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Table 1 The first five principal components (PCs) of the data retain approximately 88% of the data variability

From: Autism risk classification using placental chorionic surface vascular network features

Boruta ranking Vascular features (variability captured) PC1 (35.27%) PC2 (22.57%) PC3 (17.20%) PC4 (7.79%) PC5 (5.80%)
1 MeanThickness − 0.1582 − 0.4747 0.1035 0.0651 − 0.0089
2 MeanTortuosity 0.0002 0.0575 0.5347 − 0.0979 0.0013
3 MurrayL1FitError − 0.256 − 0.3903 0.0438 0.0139 0.0397
4 StdThickness − 0.1566 − 0.4762 0.0701 − 0.0046 0.0196
5 StdDevTortuosity 0.0029 0.0812 0.5912 − 0.0641 0.1449
6 MaxTortuosity 0.0948 0.0724 0.5459 − 0.0264 0.1709
7 MeanAngle − 0.0611 0.0704 0.2028 0.2135 − 0.936
8 NumEndPoints 0.4251 − 0.0298 − 0.0132 0.0153 − 0.005
9 ArcLength 0.3773 − 0.1259 − 0.0035 − 0.0163 0.0116
10 NumBranchPoints 0.4254 − 0.0301 − 0.0125 0.0146 − 0.0038
11 MurrayBranchesUsed 0.4254 − 0.0301 − 0.0125 0.0146 − 0.0038
12 Volume 0.1444 − 0.4823 0.065 0.0502 − 0.0368
13 NumGenerations 0.3182 − 0.0237 0.014 0.2178 − 0.0619
14 MeanDistEndPointToPerim 0.0055 − 0.0323 0.0545 0.905 0.2124
15 VesselToDiscPercent 0.255 − 0.3502 0.0031 − 0.2561 − 0.1457
  1. The absolute value of the attributes within each PC gives a measure of contribution. The higher the value, the bigger the contribution. Specifically, NumEndPoints, NumBranchPoints, and MurrayBranchesUsed contributed the most to PC1, Thickness, StdThickness, and Volume contributed the most to PC2, MeanTortuosity, StdDevTortuosity, MaxTortuosity contributed the most to PC3, MeanDistEndPointToPerim contributed most to PC4, and MeanAngle contributed most to PC5