Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer
| Autor: | Cantini, Laura, Zakeri, Pooya, Hernandez, Céline, Naldi, Aurelien, Thieffry, Denis, Remy, Elisabeth, Baudot, Anaïs |
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| Přispěvatelé: | Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), ANR-20-CE45-0015,scMOmix,Méthodes pour l'intégration de données multi-omiques en cellule-unique(2020), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multifactor Dimensionality Reduction
Science Computational Biology Datasets as Topic Reproducibility of Results Molecular Sequence Annotation Survival Analysis [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] Article Neoplasm Proteins Computational biology and bioinformatics Gene Expression Regulation Neoplastic Benchmarking Gene Ontology Cell Line Tumor Neoplasms Machine learning Humans Data integration Single-Cell Analysis Algorithms |
| Zdroj: | Nature Communications Nature Communications, Nature Publishing Group, 2021, 12, ⟨10.1038/s41467-020-20430-7⟩ Nature Communications, 2021, 12, ⟨10.1038/s41467-020-20430-7⟩ Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021) |
| ISSN: | 2041-1723 |
| Popis: | 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. Advances in omics technology have resulted in the generation of multi-view data for cancer samples. Here, the authors compare dimensionality reduction techniques using simulated and TCGA data and identify the features of the methods with superior performance. |
| Databáze: | OpenAIRE |
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