Ka Yee Yeung, David R.Haynor, Walter L.Ruzzo.
Validating clustering for gene expression data.
Bioinformatics, 2001 v 17 #4: 309-318.
[PDF][Web Site]
・クラスタリング法のマイクロアレイデータ解析における性能を比較・評価する。
・データ
1.Rat CNS[Wen]
2.Yeast cell cycle[Cho]
3.Human hematopoietic differentiation[Tamayo]
・クラスタリング法
1.CAST
2.Iterative Partition Algorithm
3.K-means
4.Random Clustering ： 性能比較のため
・評価法：FOM
・方法「Our idea is to apply a clustering algorithm to the data from (m-1) conditions, and to use the remaining condition to assess the predictive power of the clustering algorithm.」
・FOMとは「a possible definition of FOM is the average squared distance from the mean expression level in each cluster,」「The aggregate figure of merit, FOM, is an estimate of the total predictive power of the algorithm over all the conditions for k clusters in a data set.」
・結果「None of the three clustering algorithms is a clear winner in our results.」
・目的「Our main contribution is not the comparison of these specific algorithms and metrics, but rather the development of a simple, quantitative data-driven methodology allowing such comparisons to be made between any clustering algorithms and any similarity metrics.」
・わかったこと「we conclude that clustering results with low FOM's tend to have high correspondence to the functional categorization in Wen et al. on the rat CNS data set.」
・結果「No clustering algorithm emerged as a clear-cut winner in this work, and we suggest that flexibility, speed, reliability and ease of implementation may be equally important in differentiating clustering algorithms.」
・やはり(？)、方法比較の場合、なかなかすっきりした結果は出ない。どうしようもないことなのか？
・FOM → 要チェック
《チェック論文》
・Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation., Proc Natl Acad Sci U S A. 1999 Mar 16;96(6):2907-12.
Validating clustering for gene expression data.
Bioinformatics, 2001 v 17 #4: 309-318.
[PDF][Web Site]
・クラスタリング法のマイクロアレイデータ解析における性能を比較・評価する。
・データ
1.Rat CNS[Wen]
2.Yeast cell cycle[Cho]
3.Human hematopoietic differentiation[Tamayo]
・クラスタリング法
1.CAST
2.Iterative Partition Algorithm
3.K-means
4.Random Clustering ： 性能比較のため
・評価法：FOM
・方法「Our idea is to apply a clustering algorithm to the data from (m-1) conditions, and to use the remaining condition to assess the predictive power of the clustering algorithm.」
・FOMとは「a possible definition of FOM is the average squared distance from the mean expression level in each cluster,」「The aggregate figure of merit, FOM, is an estimate of the total predictive power of the algorithm over all the conditions for k clusters in a data set.」
・結果「None of the three clustering algorithms is a clear winner in our results.」
・目的「Our main contribution is not the comparison of these specific algorithms and metrics, but rather the development of a simple, quantitative data-driven methodology allowing such comparisons to be made between any clustering algorithms and any similarity metrics.」
・わかったこと「we conclude that clustering results with low FOM's tend to have high correspondence to the functional categorization in Wen et al. on the rat CNS data set.」
・結果「No clustering algorithm emerged as a clear-cut winner in this work, and we suggest that flexibility, speed, reliability and ease of implementation may be equally important in differentiating clustering algorithms.」
・やはり(？)、方法比較の場合、なかなかすっきりした結果は出ない。どうしようもないことなのか？
・FOM → 要チェック
《チェック論文》
・Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation., Proc Natl Acad Sci U S A. 1999 Mar 16;96(6):2907-12.
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