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Application of Independent Component Analysis to Tumor Transcriptomes Reveals Specific and Reproducible Immune-Related Signals

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Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10891))

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.

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Acknowledgments

We thank Vassili Soumelis for discussions on multidimensionality of biological systems. This work has been funded by INSERM Plan Cancer \(\mathrm {N}\) BIO2014-08 COMET grant under ITMO Cancer BioSys program and by ITMO Cancer (AVIESAN) who provided 3-year PhD grant. We would like to acknowledge as well foundation Bettencourt Schueller and Center for Interdisciplinary Research funding for the training of the PhD student.

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Authors and Affiliations

  1. Institut Curie, INSERM U900, PSL Research University, Mines ParisTech, 26 rue d’Ulm, Paris, France

    Urszula Czerwinska, Laura Cantini, Emmanuel Barillot & Andrei Zinovyev

  2. Laboratory of Bioinformatics and Computational Systems Biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan

    Ulykbek Kairov

  3. Center for Interdisciplinary Research, Paris Descartes University, Paris, France

    Urszula Czerwinska

Authors
  1. Urszula Czerwinska
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  2. Laura Cantini
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  3. Ulykbek Kairov
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  4. Emmanuel Barillot
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  5. Andrei Zinovyev
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Corresponding author

Correspondence to Urszula Czerwinska .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, Toulouse, France

    Yannick Deville

  2. Bar-Ilan University, Ramat Gan, Israel

    Sharon Gannot

  3. University of Surrey, Guildford, United Kingdom

    Russell Mason

  4. University of Surrey, Guildford, United Kingdom

    Mark D. Plumbley

  5. University of Surrey, Guildford, United Kingdom

    Dominic Ward

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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: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_46

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  • DOI: https://doi.org/10.1007/978-3-319-93764-9_46

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-93764-9

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