A new clustering method for microarray data analysis.

Louxin Zhang; Song Zhu

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PubMed · 15838143

A new clustering method for microarray data analysis.

Louxin Zhang·Song Zhu

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Abstract

A novel clustering approach is introduced to overcome data missing and inconsistency of gene expression levels under different conditions in the stage of clustering. It is based on the so-called smooth score, which is defined for measuring the deviation of the expression level of a gene and the average expression level of all the genes involved under a condition. We present an efficient greedy algorithm for finding clusters with smooth score below a threshold after studying its computational complexity. The algorithm was tested intensively on random matrixes and a yeast data. It was shown to perform well in finding co-regulation patterns in a test with the yeast data.

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Algorithms Cluster Analysis Gene Expression Profiling Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Saccharomyces cerevisiae Proteins Sequence Alignment Sequence Analysis, DNA Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Louxin Zhang, Song Zhu. 2002. A new clustering method for microarray data analysis.. https://pubmed.ncbi.nlm.nih.gov/15838143/

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