Microarray sample clustering using independent component analysis

DNA microarray technology has been used to measure expression levels for thousands of genes in a single experiment, across different samples. These samples can be clustered into homogeneous groups corresponding to some particular macroscopic phenotypes. In sample clustering problems, it is common to come up against the challenges of high dimensional data due to small sample volume and high feature (gene) dimensionality. Therefore, it is necessary to conduct dimension reduction on the gene dimension and identify informative genes prior to the clustering on the samples. This paper introduces a method for informative genes selection by utilizing independent component analysis (ICA). The performance of the proposed method on various microarray datasets is reported to illustrate its effectiveness

[1]  D. Botstein,et al.  The transcriptional program in the response of human fibroblasts to serum. , 1999, Science.

[2]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Francisco Azuaje,et al.  Making genome expression data meaningful: prediction and discovery of classes of cancer through a connectionist learning approach , 2000, Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering.

[4]  Wolfram Liebermeister,et al.  Linear modes of gene expression determined by independent component analysis , 2002, Bioinform..

[5]  Richard M. Karp,et al.  CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts , 2001, ISMB.

[6]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[7]  A. Brazma,et al.  Gene expression data analysis , 2000, FEBS letters.

[8]  L. Penland,et al.  Use of a cDNA microarray to analyse gene expression patterns in human cancer , 1996, Nature Genetics.

[9]  J. J. Chen,et al.  Profiling expression patterns and isolating differentially expressed genes by cDNA microarray system with colorimetry detection. , 1998, Genomics.

[10]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[12]  Masato Inoue,et al.  BLIND GENE CLASSIFICATION BASED ON ICA OF MICROARRAY DATA , 2001 .

[13]  William A. Schmitt,et al.  Interactive exploration of microarray gene expression patterns in a reduced dimensional space. , 2002, Genome research.

[14]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[16]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.