Figure 3.

Correlation-based analysis of the drug-CCRG pairs. (A) For each of the 62 drug-CCRG pairs, we compared the PCC of drug-CCRG with that of random drug-gene pairs. The numbers of drug-CCRG pairs, which were identified under the corresponding zthreshold, were listed over the blue bar. We set zthreshold to 0.8 in concordance with previous reports (Proc Natl Acad Sci U S A 2001, 98:10787–10792). Under this threshold, we conducted further analysis (Figure 3B, Figure 3C, and Figure 3D). (B, C, D) Three types of PCC distribution compared to random PCC. The x-axis shows the PCC of drug-gene pair, the y-axis shows the probability density value of PCC. The red line represents the PCC of a drug-CCRG pair, while the blue curves shows the distribution of PCC of random drug-gene pairs. (B) PCC of drug-CCRG is significantly smaller than PCC of random drug-gene pairs. 21/62 indicates that 21 of 62 drug-CCRG pairs exhibit PCCs significantly smaller than random PCCs. We offered an example between doxorubicin and ABCB1. It was reported that ABCB1 overexpression predicts doxorubicin resistance. (C) PCC of drug-CCRG is significantly larger than that of random drug-gene pairs. 14/62 indicates that 14 of 62 drug-CCRG pairs exhibit PCCs significantly largerr than random PCCs. It was reported PRKCB can predict chemosensitivity of NSC169517. (D) PCC of drug-CCRG is similar with that of random drug-gene pairs. 27/62 indicates that 27 of 62 drug-CCRG pairs do not exhibit PCCs significantly different from random PCCs. It was reported that GRIK1 was able to predict chemosensitivity of paclitaxel (taxol).

Chen et al. BMC Medical Genomics 2012 5:43   doi:10.1186/1755-8794-5-43
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