RT Journal Article
SR Electronic
T1 Benchmarking joint multi-omics dimensionality reduction approaches for cancer study
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.14.905760
DO 10.1101/2020.01.14.905760
A1 Laura Cantini
A1 Pooya Zakeri
A1 Celine Hernandez
A1 Aurelien Naldi
A1 Denis Thieffry
A1 Elisabeth Remy
A1 Anaïs Baudot
YR 2020
UL http://biorxiv.org/content/early/2020/01/14/2020.01.14.905760.abstract
AB High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines.We performed a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluated their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we used TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assessed their classification of multi-omics single-cell data.From these in-depth comparisons, we observed that intNMF performs best in clustering, while MCIA offers a consistent and effective behavior across many contexts. The full code of this benchmark is implemented in a Jupyter notebook - multi-omics mix (momix) - to foster reproducibility, and support data producers, users and future developers.