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
Volume2018-April
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

Other

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

Fingerprint

Gene expression
Genes
Gene Expression
Cell Nucleus Shape
Neoplasms
Textures
Imaging techniques
Atlases
Genomics
Coloring Agents
Joints
Genotype
Genome
Breast Neoplasms
Phenotype

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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 (Vol. 2018-April, pp. 805-808). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363694

Correlating cellular features with gene expression using CCA. / Subramanian, Vaishnavi; Chidester, Benjamin; Ma, Jian; Do, Minh N.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. p. 805-808.

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

Subramanian, V, Chidester, B, Ma, J & Do, MN 2018, Correlating cellular features with gene expression using CCA. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. vol. 2018-April, IEEE Computer Society, pp. 805-808, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/4/18. https://doi.org/10.1109/ISBI.2018.8363694
Subramanian V, Chidester B, Ma J, Do MN. Correlating cellular features with gene expression using CCA. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April. IEEE Computer Society. 2018. p. 805-808 https://doi.org/10.1109/ISBI.2018.8363694
Subramanian, Vaishnavi ; Chidester, Benjamin ; Ma, Jian ; Do, Minh N. / Correlating cellular features with gene expression using CCA. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. pp. 805-808
@inproceedings{d49109c5551745c19f2de2bb61d8644b,
title = "Correlating cellular features with gene expression using CCA",
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.",
author = "Vaishnavi Subramanian and Benjamin Chidester and Jian Ma and Do, {Minh N}",
year = "2018",
month = "5",
day = "23",
doi = "10.1109/ISBI.2018.8363694",
language = "English (US)",
volume = "2018-April",
pages = "805--808",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Correlating cellular features with gene expression using CCA

AU - Subramanian, Vaishnavi

AU - Chidester, Benjamin

AU - Ma, Jian

AU - Do, Minh N

PY - 2018/5/23

Y1 - 2018/5/23

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85048073411&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048073411&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2018.8363694

DO - 10.1109/ISBI.2018.8363694

M3 - Conference contribution

VL - 2018-April

SP - 805

EP - 808

BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018

PB - IEEE Computer Society

ER -