature missing. Benchmark analysis showed that MoTP outperformed most established tumor purity prediction algorithms, and that it required less running time and computational resource to fulfill the predictive task. Thus, MoTP would be an attractive option for computational tumor purity inference.
Keywords
Humans, Machine Learning, Neoplasms, Algorithms, DNA Methylation, Genomics, Bayes Theorem, Computational Biology, DNA Copy Number Variations, Neural Networks, Computer, Multiomics, tumor purity prediction, machine learning, omics data, algorithm
Published Open-Access
yes
Recommended Citation
, "Inferring Tumor Purity Using Multi-omics Data Based on a Uniform Machine Learning Framework MoTP" (2024). Faculty, Staff and Student Publications. 1011.
https://digitalcommons.library.tmc.edu/uthshis_docs/1011