Similar observations were made over all datasets (see Additional file 4 and Additional file 5). Given the results from all 28 datasets, we were also able to compare the PPI PCC calculation and GGI PCC calculation at a higher level. Under a FDR threshold of 0.1, the list of significant PPI pairs or GGI pairs from each dataset was determined. Based on these lists, we counted the significantly correlated PPIs or GGIs (termed 'significant PPIs' or 'significant GGIs') over each dataset, and the datasets on which a PPI or GGI demonstrates significant correlation (termed 'PPIsignificant datasets' or 'GGI-significant datasets'). The former is a 'twenty-eight by two' table, shown in Table 4 ; the latter is a '1,037 by two' table, shown fully in Additional file 4 and partly in Table 5 . In both summary tables, we find that the statistics of PPI PCC calculation are mostly larger than the counterparts of GGI PCC calculation, indicating that PPI PCC calculation can detect more correlated PPI pairs in a certain experiment setting ( Table  4 ), and that it can detect the correlation tendency across more experiment settings ( Table 5 ).