Comparisons of graph-structure clustering methods for gene expression data.
Although many numerical clustering algorithms have been applied to gene expression data analysis, the essential step is still biological interpretation by manual inspection. The correlation between genetic co-regulation and affiliation to a common biological process is what biologists expect. Here, we introduce some clustering algorithms that are based on graph structure constituted by biological knowledge. After applying a widely used dataset, we compared the result clusters of two of these algorithms in terms of the homogeneity of clusters and coherence of annotation and matching ratio. The results show that the clusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Cluster software, which contains the genes that are most expression correlative and most consistent with biological functions. Moreover, knowledge-guided analysis seems much more applicable than GO-Cluster in a larger dataset.
Fang
Zhuo
Z
Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology,Huazhong University of Science and Technology, Wuhan 430074, China.
Liu
Lei
L
Yang
Jiong
J
Luo
Qing-Ming
QM
Li
Yi-Xue
YX
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D012984:Software