Zhuo Fang, Jiong Yang, Yixue Li, Qingming Luo, Lei Liu
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics archive Volume 39 ,Issue 4(August 2006)Pages:401-411
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・従来の発現量数値データのみに基いて計算する数学的クラスタリング法に対して、Gene Ontology (GO)の手法により生物学的知識を取り入れたクラスタリング法を提案する。
・データ
1.Yeast, 2467 genes, 79 conditions [Eisen]
2.Yeast cell cycle, 6220 mRNA species, 17 time points [Cho]
・クラスタリング結果の評価は、アノテーションとの相関を示す関数(WF)を使用。
・問題点「
However, all of these algorithms only pay attention to mathmatical similarity of genes and conditions, while the biological meaning of clusters is still neglected.」
・GOとは「
gene ontology (GO), a large hierarchical vocabulary describing gene product functions in an organism-independent fashion,」
・処理「
There, firstly, a GO tree is constructed from GO data file. Subsequently, genes involved in the expression dataset are mapped to this GO tree via species related database, and unmapped nodes (terms) in GO tree are excluded. Thereafter, every node in this GO tree is checked from top to bottom.」
・問題点「
In most of the cases, it is hard to interpret the clustering results, because some genes in the same cluster might have no biological similarity at all.」
・長所「
Obviously, our clustering method will produce clusters with high similarities of both expression and function.」
・GOの考え方はサッパリわからず。
・計算の結果、クラスター数が数百とやたら細かく分かれているが、問題ないのか?