Ssignment of Acute Myelogenous Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL)Kmeans (K) ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL_R_B.cell ALL_R_B.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ Error Count L L L L L L L L L L L L L L L L L L L L L L L L L L L L L M M L L L M L L M SVD (K) B T M B B B M T M T B M M B M T M M B T T T T M T T M M M M T B B M T B M T L L L L L L L L L L L L L L L L L L L L M M M L M L M L L L L L L L L L L LPCA (K) M B M B M M M M B B M 
M M B M B M M B B B T T T B B B M B M M M M M M M M M L L L L L M L L L L L L L L L L M L L L L L L L L L L M L M M M M M M M M MICA (K) B B B B B B B B B B B B B B B B B B B B B T T T B B T M B M M M M M M M M M L L L L L M L L L L L L L L L L M L L L L L L L L L L M M M M M M M M M M MNMF (K) B B B B B M B B B T B B B B B B M B B T T T T T T T T M M M M M M M M M M MSNMF (K) B B B B B B B B B T B B B B B B B B B T T T T T T T T M B M M M M M M M M MBSNMF (K) B B B B B B B B B B B B B B B B B B B T T T T T T T T M B M M M M M M M M MVoting (K) B B B B B B B B B B B B B B B B B B B T T T T T T 
T T M B M M M M M M M M M(K)(K) M L M M M M L L M L M M L M L L L L M L L L L L L L L L L L L M M L L M L L(K)(K)(K)(K) L L L L L L L L L L L L L L L L L L L L L L L L L L L M L M M M M M M M M M(K) L L L L L L L L L L L L L L L L L L L L L L L L L L L M L M M M M M M M M M(K) L L L L L L L L L L L L L L L L L L L L L L L L L L L M L M M M M M M M M MB B B B B B B B B B B B B B B B B B B T T T T T T T T B B M M B B B M B B MClass Assignment of Acute Myelogenous Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) at K and K. SVD: singular worth decomposition, PCA: principal component evaluation, ICA: independent element analysis, NMF: non-negative matrix factorization, SNMF: sparse non-negative matrix factorization, BSNMF: bi-directional non-negative matrix factorization, Voting: Voting class L: ALL, M: AML, B: ALL_B cell, T: ALL_T cell Bold-faced: misclassified samplesevaluation study for the solutions has not been reported. For that reason, we evaluated orthogonal (i.e. PCA, SVD), non-orthogonal (i.e. ICA, NMF and SNMF) MFs as well as a standard clustering algorithm (i.e. K-means) making use of seven clustering-quality (i.e. homogeneity, 4-IBP site separation, Dunn index, typical silhouette width, Pearson correlation of cophenetic distance, dl-Alprenolol hydrochloride Hubert correlation of cophenetic distance and also the GAP statistic) and two prediction-accuracy measures (i.e. the adjusted Rand index and prediction accuracy) applying to five published datasets. We also included an enhancing non-orthogonal MFs, BSNMF within the evaluation study. As a result, we observed that clustering top quality and prediction-accuracy indices applying non-orthogonalMFs are far better than those of orthogonal MFs and Kmeans. In respect to outcomes from Homogeneity, separation, Dunn index, typical silhouette width and Hubert correlation of cophenetic distance, non-orthogonal MFs had greater worth than these of orthogonal MFs and Kmeans. The GAP statistic was reduced PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19387489?dopt=Abstract for non-orthogonal MFs than for orthogonal MFs and K-means. When we tested predictive accuracy for the three datasets with identified class labels, we also observed greater overall performance for non-orthogonal MFs than for the rest. We also investig.Ssignment of Acute Myelogenous Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL)Kmeans (K) ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL__B.cell ALL_R_B.cell ALL_R_B.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell ALL__T.cell AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ AML_ Error Count L L L L L L L L L L L L L L L L L L L L L L L L L L L L L M M L L L M L L M SVD (K) B T M B B B M T M T B M M B M T M M B T T T T M T T M M M M T B B M T B M T L L L L L L L L L L L L L L L L L L L L M M M L M L M L L L L L L L L L L LPCA (K) M B M B M M M M B B M M M B M B M M B B B T T T B B B M B M M M M M M M M M L L L L L M L L L L L L L L L L M L L L L L L L L L L M L M M M M M M M M MICA (K) B B B B B B B B B B B B B B B B B B B B B T T T B B T M B M M M M M M M M M L L L L L M L L L L L L L L L L M L L L L L L L L L L M M M M M M M M M M MNMF (K) B B B B B M B B B T B B B B B B M B B T T T T T T T T M M M M M M M M M M MSNMF (K) B B B B B B B B B T B B B B B B B B B T T T T T T T T M B M M M M M M M M MBSNMF (K) B B B B B B B B B B B B B B B B B B B T T T T T T T T M B M M M M M M M M MVoting (K) B B B B B B B B B B B B B B B B B B B T T T T T T T T M B M M M M M M M M M(K)(K) M L M M M M L L M L M M L M L L L L M L L L L L L L L L L L L M M L L M L L(K)(K)(K)(K) L L L L L L L L L L L L L L L L L L L L L L L L L L L M L M M M M M M M M M(K) L L L L L L L L L L L L L L L L L L L L L L L L L L L M L M M M M M M M M M(K) L L L L L L L L L L L L L L L L L L L L L L L L L L L M L M M M M M M M M MB B B B B B B B B B B B B B B B B B B T T T T T T T T B B M M B B B M B B MClass Assignment of Acute Myelogenous Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) at K and K. SVD: singular value decomposition, PCA: principal component evaluation, ICA: independent component evaluation, NMF: non-negative matrix factorization, SNMF: sparse non-negative matrix factorization, BSNMF: bi-directional non-negative matrix factorization, Voting: Voting class L: ALL, M: AML, B: ALL_B cell, T: ALL_T cell Bold-faced: misclassified samplesevaluation study for the solutions has not been reported. Consequently, we evaluated orthogonal (i.e. PCA, SVD), non-orthogonal (i.e. ICA, NMF and SNMF) MFs along with a regular clustering algorithm (i.e. K-means) employing seven clustering-quality (i.e. homogeneity, separation, Dunn index, typical silhouette width, Pearson correlation of cophenetic distance, Hubert correlation of cophenetic distance along with the GAP statistic) and two prediction-accuracy measures (i.e. the adjusted Rand index and prediction accuracy) applying to 5 published datasets. We also incorporated an enhancing non-orthogonal MFs, BSNMF within the evaluation study. Consequently, we observed that clustering quality and prediction-accuracy indices applying non-orthogonalMFs are far better than those of orthogonal MFs and Kmeans. In respect to final results from Homogeneity, separation, Dunn index, typical silhouette width and Hubert correlation of cophenetic distance, non-orthogonal MFs had larger worth than those of orthogonal MFs and Kmeans. The GAP statistic was reduce PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19387489?dopt=Abstract for non-orthogonal MFs than for orthogonal MFs and K-means. When we tested predictive accuracy for the three datasets with identified class labels, we also observed greater efficiency for non-orthogonal MFs than for the rest. We also investig.