A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

Abstract Background Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as...

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Bibliographic Details
Main Authors: Xiting Yan, Anqi Liang, Jose Gomez, Lauren Cohn, Hongyu Zhao, Geoffrey L. Chupp
Format: Article
Language:English
Published: BMC 2017-06-01
Series:BMC Bioinformatics
Subjects:
Data integration
Unsupervised clustering
Disease heterogeneity
Pathway-based distance
Online Access:http://link.springer.com/article/10.1186/s12859-017-1727-4

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http://link.springer.com/article/10.1186/s12859-017-1727-4

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