TY - JOUR
T1 - BIODICA
T2 - a computational environment for Independent Component Analysis of omics data
AU - Captier, Nicolas
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
N1 - Funding
The development of BIODICA was financially supported by the French government
under management of Agence Nationale de la Recherche as part of the
‘Investissements d’avenir’ program, reference ANR-19-P3IA-0001 (PRAIRIE
3IA Institute) and by the European Union’s Horizon 2020 program [826121,
iPC project]. This work was also a part of the TIPIT project (Towards an
Integrative approach for Precision ImmunoTherapy) funded by Fondation
ARC call «SIGN’IT 2020—Signatures in Immunotherapy» and the IMMUcan
project which has received funding from the Innovative Medicines Initiative 2
Joint Undertaking [821558]. The present study was supported by the research
grants of the Ministry of Education and Science of the Republic of Kazakhstan
[AP09058660], CRP NU [021220CRP222] ‘Identification of a long noncoding
RNA (lncRNA) and microRNA in ESCC’. P.V.N. was supported by the
Luxembourg National Research Fund [C17/BM/11664971/DEMICS].
Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press. All rights reserved.
PY - 2022/5/15
Y1 - 2022/5/15
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85132365270&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/35561190
U2 - 10.1093/bioinformatics/btac204
DO - 10.1093/bioinformatics/btac204
M3 - Article
C2 - 35385067
SN - 1367-4803
VL - 38
SP - 2963
EP - 2964
JO - Bioinformatics
JF - Bioinformatics
IS - 10
ER -