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Table 1 Measures of dependence between two variables

From: From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

Co-expression measures What measures? Input/Output Features
Pearson’s correlation (PC) Tendency to respond in opposite/same direction across different samples Input: gene expressions value Output:   • [0,1] both genes increase   • [−1,0] one increase and other decrease   • Sensitivity to outliers   • Bad array of expression level can determine positive PC value   • Measure linear relations
Spearman’s correlation (SC) Tendency to respond in opposite/same direction across different samples Input: ranking values from expression levels in samples Output:   • [0,1] Both genes increase   • [−1,0] One increase and the other decrease   • Robust to outliers   • Detect non-linear associations
Mutual information Reduction of uncertainty of a gene given the knowledge about other gene Input: gene expression values Output:   • 0 there is no interdependence   • >0 there is interdependence   • Measure complex non-linear type relations (rarely present in biological data)   • More samples are needed than PC, SC   • Time-consuming computation
Kendall Correspondence/compatibility among two rankings Input: gene expression value Output:   • 1 perfect correspondence   • -1 rankings exactly inverted   • Similar to SC   • Robust to outliers   • Assumes fewer values than SC in the range [−1,1]