Name
Title | ||
|---|---|---|
Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis |
Abstract | ||
|---|---|---|
In microarray data analysis, each gene expression sample has thousands of genes and reducing such high dimensionality is useful for both visualization and further clustering of samples. Traditional principal component analysis (PCA) is a commonly used method which has problems. Nonnegative Matrix Factorization (NMF) is a new dimension reduction method. In this paper we compare NMF and PCA for dimension reduction. The reduced data is used for visualization, and clustering analysis via k-means on 11 real gene expression datasets. Before the clustering analysis, we apply NMF and PCA for reduction in visualization. The results on one leukemia dataset show that NMF can discover natural clusters and clearly detect one mislabeled sample while PCA cannot. For clustering analysis via k-means, NMF most typically outperforms PCA. Our results demonstrate the superiority of NMF over PCA in reducing microarray data. (C) 2007 Elsevier Inc. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.jbi.2007.12.003 | JOURNAL OF BIOMEDICAL INFORMATICS |
Keywords | Field | DocType |
microarray data,Nonnegative Matrix Factorization,principal component analysis,visualization,clustering analysis | Data mining,Clustering high-dimensional data,Dimensionality reduction,Pattern recognition,Visualization,Computer science,Curse of dimensionality,Microarray analysis techniques,Artificial intelligence,Non-negative matrix factorization,Cluster analysis,Principal component analysis | Journal |
Volume | Issue | ISSN |
41 | 4 | 1532-0464 |
Citations | PageRank | References |
16 | 0.74 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weixiang Liu | 1 | 105 | 12.26 |
| Yuan Kehong | 2 | 52 | 8.00 |
| Ye Datian | 3 | 41 | 10.06 |