Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data.
Int J Data Min Bioinform
; 2(3): 236-49, 2008.
Article
in En
| MEDLINE
| ID: mdl-19024496
ABSTRACT
Nonnegative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis as it reduces thousands of genes to a few compact metagenes, especially in clustering gene expression samples for cancer class discovery. Enhancing sparseness of the factorisation can find only a few dominantly coexpressed metagenes and improve the clustering effectiveness. Sparse p-norm (p > 1) Nonnegative Matrix Factorization (Sp-NMF) is a more sparse representation method using high order norm to normalise the decomposed components. In this paper, we investigate the benefit of high order normalisation for clustering cancer-related gene expression samples. Experimental results demonstrate that Sp-NMF leads to robust and effective clustering in both automatically determining the cluster number, and achieving high accuracy.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Signal Transduction
/
Biomarkers, Tumor
/
Multigene Family
/
Proteome
/
Gene Expression Profiling
/
Neoplasm Proteins
/
Neoplasms
Limits:
Animals
/
Humans
Language:
En
Journal:
Int J Data Min Bioinform
Journal subject:
INFORMATICA MEDICA
Year:
2008
Document type:
Article
Affiliation country:
China