References

Seminal ICA articles

  • Bell, A.J.; Sejnowski, T.J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995, 7, 1129–1159. Link

  • Hyvärinen, A.; Oja, E. Independent component analysis: Algorithms and applications. Neural Netw. 2000, 13, 411–430. Link

  • Himberg, J.; Hyvarinen, A. Icasso: Software for investigating the reliability of ICA estimates by clustering and visualization. In Proceedings of the 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), Toulouse, France, 17–19 September 2003; pp. 259–268. Link

First studies applying ICA to gene expression data analysis

  • Liebermeister, W. Linear modes of gene expression determined by independent component analysis. Bioinformatics 2002, 18, 51-60. Link

  • Lee, S.-I.; Batzoglou, S. Application of independent component analysis to microarrays. Genome Biol. 2003, 4, R76. Link

  • Saidi, S.A.; Holland, C.M.; Kreil, D.P.;MacKay, D.J.C.; Charnock-Jones, D.S.; Print, C.G.; Smith, S.K. Independent component analysis of microarray data in the study of endometrial cancer. Oncogene 2004, 23, 6677-6683. Link

  • Teschendorff, A.E.; Journee, M.; Absil, P.A.; Sepulchre, R.; Caldas, C. Elucidating the altered transcriptional programs in breast cancer using independent component analysis. PLoS Comput. Biol. 2007, 3, e161. Link

Review articles

  • Kong, W.; Vanderburg, C.R.; Gunshin, H.; Rogers, J.T.; Huang, X. A review of independent component analysis application to microarray gene expression data. Biotechniques 2008, 45, 501–520. Link

  • Zinovyev, A.; Kairov, U.; Karpenyuk, T.; Ramanculov, E. Blind source separation methods for deconvolution of complex signals in cancer biology. Biochem. Biophys. Res. Commun. 2013, 430, 1182–1187. Link

  • Sompairac N, Nazarov PV, Czerwinska U, Cantini L, Biton A, Molkenov A, Zhumadilov Z, Barillot E, Radvanyi F, Gorban A, Kairov U, Zinovyev A. Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets. Int J Mol Sci. 2019 Sep 7;20(18):4414. doi: 10.3390/ijms20184414. Link

Comparison between ICA and other matrix factorization methods

  • Cantini, L.; Kairov, U.; de Reyniès, A.; Barillot, E.; Radvanyi, F.; Zinovyev, A. Assessing reproducibility of matrix factorization methods in independent transcriptomes. Bioinformatics 2019. Link

  • Way GP, Zietz M, Rubinetti V, Himmelstein DS, Greene CS. Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations. Genome Biol. 2020 May 11;21(1):109. Link

Meta-analysis of multiple datasets using ICA

  • Biton, A.; Bernard-Pierrot, I.; Lou, Y.; Krucker, C.; Chapeaublanc, E.; Rubio-Pérez, C.; López-Bigas, N.; Kamoun, A.; Neuzillet, Y.; Gestraud, P.; et al. Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypes. Cell Rep. 2014, 9, 1235–1245. Link

Choosing the right number of ICA components in omics studies

  • Kairov, U.; Cantini, L.; Greco, A.; Molkenov, A.; Czerwinska, U.; Barillot, E.; Zinovyev, A. Determining the optimal number of independent components for reproducible transcriptomic data analysis. BMC Genom. 2017, 18, 712. Link

  • Krumsiek, J.; Suhre, K.; Illig, T.; Adamski, J.; Theis, F.J. Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data. J. Proteome Res. 2012, 11, 4120–4131. Link

Annotation tools

  • Bonnet, E., Viara, E., Kuperstein, I., Calzone, L., Cohen, D. P. A., Barillot, E., and Zinovyev, A. (2015). NaviCell Web Service for network-based data visualization. Nucleic Acids Research, 43(W1), W560–W565.

  • Chen, J., Bardes, E. E., Aronow, B. J., and Jegga, A. G. (2009). ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Research,37(suppl2), W305−−W311.

  • Kairov, U., Karpenyuk, T., Ramanculov, E., and Zinovyev, A. (2012). Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures. Bioinformation, 8(16), 773–776. 23055628[pmid].

  • Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., and Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.

Applications to single cell data analysis

  • Aynaud MM, Mirabeau O, Gruel N, Grossetête S, Boeva V, Durand S, Surdez D, Saulnier O, Zaïdi S, Gribkova S, Fouché A, Kairov U, Raynal V, Tirode F, Grünewald TGP, Bohec M, Baulande S, Janoueix-Lerosey I, Vert JP, Barillot E, Delattre O, Zinovyev A. Transcriptional Programs Define Intratumoral Heterogeneity of Ewing Sarcoma at Single-Cell Resolution. Cell Rep. 2020 Feb 11;30(6):1767-1779.e6. Link

  • Kondratova M, Czerwinska U, Sompairac N, Amigorena SD, Soumelis V, Barillot E, Zinovyev A, Kuperstein I. A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures. Nat Commun. 2019 Oct 22;10(1):4808. Link

Applications of ICA to other layers than gene expression omics data and multiomics

  • Scherer M, Nazarov PV, Toth R, Sahay S, Kaoma T, Maurer V, Vedeneev N, Plass C, Lengauer T, Walter J, Lutsik P. Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz. Nat Protoc. 2020 Oct;15(10):3240-3263. Link

  • Meunier L, Hirsch TZ, Caruso S, Imbeaud S, Bayard Q, Roehrig A, Couchy G, Nault JC, Llovet JM, Blanc JF, Calderaro J, Zucman-Rossi J, Letouzé E. DNA Methylation Signatures Reveal the Diversity of Processes Remodeling Hepatocellular Carcinoma Methylomes. Hepatology. 2021 Aug;74(2):816-834. Link

  • Teschendorff, A.E.; Jing, H.; Paul, D.S.; Virta, J.; Nordhausen, K. Tensorial blind source separation for improved analysis of multi-omic data. Genome Biol. 2018, 19, 76. Link