Table 2 Simulation results using sparse mCIA are shown. Sensitivity (Sens), Specificity (Spec), and Matthew’s correlation coefficient (MCC) for feature selection performance and Angle for estimation performance are calculated. 5-fold cross validation is used to choose the best tuning parameter combination in each method. Values within parenthesis are standard errors
From: Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data
sparse multiple CIA | mCIA | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Â | a1 | a2 | a3 | a1 | a2 | a3 | |||||||||
scen | Sens | Spec | MCC | Angle | Sens | Spec | MCC | Angle | Sens | Spec | MCC | Angle | Angle | Angle | Angle |
1 | 0.675 | 0.991 | 0.754 | 0.885 | 0.74 | 0.991 | 0.803 | 0.901 | 0.77 | 0.991 | 0.82 | 0.905 | 0.882 | 0.847 | 0.830 |
| Â | (0.285) | (0.018) | (0.161) | (0.081) | (0.205) | (0.014) | (0.102) | (0.052) | (0.155) | (0.012) | (0.071) | (0.037) | (0.025) | (0.028) | (0.025) |
2 | 0.754 | 0.974 | 0.781 | 0.901 | 0.759 | 0.966 | 0.762 | 0.886 | 0.755 | 0.96 | 0.743 | 0.875 | 0.879 | 0.847 | 0.833 |
| Â | (0.130) | (0.032) | (0.058) | (0.028) | (0.089) | (0.027) | (0.046) | (0.024) | (0.071) | (0.022) | (0.041) | (0.021) | (0.024) | (0.027) | (0.023) |
3 | 0.711 | 0.996 | 0.794 | 0.904 | 0.776 | 0.996 | 0.846 | 0.924 | 0.813 | 0.996 | 0.87 | 0.933 | 0.933 | 0.915 | 0.897 |
| Â | (0.316) | (0.012) | (0.200) | (0.095) | (0.231) | (0.009) | (0.134) | (0.066) | (0.177) | (0.007) | (0.096) | (0.047) | (0.011) | (0.011) | (0.015) |
4 | 0.826 | 0.982 | 0.848 | 0.937 | 0.846 | 0.981 | 0.857 | 0.936 | 0.845 | 0.977 | 0.845 | 0.928 | 0.933 | 0.915 | 0.897 |
| Â | (0.145) | (0.029) | (0.069) | (0.033) | (0.100) | (0.022) | (0.040) | (0.020) | (0.077) | (0.020) | (0.040) | (0.018) | (0.011) | (0.011) | (0.015) |
5 | 0.771 | 0.986 | 0.816 | 0.908 | 0.763 | 0.989 | 0.812 | 0.902 | 0.764 | 0.991 | 0.819 | 0.903 | 0.882 | 0.847 | 0.83 |
| Â | (0.162) | (0.020) | (0.079) | (0.042) | (0.159) | (0.015) | (0.077) | (0.040) | (0.157) | (0.012) | (0.074) | (0.039) | (0.024) | (0.028) | (0.025) |
6 | 0.812 | 0.93 | 0.757 | 0.897 | 0.783 | 0.944 | 0.742 | 0.879 | 0.767 | 0.954 | 0.738 | 0.871 | 0.883 | 0.85 | 0.825 |
| Â | (0.081) | (0.042) | (0.046) | (0.023) | (0.078) | (0.031) | (0.049) | (0.023) | (0.078) | (0.024) | (0.051) | (0.027) | (0.023) | (0.025) | (0.03) |
7 | 0.839 | 0.99 | 0.873 | 0.941 | 0.836 | 0.993 | 0.875 | 0.938 | 0.837 | 0.994 | 0.878 | 0.939 | 0.933 | 0.912 | 0.9 |
| Â | (0.161) | (0.017) | (0.087) | (0.043) | (0.159) | (0.013) | (0.083) | (0.041) | (0.160) | (0.010) | (0.082) | (0.042) | (0.011) | (0.014) | (0.013) |
8 | 0.88 | 0.959 | 0.851 | 0.942 | 0.865 | 0.968 | 0.847 | 0.933 | 0.863 | 0.975 | 0.854 | 0.933 | 0.933 | 0.913 | 0.899 |
| Â | (0.077) | (0.039) | (0.044) | (0.017) | (0.076) | (0.026) | (0.040) | (0.017) | (0.071) | (0.021) | (0.036) | (0.015) | (0.011) | (0.014) | (0.013) |