BIODICA: a computational environment for Independent Component Analysis of omics data

Nicolas Captier; Jane Merlevede; Askhat Molkenov; Ainur Seisenova; Altynbek Zhubanchaliyev; Petr V Nazarov; Emmanuel Barillot; Ulykbek Kairov; Andrei Zinovyev

doi:10.1093/bioinformatics/btac204

BIODICA: a computational environment for Independent Component Analysis of omics data

Nicolas Captier
, Jane Merlevede
, Askhat Molkenov
, Ainur Seisenova
, Altynbek Zhubanchaliyev
, Petr V Nazarov
, Emmanuel Barillot
, Ulykbek Kairov
, Andrei Zinovyev

Institut National de la Santé et de la Recherche Médicale (INSERM)
Nazarbayev University
Luxembourg Institute of Health

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

We developed BIODICA, an integrated computational environment for application of independent component analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts.

Original language	English
Pages (from-to)	2963-2964
Number of pages	2
Journal	Bioinformatics
Volume	38
Issue number	10
Early online date	Apr 6 2022
DOIs	https://doi.org/10.1093/bioinformatics/btac204
Publication status	Published - May 15 2022

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abstract = "We developed BIODICA, an integrated computational environment for application of independent component analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts.",

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AU - Merlevede, Jane

AU - Molkenov, Askhat

AU - Seisenova, Ainur

AU - Zhubanchaliyev, Altynbek

AU - Nazarov, Petr V

AU - Barillot, Emmanuel

AU - Kairov, Ulykbek

AU - Zinovyev, Andrei

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AB - We developed BIODICA, an integrated computational environment for application of independent component analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts.

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BIODICA: a computational environment for Independent Component Analysis of omics data

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