Here is my question before:C: which kind of normalization is suitable for corr.test has been already finished.

Now I met a new problem with my result after I used corr.test with log2(fpkm+1) gene expression counts. That means I got many NAs after filter :

res<-res[abs(res$r)>0.65 & res$p<0.05,]

> res<-res[abs(res$r)>0.65 & res$p<0.05,]
> res
                 r            p
Zhx3     0.7006877 1.584370e-28
Zhx2     0.6918752 2.025494e-27
Zfp747   0.7003759 1.736518e-28
Zfp46    0.6860264 1.045938e-26
Zcchc6   0.6585428 1.431681e-23
Zbtb7a   0.6552056 3.268403e-23
Zbtb46   0.6539389 4.459021e-23
Zbtb38   0.6721778 4.385947e-25
NA              NA           NA
Wwtr1    0.6562822 2.507464e-23
Wfdc1    0.6649660 2.836392e-24
Wdr44    0.6584699 1.457838e-23
Vps37c   0.6595098 1.124932e-23
NA.1            NA           NA
NA.2            NA           NA
NA.3            NA           NA
NA.4            NA           NA
NA.5            NA           NA
NA.6            NA           NA
NA.7            NA           NA
NA.8            NA           NA
NA.9            NA           NA
NA.10           NA           NA
NA.11           NA           NA
NA.12           NA           NA
NA.13           NA           NA
NA.14           NA           NA
NA.15           NA           NA
NA.16           NA           NA
NA.17           NA           NA
NA.18           NA           NA
NA.19           NA           NA
NA.20           NA           NA

Is there anything wrong? Should I think that I can filter the NAs only ? Is't that right with NAs in my corr.test(log2(fpkm+1)) results ?

I need your help. Vary thankful.