Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection
Table 3
Pearson correlation coefficient matrixes of rank lists by t-test, PLS, SVM, and RF in 2 cases based on all variables (A-B) and identified metabolites (C-D).
Method
tRanka
PLSRankb
RFRankc
SVMRankd
LDARanke
Pearson correlation coefficient matrix based on all variables
(A) Normal versus CRC
tRank
1.000
0.794f
0.575
0.327
0.342
PLSRank
0.794
1.000
0.574
0.328
0.342
RFRank
0.575
0.574
1.000
0.210
0.256
SVMRank
0.327
0.328
0.210
1.000
0.167
LDARank
0.342
0.342
0.256
0.167
1.000
(B) Pre versus post
tRank
1.000
0.232
0.217
0.021
0.032
PLSRank
0.232
1.000
0.652
0.066
0.066
RFRank
0.217
0.652
1.000
0.086
0.057
SVMRank
0.021
0.066
0.086
1.000
0.007
LDARank
0.032
0.066
0.057
0.007
1.000
Pearson correlation coefficient matrix based on identified metabolites
(C) Normal versus CRC
tRank
1.000
0.753
0.754
0.364
0.340
PLSRank
0.753
1.000
0.756
0.267
0.340
RFRank
0.754
0.756
1.000
0.495
0.308
SVMRank
0.364
0.267
0.495
1.000
0.190
LDARank
0.340
0.340
0.308
0.190
1.000
(D) Pre versus post
tRank
1.000
0.272
0.258
0.194
0.187
PLSRank
0.272
1.000
0.733
0.048
0.044
RFRank
0.258
0.733
1.000
0.034
0.041
SVMRank
0.194
0.048
0.034
1.000
0.187
LDARank
0.187
0.044
0.041
0.187
1.000
variable rank by the value of -test.
bvariable rank by the VIP value of PLS.
cvariable rank by the Gini value of RF.
dvariable rank by the SVM-REF.
evariable rank by the LDA coefficient.
fPearson correlation coefficient of PLS and -test variable rank lists for differentiating Normal and CRC.
