Title :
A new clustering method for microarray data analysis
Author :
Zhang, Louxin ; Zhu, Song
Author_Institution :
Dept. of Math. & LIT, Nat. Univ. of Singapore, Singapore
Abstract :
A novel clustering approach is introduced to overcome missing data 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 a smooth score below a threshold after studying its computational complexity. The algorithm was tested intensively on random matrices and yeast data. It was shown to perform it well in finding co-regulation patterns in a test with the yeast data.
Keywords :
DNA; arrays; biology computing; computational complexity; data analysis; genetics; molecular biophysics; pattern clustering; average expression level; clustering method; co-regulation patterns; computational complexity; efficient greedy algorithm; gene expression levels; microarray data analysis; random matrices; smooth score; yeast data; Algorithm design and analysis; Chromium; Clustering algorithms; Clustering methods; Data analysis; Fungi; Gene expression; Partitioning algorithms; Self organizing feature maps; Testing;
Conference_Titel :
Bioinformatics Conference, 2002. Proceedings. IEEE Computer Society
Print_ISBN :
0-7695-1653-X
DOI :
10.1109/CSB.2002.1039349
