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Table 5 Connectivity, differential expression and machine learning data used as criteria for module prioritization

From: Efficient and biologically relevant consensus strategy for Parkinson’s disease gene prioritization

Healthy Control (HC) Modules
Module n <k> <kintra> <logPD-logHC> nML Merit_ML nLimma Merit_Limma nML-Limma Merit_ML-Limma
HC_01 123 12.04 1.38 −0.021 3 1.23 1 0.51 1 1.23
HC_02 349 34.57 7.29 −0.061 6 0.87 13 2.36 4 1.73
HC_03 1057 19.04 8.85 0.011 4 0.19 2 0.12 2 0.29
HC_04 169 17.02 2.59 −0.002 1 0.30 0 0.00 0 0.00
HC_05 347 9.23 2.59 0.165 2 0.29 1 0.18 1 0.44
HC_06 74 8.26 0.73 0.005 0 0.00 0 0.00 0 0.00
HC_07 290 14.81 5.19 0.073 4 0.70 6 1.31 1 0.52
HC_08 251 10.94 2.05 0.030 11 2.21 10 2.52 5 3.02
HC_09 2 1.15 0.00 0.022 0 0.00 0 0.00 0 0.00
HC_10 37 15.32 1.48 0.043 1 1.36 0 0.00 0 0.00
HC_11 91 10.95 1.23 0.048 3 1.66 0 0.00 0 0.00
HC_12 61 23.65 3.85 0.028 2 1.65 0 0.00 0 0.00
HC_13 164 10.23 1.79 0.007 3 0.92 1 0.39 1 0.92
HC_14 71 8.33 0.81 −0.001 0 0.00 0 0.00 0 0.00
HC_15 2120 49.53 36.69 −0.062 82 1.95 97 2.89 40 2.86
HC_16 3271 22.06 14.66 −0.064 46 0.71 3 0.06 1 0.05
Parkinson’s Disease (PD) Modules
PD_01 603 286.30 70.52 0.022 6 0.50 1 0.10 1 0.25
PD_02 1437 262.21 150.85 −0.126 69 2.42 103 4.53 42 4.42
PD_03 133 210.12 13.36 0.035 1 0.38 0 0.00 0 0.00
PD_04 161 284.83 22.96 0.089 4 1.25 3 1.18 2 1.88
PD_05 789 231.70 62.45 −0.025 5 0.32 1 0.08 0 0.00
PD_06 468 238.37 38.64 0.132 3 0.32 0 0.00 0 0.00
PD_07 494 316.82 58.43 0.103 24 2.45 19 2.43 8 2.45
PD_08 213 218.15 28.17 −0.033 4 0.95 2 0.59 1 0.71
PD_09 4179 333.39 247.08 −0.047 52 0.63 5 0.08 2 0.07
  1. n: number of genes in the module; <k>: average node degree; <k intra >: intra-modular average node degree; <logPD-logHC>: module average differential of the log transformed average expression of a gene i across PD samples and healthy control samples; n ML : number of genes identified by ML analysis included in the module; n Limma : number of genes identified by Limma analysis included in the module; n ML-Limma : number of common genes identified by both ML and Limma analyses included in the module; Merit_ML = (n ML /168)/(N/8477): merit assigned to the module based on n ML , the total number of genes identified by ML analysis (168), N, and the total number of background genes (8477); Merit_Limma = (n Limma /134)/(N/8477): merit assigned to the module based on n Limma , the total number of genes identified by Limma analysis (134), N, and the total number of background genes (8477); Merit_ML-Limma = (n ML-Limma /56)/(N/8477): merit assigned to the module based on n ML-Limma , the total number of common genes identified by both ML and Limma analyses (56), N, and the total number of background genes (8477)
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