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Pré-Publication, Document De Travail Année : 2020

Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

Laura Cantini
Pooya Zakeri
  • Fonction : Auteur
Céline Hernandez
Aurelien Naldi
Denis Thieffry
Elisabeth Remy
  • Fonction : Auteur
Anaïs Baudot

Résumé

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.
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.
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Dates et versions

hal-02998156 , version 1 (12-11-2020)

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Laura Cantini, Pooya Zakeri, Céline Hernandez, Aurelien Naldi, Denis Thieffry, et al.. Benchmarking joint multi-omics dimensionality reduction approaches for cancer study. 2020. ⟨hal-02998156⟩

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