Cargando…
Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR metho...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785750/ https://www.ncbi.nlm.nih.gov/pubmed/33402734 http://dx.doi.org/10.1038/s41467-020-20430-7 |
_version_ | 1783632488370798592 |
---|---|
author | Cantini, Laura Zakeri, Pooya Hernandez, Celine Naldi, Aurelien Thieffry, Denis Remy, Elisabeth Baudot, Anaïs |
author_facet | Cantini, Laura Zakeri, Pooya Hernandez, Celine Naldi, Aurelien Thieffry, Denis Remy, Elisabeth Baudot, Anaïs |
author_sort | Cantini, Laura |
collection | PubMed |
description | High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper 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 perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook—multi-omics mix (momix)—to foster reproducibility, and support users and future developers. |
format | Online Article Text |
id | pubmed-7785750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77857502021-01-14 Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer Cantini, Laura Zakeri, Pooya Hernandez, Celine Naldi, Aurelien Thieffry, Denis Remy, Elisabeth Baudot, Anaïs Nat Commun Article High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper 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 perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook—multi-omics mix (momix)—to foster reproducibility, and support users and future developers. Nature Publishing Group UK 2021-01-05 /pmc/articles/PMC7785750/ /pubmed/33402734 http://dx.doi.org/10.1038/s41467-020-20430-7 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cantini, Laura Zakeri, Pooya Hernandez, Celine Naldi, Aurelien Thieffry, Denis Remy, Elisabeth Baudot, Anaïs Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
title | Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
title_full | Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
title_fullStr | Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
title_full_unstemmed | Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
title_short | Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
title_sort | benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785750/ https://www.ncbi.nlm.nih.gov/pubmed/33402734 http://dx.doi.org/10.1038/s41467-020-20430-7 |
work_keys_str_mv | AT cantinilaura benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer AT zakeripooya benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer AT hernandezceline benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer AT naldiaurelien benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer AT thieffrydenis benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer AT remyelisabeth benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer AT baudotanais benchmarkingjointmultiomicsdimensionalityreductionapproachesforthestudyofcancer |