Novel algorithm for coexpression detection in time-varying microarray data sets.
When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new dataset. Therefore, this study proposes a novel scheme, designated as the Variation-based Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three real-world microarray datasets are employed to evaluate the performance of the proposed algorithm.
Yin
Zong-Xian
ZX
Chiang
Jung-Hsien
JH
D000465:Algorithms; D000818:Animals; D016845:Blastocystis hominis / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D019073:HT29 Cells; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015800:Protozoan Proteins / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D012568:Schizosaccharomyces / Q000235:genetics; D029702:Schizosaccharomyces pombe Proteins / Q000235:genetics
D015800:Protozoan Proteins; D029701:Saccharomyces cerevisiae Proteins; D029702:Schizosaccharomyces pombe Proteins