Discovering Distinct Patterns in Gene Expression Profiles
Full article [PDF] (1,99 MB)
doi:10.2390/biecoll-jib-2008-105
June 10, 2008
August 25, 2008
PubMed ID 20134072
Li Teng and Laiwan Chan
Correspondence should be addressed to:
Li Teng
Room 1013, HSB Engineering Building, The Chinese University of Hong Kong, NT, Hong Kong
kh.ude.khuc.esc@nullgnetl
Abstract
Traditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.