SPARCoC: a new framework for molecular pattern discovery and cancer gene identification

PloS One
Shiqian MaXiuzhen Huang

Abstract

It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung adenocarcinoma (ADCA), an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05). Our results are only based on gene expression profiling data analysis, without incorporating any other...Continue Reading

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Datasets Mentioned

BETA
GSE5843

Methods Mentioned

BETA
exome
protein array
surgical resection
PCA

Software Mentioned

PAM
Hclust
MATLAB
- CoClust
QUIBC
LAS
ConcensusClusterPlus
Sparse
iCluster
SPARCoC

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