Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets

PLoS ONE 2(11): e1195, 2007. Published: 2007.11.20

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

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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 on 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.

Keywords: data set integration molecular subclass unsupervised clustering

Figure1

Supplemental Data

Description Link/Filename
Breast-A: data set Breast_A.gct
Breast-A: class labels Breast_A.cls
Breast-B: data set Breast_B.gct
Breast-B: class labels Breast_B.cls
Multi-A: data set Multi_A.gct
Multi-A: class labels Multi_A.cls
Multi-B: data set Multi_B.gct
Multi-B: class labels Multi_B.cls
DLBCL-A: data set DLBCL_A.gct
DLBCL-A: class labels DLBCL_A.cls
DLBCL-B: data set DLBCL_B.gct
DLBCL-B: class labels DLBCL_B.cls
DLBCL-C: data set DLBCL_C.gct
DLBCL-C: class labels DLBCL_C.cls
DLBCL-D: dara set DLBCL_D.gct
DLBCL-D: class labels DLBCL_D.cls
HCclustid (generates .cls files from a clustering result) HCclustid.zip