Table 3 Simulation results using structured sparse mCIA are shown. Sensitivity (Sens), Specificity (Spec), and Matthews 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
structured sparse multiple CIA | mCIA | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Â | a1 | a2 | a3 | a1 | a2 | a3 | |||||||||
scenario | Sens | Spec | MCC | Angle | Sens | Spec | MCC | Angle | Sens | Spec | MCC | Angle | Angle | Angle | Angle |
1 | 0.71 | 0.994 | 0.786 | 0.897 | 0.767 | 0.993 | 0.827 | 0.913 | 0.79 | 0.992 | 0.837 | 0.915 | 0.882 | 0.847 | 0.830 |
| Â | (0.284) | (0.011) | (0.166) | (0.088) | (0.204) | (0.009) | (0.106) | (0.056) | (0.154) | (0.008) | (0.073) | (0.041) | (0.025) | (0.028) | (0.025) |
2 | 0.79 | 0.979 | 0.814 | 0.918 | 0.787 | 0.97 | 0.789 | 0.901 | 0.774 | 0.962 | 0.761 | 0.885 | 0.879 | 0.847 | 0.833 |
| Â | (0.127) | (0.021) | (0.058) | (0.030) | (0.089) | (0.018) | (0.046) | (0.024) | (0.068) | (0.016) | (0.041) | (0.022) | (0.024) | (0.027) | (0.023) |
3 | 0.748 | 0.995 | 0.816 | 0.915 | 0.807 | 0.996 | 0.863 | 0.934 | 0.838 | 0.996 | 0.884 | 0.941 | 0.933 | 0.915 | 0.897 |
| Â | (0.300) | (0.010) | (0.186) | (0.092) | (0.221) | (0.008) | (0.126) | (0.064) | (0.171) | (0.006) | (0.091) | (0.047) | (0.011) | (0.011) | (0.015) |
4 | 0.854 | 0.987 | 0.875 | 0.947 | 0.867 | 0.984 | 0.877 | 0.945 | 0.862 | 0.979 | 0.861 | 0.937 | 0.933 | 0.915 | 0.897 |
| Â | (0.142) | (0.016) | (0.072) | (0.034) | (0.097) | (0.014) | (0.042) | (0.021) | (0.074) | (0.013) | (0.038) | (0.018) | (0.011) | (0.011) | (0.015) |
5 | 0.798 | 0.986 | 0.833 | 0.919 | 0.791 | 0.989 | 0.831 | 0.913 | 0.793 | 0.992 | 0.838 | 0.915 | 0.882 | 0.847 | 0.83 |
| Â | (0.162) | (0.016) | (0.075) | (0.042) | (0.162) | (0.012) | (0.076) | (0.043) | (0.160) | (0.009) | (0.073) | (0.042) | (0.024) | (0.028) | (0.025) |
6 | 0.83 | 0.939 | 0.781 | 0.911 | 0.803 | 0.951 | 0.768 | 0.893 | 0.785 | 0.959 | 0.76 | 0.884 | 0.883 | 0.85 | 0.825 |
| Â | (0.069) | (0.029) | (0.042) | (0.020) | (0.069) | (0.020) | (0.043) | (0.021) | (0.065) | (0.017) | (0.043) | (0.024) | (0.023) | (0.025) | (0.03) |
7 | 0.852 | 0.993 | 0.887 | 0.947 | 0.848 | 0.994 | 0.886 | 0.944 | 0.849 | 0.996 | 0.89 | 0.945 | 0.933 | 0.912 | 0.9 |
| Â | (0.158) | (0.011) | (0.087) | (0.044) | (0.157) | (0.008) | (0.083) | (0.043) | (0.156) | (0.006) | (0.081) | (0.043) | (0.011) | (0.014) | (0.013) |
8 | 0.873 | 0.968 | 0.859 | 0.945 | 0.861 | 0.975 | 0.857 | 0.938 | 0.86 | 0.981 | 0.864 | 0.937 | 0.933 | 0.913 | 0.899 |
| Â | (0.076) | (0.025) | (0.039) | (0.018) | (0.077) | (0.017) | (0.039) | (0.018) | (0.072) | (0.014) | (0.035) | (0.016) | (0.011) | (0.014) | (0.013) |