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Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

Laura Cantini, Pooya Zakeri, Celine Hernandez, Aurelien Naldi, Denis Thieffry, Elisabeth Remy, Anaïs Baudot
doi: https://doi.org/10.1101/2020.01.14.905760
Laura Cantini
1Computational Systems Biology Team, Institut de Biologie de l’Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, Paris Sciences et Lettres Research University, 75005 Paris, France
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  • For correspondence: laura.cantini@ens.fr anais.baudot@univ-amu.fr
Pooya Zakeri
2Aix Marseille Univ, INSERM, Marseille Medical Genetics, CNRS, Marseille, France
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Celine Hernandez
1Computational Systems Biology Team, Institut de Biologie de l’Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, Paris Sciences et Lettres Research University, 75005 Paris, France
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Aurelien Naldi
1Computational Systems Biology Team, Institut de Biologie de l’Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, Paris Sciences et Lettres Research University, 75005 Paris, France
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Denis Thieffry
1Computational Systems Biology Team, Institut de Biologie de l’Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, Paris Sciences et Lettres Research University, 75005 Paris, France
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Elisabeth Remy
3Aix Marseille University, CNRS, Marseille Mathematics Institute, France
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Anaïs Baudot
2Aix Marseille Univ, INSERM, Marseille Medical Genetics, CNRS, Marseille, France
4Barcelona Supercomputing Center (BSC), Barcelona, 08034 Spain
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  • For correspondence: laura.cantini@ens.fr anais.baudot@univ-amu.fr
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Abstract

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.

Footnotes

  • https://github.com/ComputationalSystemsBiology/momix-notebook

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 14, 2020.
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Benchmarking joint multi-omics dimensionality reduction approaches for cancer study
Laura Cantini, Pooya Zakeri, Celine Hernandez, Aurelien Naldi, Denis Thieffry, Elisabeth Remy, Anaïs Baudot
bioRxiv 2020.01.14.905760; doi: https://doi.org/10.1101/2020.01.14.905760
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Benchmarking joint multi-omics dimensionality reduction approaches for cancer study
Laura Cantini, Pooya Zakeri, Celine Hernandez, Aurelien Naldi, Denis Thieffry, Elisabeth Remy, Anaïs Baudot
bioRxiv 2020.01.14.905760; doi: https://doi.org/10.1101/2020.01.14.905760

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