Clustering of Transcriptomic Data for the Identification of Cancer Subtypes
Abstract
Cancer is a number of related yet highly heterogeneous diseases. Correct identification of cancer subtypes is critical for clinical decisions. The advance in sequencing technologies has made it possible to study cancer based on abundant genomics and transcriptomic (-omics) data. Such a data-driven approach is expected to address limitations and issues with traditional methods in identifying cancer subtypes. We evaluate the suitability of clustering--a data mining tool to study heterogenous data when there is a lack of sufficient understanding of the subject matters--in the identification of cancer subtypes. A number of popular clustering algorithms and their consensus are explored, and we find cancer subtypes identified by consensus clustering agree well with clinical studies.
Keywords
Cite
@article{arxiv.1811.09926,
title = {Clustering of Transcriptomic Data for the Identification of Cancer Subtypes},
author = {Xiaochun Chen and Honggang Wang and Donghui Yan},
journal= {arXiv preprint arXiv:1811.09926},
year = {2018}
}
Comments
10 pages, 3 figures. The 4th International Conference on Fuzzy Systems and Data Mining, 2018