Subclass mapping: Identifying common subtypes in independent disease data sets

Yujin Hoshida, Jean Philippe Brunet, Pablo Tamayo, Todd R. Golub, Jill P. Mesirov

Research output: Contribution to journalArticlepeer-review

383 Scopus citations

Abstract

Whole genome expression profiles are widely used to discover molecular subtypes of diseases. A remaining challenge is to identify the correspondence or commonality of subtypes found in multiple, independent data sets generated on various platforms. While model-based supervised learning is often used to make these connections, the models can be biased to the training data set and thus miss inherent, relevant substructure in the test data. Here we describe an unsupervised subclass mapping method (SubMap), which reveals common subtypes between independent data sets. The subtypes within a data set can be determined by unsupervised clustering or given by predetermined phenotypes before applying SubMap. We define a measure of correspondence for subtypes and evaluate its significance building an our previous work on gene set enrichment analysis. The strength of the SubMap method is that it does not impose the structure of one data set upon another, but rather uses a bi-directional approach to highlight the common substructures in both. We show how this method can reveal the correspondence between several cancer-related data sets. Notably, it identifies common subtypes of breast cancer associated with estrogen receptor status, and a subgroup of lymphoma patients who share similar survival patterns, thus improving the accuracy of a clinical outcome predictor.

Original languageEnglish (US)
Article numbere1195
JournalPloS one
Volume2
Issue number11
DOIs
StatePublished - Nov 21 2007
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • General

Fingerprint

Dive into the research topics of 'Subclass mapping: Identifying common subtypes in independent disease data sets'. Together they form a unique fingerprint.

Cite this