Application of independent component analysis to tumor transcriptomes reveals specific and reproducible immune-related signals

Urszula Czerwinska, Laura Cantini, Ulykbek Kairov, Emmanuel Barillot, Andrei Zinovyev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Independent Component Analysis (ICA) can be used to model gene expression data as an action of a set of statistically independent hidden factors. The ICA analysis with a downstream component analysis was successfully applied to transcriptomic data previously in order to decompose bulk transcriptomic data into interpretable hidden factors. Some of these factors reflect the presence of an immune infiltrate in the tumor environment. However, no foremost studies focused on reproducibility of the ICA-based immune-related signal in the tumor transcriptome. In this work, we use ICA to detect immune signals in six independent transcriptomic datasets. We observe several strongly reproducible immune-related signals when ICA is applied in sufficiently high-dimensional space (close to one hundred). Interestingly, we can interpret these signals as cell-type specific signals reflecting a presence of T-cells, B-cells and myeloid cells, which are of high interest in the field of oncoimmunology. Further quantification of these signals in tumoral transcriptomes has a therapeutic potential.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 14th International Conference, LVA/ICA 2018, Proceedings
PublisherSpringer Verlag
Pages501-513
Number of pages13
ISBN (Print)9783319937632
DOIs
Publication statusPublished - Jan 1 2018
Event14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018 - Guildford, United Kingdom
Duration: Jul 2 2018Jul 5 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10891 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018
CountryUnited Kingdom
CityGuildford
Period7/2/187/5/18

Keywords

  • Blind source separation
  • Cancer
  • Genomic data analysis
  • Immunology
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Czerwinska, U., Cantini, L., Kairov, U., Barillot, E., & Zinovyev, A. (2018). Application of independent component analysis to tumor transcriptomes reveals specific and reproducible immune-related signals. In Latent Variable Analysis and Signal Separation - 14th International Conference, LVA/ICA 2018, Proceedings (pp. 501-513). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10891 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93764-9_46