Biclustering of Microarray Data Based on Singular Value Decomposition

Biclustering via Sparse Singular Value Decomposition

“…See Busygin et al (2008) for a survey on these methods. The hierarchical clustering of Yang et al (2007) is more akin to SSVD, which involves first extracting the few leading singular vectors, before applying hierarchical clustering separately to the left and right singular vectors. Note that the sparsity is not incorporated when the singular vectors are obtained, and clustering is not performed together on the samples and the variables.…”

“…Several other biclustering methods are also based on SVD, such as the coclustering algorithm of Dhillon, Mallela, and Modha (2003); the spectral method of Kluger et al (2003); the double conjugated clustering by Busygin, Prokopyev, and Pardalos (2005); and the hierarchical clustering of Yang, Dai, and Yan (2007). See Busygin et al (2008) for a survey on these methods.…”

Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

“…In recent years many of these methods have been proposed to compare gene expression levels in samples drawn, in general, from two different conditions [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Table (1) shows a comprehensive summary of existing microarray clustering and classification methods.…”

“…Experiments showed misclassification errors depend on the number of iteration levels. Improved accuracy was achieved using a Biclustering algorithm [10] to identify local structures from gene expression dataset based on Singular Value Decomposition (SVD). The main limitation of all these methods is their dependence on the correct choice of the threshold level parameter that is used in the clustering estimation.…”