BIODICA: a computational environment for Independent Component Analysis

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

Research output: Contribution to journalArticlepeer-review

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/.

Original languageEnglish
JournalBioinformatics
DOIs
Publication statusE-pub ahead of print - Apr 6 2022

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