# dual RPKM threshold:
# for polyRPKM <20, sig. median diff!
m0
[1] 20
[1] 0.0004264747
[1] 7.028158e-10
[1] 30
[1] 7.711474e-05
[1] 4.252154e-13
m2
[1] 20
[1] 3.293062e-12
[1] 0

# for polyRPKM >100, no sig. median diff!
m0 [1] 100
[1] 0.4457927
[1] 0.133914



#
opt RPKM threshold

m0
#up to 40 , WT > KO for >

# for polyRPKM >20, no sig. median diff!
# for polyRPKM >60  #KS non sig
m2
# for polyRPKM >100, no sig. median diff!
# for polyRPKM >120  #KS non sig


for (th in seq(10,150,10)){
print (th)
lpWT_m0=all_m0[ (((all_m0$RPKM_WT_0h.x))<th),]
lpKO_m0=all_m0[ (((all_m0$RPKM_KO_0h.x))<th),]
lpWT_m0_rna=log2(1+lpWT_m0$RPKM_WT_0h.y)
lpKO_m0_rna=log2(1+lpKO_m0$RPKM_KO_0h.y)
print(summary(lpWT_m0_rna))
print(summary(lpKO_m0_rna))
t=wilcox.test(lpWT_m0_rna,lpKO_m0_rna)
#print (t)
#t=ks.test(lpWT_m0_rna,lpKO_m0_rna)
print (t$p.value)
}
[1] 10
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.506   2.058   2.277   2.753  13.860    8043 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.331   1.941   2.118   2.609  13.860    8037 
[1] 8.727984e-27
[1] 20
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.616   2.235   2.432   2.976  13.860    8261 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.468   2.161   2.345   2.931  15.200    8249 
[1] 3.614908e-11
[1] 30
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.683   2.339   2.520   3.118  13.860    8329 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.548   2.295   2.481   3.165  15.200    8328 
[1] 7.711474e-05
[1] 40
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.719   2.404   2.578   3.211  13.860    8371 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.588   2.370   2.563   3.307  15.200    8357 
[1] 0.01291414

#up to 40 , WT > KO

[1] 50
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.741   2.444   2.620   3.274  13.860    8385 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.614   2.424   2.622   3.396  15.200    8379 
[1] 0.1223387
[1] 60
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.758   2.475   2.657   3.325  13.860    8396 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.633   2.458   2.663   3.464  15.200    8389 
[1] 0.1544029
[1] 70
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.766   2.491   2.676   3.359  13.860    8406 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.647   2.481   2.694   3.502  15.200    8398 
[1] 0.3948446
[1] 80
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.772   2.509   2.696   3.390  13.860    8414 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.660   2.507   2.723   3.542  15.200    8404 
[1] 0.6433163
[1] 90
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.780   2.520   2.710   3.411  13.860    8415 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.667   2.523   2.742   3.567  15.200    8406 
[1] 0.8198271
[1] 100
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.785   2.527   2.721   3.426  15.580    8418 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.673   2.533   2.757   3.587  15.660    8411 
[1] 0.9064632
[1] 110
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.790   2.535   2.735   3.444  15.580    8420 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.677   2.540   2.770   3.601  15.660    8412 
[1] 0.8466212
[1] 120
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.792   2.544   2.743   3.456  15.580    8420 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.683   2.551   2.784   3.620  15.660    8418 
[1] 0.9598768
[1] 130
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.793   2.550   2.750   3.467  15.580    8424 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.685   2.559   2.793   3.630  15.660    8418 
[1] 0.9227511
[1] 140
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.795   2.557   2.758   3.478  15.580    8426 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.689   2.563   2.800   3.642  15.660    8421 
[1] 0.9838746
[1] 150
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.263   1.798   2.562   2.766   3.490  15.580    8427 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.246   1.691   2.570   2.809   3.651  15.660    8425 
[1] 0.9375556


for (th in seq(10,300,10)){
 print (th)
lpWT_m0=all_m0[ (((all_m0$RPKM_WT_0h.x))>=th),]
lpKO_m0=all_m0[ (((all_m0$RPKM_KO_0h.x))>=th),]
lpWT_m0_rna=log2(1+lpWT_m0$RPKM_WT_0h.y)
lpKO_m0_rna=log2(1+lpKO_m0$RPKM_KO_0h.y)
s1=summary(lpWT_m0_rna)
s2=summary(lpKO_m0_rna)
print (s2[["Median"]]-s1[["Median"]])
t=wilcox.test(lpWT_m0_rna,lpKO_m0_rna)
#print (t)
#t=ks.test(lpWT_m0_rna,lpKO_m0_rna)
print (t$p.value)
}

for (th in seq(10,150,10)){
 print (th)
lpWT_m0=all_m0[ (((all_m0$RPKM_WT_0h.x))>=th),]
lpKO_m0=all_m0[ (((all_m0$RPKM_KO_0h.x))>=th),]
lpWT_m0_rna=log2(1+lpWT_m0$RPKM_WT_0h.y)
lpKO_m0_rna=log2(1+lpKO_m0$RPKM_KO_0h.y)
print(summary(lpWT_m0_rna))
print(summary(lpKO_m0_rna))
t=wilcox.test(lpWT_m0_rna,lpKO_m0_rna)
#print (t)
#t=ks.test(lpWT_m0_rna,lpKO_m0_rna)
print (t$p.value)
}
[1] 10
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.579   2.855   3.554   3.758   4.392  15.580    4234 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.855   3.216   3.900   4.148   4.804  15.660    4240 
[1] 2.086947e-60
[1] 20
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.650   3.204   3.935   4.105   4.714  15.580    4016 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.942   3.643   4.387   4.574   5.236  15.660    4028 
[1] 1.860779e-61
[1] 30
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.884   3.541   4.249   4.411   5.039  15.580    3948 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.103   3.991   4.712   4.900   5.575  15.660    3949 
[1] 3.365896e-47
[1] 40
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.273   3.810   4.489   4.650   5.285  15.580    3906 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.120   4.283   5.030   5.187   5.858  15.660    3920 
[1] 5.997346e-43
[1] 50
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.445   4.012   4.674   4.861   5.514  15.580    3892 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.262   4.508   5.265   5.422   6.110  15.660    3898 
[1] 1.639245e-36
[1] 60
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.514   4.187   4.862   5.033   5.673  15.580    3881 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.262   4.681   5.395   5.576   6.300  15.660    3888 
[1] 8.967766e-28
[1] 70
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.514   4.305   5.002   5.156   5.824  15.580    3871 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.262   4.811   5.556   5.720   6.493  15.660    3879 
[1] 4.202631e-25
[1] 80
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.514   4.429   5.104   5.279   5.999  15.580    3863 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   4.947   5.700   5.851   6.651  15.660    3873 
[1] 7.144085e-22
[1] 90
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.514   4.520   5.245   5.381   6.122  15.580    3862 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.064   5.876   5.990   6.797  15.660    3871 
[1] 6.217653e-22
[1] 100
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.683   4.590   5.353   5.462   6.209  15.570    3859 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.144   5.975   6.061   6.874  14.490    3866 
[1] 3.653003e-19
[1] 110
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.683   4.650   5.464   5.550   6.275  15.570    3857 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.198   6.090   6.156   6.980  14.490    3865 
[1] 9.652684e-17
[1] 120
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.683   4.708   5.539   5.644   6.398  15.570    3857 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.292   6.182   6.255   7.108  14.490    3859 
[1] 1.30591e-15
[1] 130
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.881   4.782   5.585   5.712   6.469  15.570    3853 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.315   6.266   6.328   7.176  14.490    3859 
[1] 2.295751e-14
[1] 140
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.881   4.838   5.674   5.771   6.544  15.570    3851 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.360   6.322   6.392   7.277  14.490    3856 
[1] 3.938809e-13
[1] 150
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.881   4.883   5.751   5.801   6.610  13.080    3850 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.338   5.484   6.386   6.455   7.305  14.490    3852 
[1] 1.81303e-12

















for (th in seq(5,100,5)){
print (th)
lpWT_m0=all_m0[ (((all_m0$RPKM_WT_0h.x))>=th),]
lpKO_m0=all_m0[ (((all_m0$RPKM_KO_0h.x))>=th),]
lpWT_m0_rna=log2(1+lpWT_m0$RPKM_WT_0h.y)
lpKO_m0_rna=log2(1+lpKO_m0$RPKM_KO_0h.y)
summary(lpWT_m0_rna)
summary(lpKO_m0_rna)
t=wilcox.test(lpWT_m0_rna,lpKO_m0_rna)
print (t$p.value)
t=ks.test(lpWT_m0_rna,lpKO_m0_rna)
print (t$p.value)
}
# for polyRPKM >20, no sig. median diff!
# for polyRPKM >60  #KS non sig

#@
for (th in seq(5,100,5)){
print (th)
lpWT_m2=all_m2[ (((all_m2$RPKM_WT_m2.x))>th),]
lpKO_m2=all_m2[ (((all_m2$RPKM_KO_m2.x))>th),]
lpWT_m2_rna=log2(1+lpWT_m2$RPKM_WT_m2.y)
lpKO_m2_rna=log2(1+lpKO_m2$RPKM_KO_m2.y)
t=wilcox.test(lpWT_m2_rna,lpKO_m2_rna)
print (t$p.value)
t=ks.test(lpWT_m2_rna,lpKO_m2_rna)
print (t$p.value)
}
[1] 100
[1] 0.09523174
for (th in seq(110,200,10)){
print (th)
lpWT_m2=all_m2[ (((all_m2$RPKM_WT_m2.x))>th),]
lpKO_m2=all_m2[ (((all_m2$RPKM_KO_m2.x))>th),]
lpWT_m2_rna=log2(1+lpWT_m2$RPKM_WT_m2.y)
lpKO_m2_rna=log2(1+lpKO_m2$RPKM_KO_m2.y)
t=wilcox.test(lpWT_m2_rna,lpKO_m2_rna)
print (t$p.value)
t=ks.test(lpWT_m2_rna,lpKO_m2_rna)
print (t$p.value)
}
[1] 120
[1] 0.112161
[1] 0.04280038
[1] 130
[1] 0.1608573
[1] 0.06199135


# t.tests
for (th in seq(5,100,5)){
print (th)
lpWT_m0=all_m0[ (((all_m0$RPKM_WT_0h.x))<th),]
lpKO_m0=all_m0[ (((all_m0$RPKM_KO_0h.x))<th),]
lpWT_m0_rna=log2(1+lpWT_m0$RPKM_WT_0h.y)
lpKO_m0_rna=log2(1+lpKO_m0$RPKM_KO_0h.y)
summary(lpWT_m0_rna)
summary(lpKO_m0_rna)
t=t.test(lpWT_m0_rna,lpKO_m0_rna)
print (t)
#t=ks.test(lpWT_m0_rna,lpKO_m0_rna)
#print (t$p.value)
}


# small RNA fc
th=1
lpWT_m0=all_m0[ abs(all_m0$logFC.y)<th,]
lpWT_m0_rna=lpWT_m0$logFC.x
summary(lpWT_m0_rna)
t=t.test(lpWT_m0_rna)
print (t)
#t=ks.test(lpWT_m0_rna,lpKO_m0_rna)
#print (t$p.value)


lpWT_m2=all_m2[ abs(all_m2$logFC.y)<th,]
lpWT_m2_rna=lpWT_m2$logFC.x
summary(lpWT_m2_rna)
t=t.test(lpWT_m2_rna)
print (t)
>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 -9.853  -0.106  -0.001  -0.012   0.091   8.444    7286 
> > 
	One Sample t-test

data:  lpWT_m2_rna
t = -3.2194, df = 11062, p-value = 0.001288
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -0.019242527 -0.004678193
sample estimates:
  mean of x 
-0.01196036 

for (th in seq(0.1,3,0.1)){
lpWT_m2=all_m2[ abs(all_m2$logFC.y)<th,]
lpWT_m2_rna=lpWT_m2$logFC.x
#summary(lpWT_m2_rna)
t=t.test(lpWT_m2_rna)
print (c(th,t$p.value,t$estimate))
}
 0.100000000  0.487146078 -0.007290036 
 0.20000000  0.47899360 -0.00528269 
 0.300000000  0.207674765 -0.007429493 
 0.400000000  0.217589422 -0.006159019 
 0.50000000  0.01674779 -0.01133406 
 0.60000000  0.01862607 -0.01024378 
 0.700000000  0.003351732 -0.012362685 
 0.800000000  0.001703001 -0.012534324 
 0.90000000  0.00218693 -0.01179294 
 1.000000000  0.001288156 -0.011960360 
 1.100000000  0.001124216 -0.011819200 
 1.200000000  0.002056868 -0.010948290 
 1.300000000  0.001429076 -0.011139561 
 1.4000000000  0.0003291699 -0.0124294739 
 1.5000000000  0.0005993362 -0.0118093158 
 1.6000000000  0.0003656361 -0.0121752754 
 1.7000000000  0.0002867508 -0.0123786652 
 1.8000000000  0.0002202352 -0.0126104108  *max log2FC_poly diff, min p-val
 1.9000000000  0.0004150935 -0.0120531962 
 2.0000000000  0.0006115344 -0.0116982407 
 2.1000000000  0.0005900779 -0.0117348341 
 2.2000000000  0.0004204676 -0.0120361638 
 2.3000000000  0.0003998488 -0.0120576850 
 2.4000000000  0.0004454837 -0.0119718344 
 2.5000000000  0.0005046685 -0.0118422309 
 2.6000000000  0.0005222726 -0.0117961573 
 2.7000000000  0.0006320587 -0.0116083550 
 2.8000000000  0.0005879617 -0.0116832775 
 2.9000000000  0.0004344912 -0.0119615066 
 3.0000000000  0.0004139633 -0.0119977615 

for (th in seq(0.1,4,0.1)){
lpWT_m0=all_m0[ abs(all_m0$logFC.y)<th,]
lpWT_m0_rna=lpWT_m0$logFC.x
#summary(lpWT_m0_rna)
t=t.test(lpWT_m0_rna)
print (c(th,t$p.value,t$estimate))
}
 1.000000e-01  6.496133e-09 -1.044221e-01 
 2.000000e-01  8.403497e-16 -1.040255e-01 
 3.000000e-01  1.460709e-24 -1.051455e-01 
 4.000000e-01  1.084818e-26 -9.606663e-02 
 5.000000e-01  7.970353e-30 -9.338154e-02 
 6.000000e-01  1.471633e-34 -9.268635e-02 
 7.000000e-01  3.942426e-39 -9.375648e-02 
 8.000000e-01  6.293286e-42 -9.064803e-02 
 9.000000e-01  1.893778e-45 -9.135268e-02 
 1.000000e+00  1.005937e-49 -9.284367e-02 
 1.100000e+00  1.723476e-51 -9.131765e-02 
 1.200000e+00  9.222956e-58 -9.439133e-02 
 1.300000e+00  4.114079e-61 -9.572962e-02 
 1.400000e+00  7.061587e-61 -9.434108e-02 
 1.500000e+00  2.333253e-63 -9.533693e-02 
 1.600000e+00  9.503528e-66 -9.634871e-02 
 1.700000e+00  8.957196e-68 -9.742002e-02 
 1.800000e+00  3.309617e-71 -9.953695e-02 
 1.900000e+00  1.198792e-71 -9.956904e-02 
 2.000000e+00  4.691089e-71 -9.986298e-02 
 2.100000e+00  6.160392e-72 -1.003344e-01 
 2.200000e+00  7.703616e-72 -1.002107e-01 
 2.300000e+00  8.822497e-73 -1.006995e-01 
 2.400000e+00  3.451962e-74 -1.015757e-01 
 2.500000e+00  3.792331e-74 -1.016553e-01 
 2.600000e+00  1.446359e-74 -1.020520e-01 
 2.700000e+00  4.097177e-75 -1.022696e-01 
 2.800000e+00  2.036075e-75 -1.025216e-01 
 2.900000e+00  5.846158e-76 -1.028980e-01 
 3.000000e+00  1.444581e-76 -1.032901e-01 
 3.100000e+00  1.101312e-76 -1.033257e-01 #max log2FC_poly
 3.200000e+00  9.264246e-77 -1.033146e-01 
 3.300000e+00  1.187754e-76 -1.031687e-01 
 3.400000e+00  2.080877e-76 -1.029676e-01 
 3.50000e+00  1.97175e-76 -1.02945e-01 
 3.600000e+00  3.075706e-76 -1.028006e-01 
 3.700000e+00  2.885652e-76 -1.028061e-01 
 3.800000e+00  2.298754e-76 -1.028651e-01 
 3.900000e+00  2.203356e-76 -1.028514e-01 
 4.000000e+00  2.433614e-76 -1.028044e-01 

for (th in seq(0.1,4,0.1)){
lpWT_m1=all_m1[ abs(all_m1$logFC.y)<th,]
lpWT_m1_rna=lpWT_m1$logFC.x
#summary(lpWT_m1_rna)
t=t.test(lpWT_m1_rna)
print (c(th,t$p.value,t$estimate))
}
 0.100000000  0.009868665 -0.018080663 
 2.000000e-01  1.520201e-05 -2.300541e-02 
 3.000000e-01  1.960078e-06 -2.165785e-02 
 4.000000e-01  1.314451e-08 -2.325017e-02 
 5.000000e-01  6.019923e-11 -2.461189e-02 
 6.000000e-01  1.113480e-14 -2.762746e-02 
 7.000000e-01  3.244012e-12 -2.502496e-02 
 8.000000e-01  1.144435e-13 -2.619572e-02 
 9.000000e-01  9.576692e-15 -2.690930e-02 
 1.000000e+00  1.045966e-14 -2.632766e-02 
 1.100000e+00  4.145957e-16 -2.729887e-02 
 1.200000e+00  1.684695e-17 -2.941222e-02 
 1.300000e+00  6.470289e-18 -2.968295e-02 
 1.400000e+00  2.063026e-19 -3.090258e-02 
 1.500000e+00  3.690243e-20 -3.134149e-02 
 1.600000e+00  7.857991e-21 -3.174453e-02 
 1.70000e+00  1.76897e-20 -3.13929e-02 
 1.800000e+00  5.679575e-21 -3.178061e-02 
 1.900000e+00  3.673031e-21 -3.187506e-02 
 2.000000e+00  8.826011e-22 -3.229144e-02 
 2.100000e+00  6.284001e-22 -3.236110e-02 
 2.200000e+00  1.204985e-22 -3.291175e-02 
 2.300000e+00  1.594133e-22 -3.277998e-02 
 2.400000e+00  2.327326e-22 -3.261924e-02 
 2.500000e+00  1.914632e-22 -3.267078e-02 
 2.600000e+00  2.468094e-22 -3.259023e-02 
 2.700000e+00  2.958341e-22 -3.251041e-02 
 2.800000e+00  2.216330e-22 -3.261032e-02 
 2.900000e+00  1.488608e-22 -3.274861e-02  #max neg log2FC_poly
 3.000000e+00  2.971957e-22 -3.252558e-02 
 3.100000e+00  2.410021e-22 -3.258412e-02 
 3.200000e+00  2.279321e-22 -3.259772e-02 
 3.300000e+00  2.666065e-22 -3.253581e-02 
 3.400000e+00  2.491112e-22 -3.256759e-02 
 3.500000e+00  4.581797e-22 -3.240852e-02 
 3.600000e+00  4.866625e-22 -3.238507e-02 
 3.700000e+00  5.008468e-22 -3.236577e-02 
 3.800000e+00  4.912017e-22 -3.237022e-02 
 3.900000e+00  4.912017e-22 -3.237022e-02 
 4.000000e+00  5.257471e-22 -3.234536e-02 

for (th in seq(0.1,3,0.1)){
lpWT_2h=all_2h[ abs(all_2h$logFC.y)<th,]
lpWT_2h_rna=lpWT_2h$logFC.x
#summary(lpWT_2h_rna)
t=t.test(lpWT_2h_rna)
print (c(th,t$p.value,t$estimate))
}
1.000000e-01 3.677624e-05 5.649861e-02 
2.000000e-01 1.018713e-06 5.122802e-02 
3.000000e-01 5.124468e-09 4.968657e-02 
4.000000e-01 3.597878e-12 5.218413e-02 
5.000000e-01 9.870399e-17 5.649109e-02 
6.000000e-01 1.894578e-20 5.876526e-02 
7.000000e-01 6.033250e-23 5.875874e-02 
8.000000e-01 1.335778e-25 5.974249e-02 
9.000000e-01 1.604874e-26 5.896874e-02 
1.000000e+00 2.103382e-28 5.949763e-02 
1.100000e+00 5.552859e-30 5.982878e-02 
1.20000e+00 2.96119e-31 6.08628e-02 
1.300000e+00 2.860608e-33 6.186547e-02 
1.400000e+00 7.439221e-34 6.172303e-02 
1.500000e+00 5.257036e-34 6.121780e-02 
1.600000e+00 1.601265e-33 6.045446e-02 
1.700000e+00 1.848349e-34 6.101948e-02 
1.800000e+00 5.604372e-35 6.106551e-02 
1.900000e+00 7.634028e-34 5.971476e-02 
2.000000e+00 1.762597e-34 6.005369e-02 
2.100000e+00 2.596502e-34 5.966979e-02 
2.200000e+00 1.279893e-34 5.982537e-02 
2.300000e+00 5.709477e-35 6.007413e-02 
2.400000e+00 1.004504e-34 5.973267e-02 
2.500000e+00 5.481460e-35 5.990467e-02 
2.600000e+00 2.103033e-35 6.047944e-02 
2.700000e+00 7.528461e-36 6.076871e-02 
2.800000e+00 8.196966e-36 6.076781e-02 
2.900000e+00 4.646835e-36 6.094868e-02 
3.000000e+00 1.945165e-36 6.131493e-02


for (th in seq(0.1,3,0.1)){
lpWT_6h=all_6h[ abs(all_6h$logFC.y)<th,]
lpWT_6h_rna=lpWT_6h$logFC.x
#summary(lpWT_6h_rna)
t=t.test(lpWT_6h_rna)
print (c(th,t$p.value,t$estimate))
}
 1.000000e-01  6.716566e-05 -4.896921e-02 
 2.000000e-01  1.801946e-12 -6.325413e-02 
 3.000000e-01  1.372263e-14 -5.455853e-02 
 4.000000e-01  4.279665e-19 -5.781018e-02 
 5.000000e-01  2.098324e-21 -5.508452e-02 
 6.000000e-01  2.365908e-25 -5.561000e-02 
 7.000000e-01  1.887478e-33 -5.993363e-02 
 8.000000e-01  2.444473e-36 -5.966464e-02 
 9.000000e-01  5.178144e-41 -6.108836e-02 
 1.000000e+00  1.160738e-44 -6.230791e-02 
 1.100000e+00  1.000155e-44 -6.299066e-02 
 1.200000e+00  4.654940e-48 -6.446069e-02 
 1.300000e+00  2.900569e-47 -6.286390e-02 
 1.400000e+00  3.260025e-49 -6.347924e-02 
 1.500000e+00  2.054068e-49 -6.281208e-02 
 1.600000e+00  9.857281e-51 -6.325790e-02 
 1.700000e+00  2.327528e-51 -6.326000e-02 
 1.800000e+00  1.915327e-52 -6.393279e-02 
 1.900000e+00  3.144686e-54 -6.505067e-02 
 2.000000e+00  1.727094e-54 -6.498147e-02 
 2.100000e+00  8.778178e-56 -6.603638e-02 
 2.200000e+00  1.836561e-55 -6.561358e-02 
 2.300000e+00  2.055488e-55 -6.547340e-02 
 2.400000e+00  4.433750e-56 -6.584019e-02 
 2.500000e+00  1.714805e-56 -6.600520e-02 
 2.600000e+00  1.123825e-56 -6.619680e-02 
 2.700000e+00  1.310599e-56 -6.608673e-02 #max neg 
 2.800000e+00  5.690101e-54 -6.538688e-02 
 2.900000e+00  1.668528e-53 -6.507636e-02 
 3.000000e+00  2.294068e-53 -6.496837e-02