Table 2.
Subset size1 | Filter2 | NSNPs3 | PDF4 | medianSel5 | IQRSel6 | NOG7 |
---|---|---|---|---|---|---|
50 | mrmr | 499 | 0.30 | 50 | 0 | 0.53 |
cforest | 454 | 0.89 | 46 | 5 | 0.03 | |
spearcor | 441 | 0.20 | 44 | 3 | 0.69 | |
univ.dtree | 151 | 0.99 | 24 | 10 | 0.00 | |
random | 189 | 0.99 | 23 | 12 | 0.00 | |
250 | mrmr | 2,096 | 0.28 | 209 | 8 | 0.53 |
cforest | 1,693 | 0.79 | 172 | 13 | 0.05 | |
spearcor | 1,575 | 0.28 | 158 | 12 | 0.54 | |
univ.dtree | 804 | 0.90 | 83 | 18 | 0.02 | |
random | 740 | 0.92 | 80 | 48 | 0.01 | |
500 | mrmr | 3,420 | 0.29 | 347 | 23 | 0.52 |
cforest | 2,287 | 0.72 | 239 | 39 | 0.07 | |
spearcor | 2,560 | 0.28 | 260 | 9 | 0.52 | |
univ.dtree | 1,292 | 0.86 | 141 | 40 | 0.02 | |
random | 1,332 | 0.86 | 151 | 63 | 0.02 | |
750 | mrmr | 4,333 | 0.28 | 437 | 24 | 0.53 |
cforest | 2,646 | 0.67 | 275 | 35 | 0.09 | |
spearcor | 3,339 | 0.27 | 338 | 21 | 0.53 | |
univ.dtree | 1,667 | 0.79 | 165 | 40 | 0.04 | |
random | 1,745 | 0.80 | 160 | 57 | 0.03 | |
1,000 | mrmr | 5,125 | 0.27 | 515 | 23 | 0.54 |
cforest | 3,008 | 0.63 | 321 | 53 | 0.10 | |
spearcor | 3,978 | 0.27 | 394 | 15 | 0.53 | |
univ.dtree | 1,750 | 0.74 | 193 | 21 | 0.06 | |
random | 1,879 | 0.76 | 192 | 62 | 0.05 | |
1,500 | mrmr | 5,978 | 0.26 | 595 | 21 | 0.55 |
cforest | 3,478 | 0.56 | 341 | 8 | 0.14 | |
spearcor | 5,096 | 0.27 | 506 | 12 | 0.54 | |
univ.dtree | 2,270 | 0.66 | 235 | 19 | 0.09 | |
random | 2,200 | 0.68 | 235 | 80 | 0.08 | |
9,523 | none | 2,963 | 0.27 | 269 | 111 | 0.56 |
Subset size = number of selected features.
Filter method = Maximum relevance minimum redundancy (mrmr); Random forest (cforest); Spearman’s correlation (spearcor); Univariate decision tree (univ.dtree); Random selection (random).
NSNPs = Total number of SNPs pre-selected in the 10 subsets.
PDF = Proportion of distinct features in the 10 subsets.
medianSel = Mean number of selected SNPS in the 10 subsets.
IQRSel = Interquartile range of the number of selected SNPS in the 10 subsets.
NOG = Nogueira et al. (2018) stability estimator.