Journal article
Topological features in cancer gene expression data
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, pp.108-119
2015
Handle:
https://hdl.handle.net/2376/114911
PMID: 25592573
Abstract
We present a new method for exploring cancer gene expression data based on tools from algebraic topology. Our method selects a small relevant subset from tens of thousands of genes while simultaneously identifying nontrivial higher order topological features, i.e., holes, in the data. We first circumvent the problem of high dimensionality by dualizing the data, i.e., by studying genes as points in the sample space. Then we select a small subset of the genes as landmarks to construct topological structures that capture persistent, i.e., topologically significant, features of the data set in its first homology group. Furthermore, we demonstrate that many members of these loops have been implicated for cancer biogenesis in scientific literature. We illustrate our method on five different data sets belonging to brain, breast, leukemia, and ovarian cancers.
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Details
- Title
- Topological features in cancer gene expression data
- Creators
- S Lockwood - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, U.S.A. svetlana.lockwood@email.wsu.eduB Krishnamoorthy
- Publication Details
- Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, pp.108-119
- Academic Unit
- Mathematics and Statistics, Department of; Paul G. Allen School for Global Animal Health
- Publisher
- United States
- Identifiers
- 99900548250701842
- Language
- English
- Resource Type
- Journal article