| Title: |
Integrative cancer patient stratification via subspace merging |
| Authors: |
Ding, Hao; Sharpnack, Michael; Wang, Chao; Huang, Kun; Machiraju, Raghu |
| Contributors: |
Berger, Bonnie; National Cancer Institute; Leidos; Indiana University Precision Health Initiative; The National Library of Medicine |
| Source: |
Bioinformatics ; volume 35, issue 10, page 1653-1659 ; ISSN 1367-4803 1367-4811 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2018 |
| Description: |
Motivation Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease. Results Our proposed approach creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more informative clusters by preserving the complementary information from each level of omics data. We applied our approach to The Cancer Genome Atlas (TCGA) breast cancer dataset and show that by integrating gene expression, microRNA and DNA methylation data, our proposed method can produce clinically useful subtypes of breast cancer. We then investigate the molecular characteristics underlying these subtypes. We discover a highly expressed cluster of genes on chromosome 19p13 that strongly correlates with survival in TCGA breast cancer patients and validate these results in three additional breast cancer datasets. We also compare our approach with previous integrative clustering approaches and obtain comparable or superior results. Availability and implementation https://github.com/michaelsharpnack/GrassmannCluster Supplementary information Supplementary data are available at Bioinformatics online. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/bioinformatics/bty866 |
| Availability: |
https://doi.org/10.1093/bioinformatics/bty866; https://academic.oup.com/bioinformatics/article-pdf/35/10/1653/48970099/bioinformatics_35_10_1653.pdf |
| Rights: |
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
| Accession Number: |
edsbas.DB7BA3BF |
| Database: |
BASE |