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Table 2 Performance comparison of different integrative methods on lung and kidney cancer datasets produced by TCGA

From: Integrating heterogeneous genomic data to accurately identify disease subtypes

Lung Methylation mRNA miRNA Concatenation SNF iBFE
PCCintraclass 0.20 0.10 0.11 0.15 0.12 0.65
PCCinterclass −0.13 −0.05 −0.06 −0.10 −0.06 −0.39
PCCintraclass-PCCinterclass 0.33 0.15 0.17 0.25 0.17 1.04
Distintraclass 160.41 142.37 22.57 218.65 0.02 2.81
Distinterclass 239.13 157.70 26.40 287.85 0.02 6.29
Distinterclass/Distintraclass 1.49 1.11 1.17 1.32 1.18 2.24
ACC_rfLOO 0.99 0.97 0.97 1.00 0.98 1.00
Kidney
PCCintraclass 0.10 0.15 0.11 0.07 0.16 0.37
PCCinterclass −0.05 −0.07 −0.05 −0.04 −0.12 −0.19
PCCintraclass-PCCinterclass 0.15 0.22 0.16 0.11 0.27 0.56
Distintraclass 207.37 164.80 22.90 277.16 0.02 3.48
Distinterclass 226.39 193.05 25.64 295.57 0.02 4.97
Distinterclass/Distintraclass 1.09 1.17 1.12 1.07 1.52 1.43
ACC_rfLOO 0.98 0.98 0.96 0.93 1.00 0.95
  1. The best performer was highlighted with the darkest color
  2. PCCintraclass: average Pearson correlation coefficients of patients within the same classes; PCCinterclass: average Pearson correlation coefficients of patients from different classes; Distintraclass: average Euclidean distance of patients within the same classes;Distinterclass:average Euclidean distance of patients from different classes;ACC_rfLOO: accuracy of leave-one-out cross-validation by random forest based on the clustering labels by k-means