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

Bioinformatics. 2022 May 13;38(10):2963-2964. doi: 10.1093/bioinformatics/btac204.

Authors

Nicolas Captier^{1

2

3

4}, Jane Merlevede^{1

2

3}, Askhat Molkenov⁵, Ainur Seisenova⁵, Altynbek Zhubanchaliyev⁵, Petr V Nazarov⁶, Emmanuel Barillot^{1

2

3}, Ulykbek Kairov⁵, Andrei Zinovyev^{1

2

3}

Affiliations

¹ Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France.
² Institut Curie, PSL Research University, F-75005 Paris, France.
³ MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France.
⁴ Laboratoire d'Imagerie Translationnelle en Oncologie, Institut Curie, INSERM U1288, PSL Research University, 91400 Orsay, France.
⁵ National Laboratory Astana, Center for Life Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.
⁶ Multiomics Data Science Research Group, Department of Cancer Research & Bioinformatics Platform, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.

PMID: 35561190
DOI: 10.1093/bioinformatics/btac204

Abstract

Summary: 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.

Availability and implementation: BIODICA is implemented in Java, Python and JavaScript. The source code is freely available on GitHub under the MIT and the GNU LGPL licenses. BIODICA is supported on all major operating systems. URL: https://sysbio-curie.github.io/biodica-environment/.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computational Biology / methods
Metadata
Software*