Correlating cellular features with gene expression using CCA

Vaishnavi Subramanian, Benjamin Chidester, Jian Ma, Minh N. Do

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

Abstract

To understand the biology of cancer, joint analysis of multiple data modalities, including imaging and genomics, is crucial. We propose the use of canonical correlation analysis (CCA) and a sparse variant as a preliminary discovery tool for identifying connections across modalities, specifically between gene expression and features describing cell and nucleus shape, texture, and stain intensity in histopathological images. Applied to 615 breast cancer samples from The Cancer Genome Atlas, CCA revealed significant correlation of several image features with expression of PAM50 genes, known to be linked to outcome, while Sparse CCA revealed associations with enrichment of pathways implicated in cancer without leveraging prior biological understanding. These findings affirm the utility of CCA for joint phenotype-genotype analysis of cancer.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages805-808
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Subramanian, V., Chidester, B., Ma, J., & Do, M. N. (2018). Correlating cellular features with gene expression using CCA. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 805-808). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363694