Table 1 Measures of dependence between two variables
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] |