CNVscan for ERP011529
Mark Ziemann
2021-09-30
Intro
Here we’re doing an analysis to identify CNV using normalised reads counts across genomic windows.
An idea could be to regress out the effect of GC content.
library("cobs")
library("quantreg")
library("parallel")
library("gplots")
library("dplyr")
library("kableExtra")interpolate_points<-function(row,dat,curve){
MY_X=dat[row,1]
MY_Y=dat[row,2]
VAL1=tail(which(curve[,1]<MY_X),1)
VAL2=VAL1+1
X <- curve[c(VAL1,VAL2),1]
Y <- curve[c(VAL1,VAL2),2]
INTERP_Y=approx(X,Y,xout=MY_X)$y
INTERP_Y
}1 Mbp bins
Without any filtering.
x <- read.table("ERP011529.1e6_fmt.tsv",header=T,row.names=1)
x <- x[which(rowSums(x)>=10),]
x <- sweep(x, 2, colSums(x), FUN="/")*1000000
mysd <- apply(x,1,sd)
mean <- apply(x,1,mean)
y <- data.frame(log10(mean),mysd/mean)
colnames(y) = c("logMean","cv")
Rbs.9 <- cobs(y$logMean,y$cv, nknots=10,constraint="none",tau=0.99)## qbsks2():
## Performing general knot selection ...
##
## Deleting unnecessary knots ...Rbs.median <- cobs(y$logMean,y$cv,nknots=10,constraint="none",tau=0.5)## qbsks2():
## Performing general knot selection ...
##
## Deleting unnecessary knots ...pred <- data.frame(predict(Rbs.9))
res <- mclapply(X=1:nrow(y),function(row) {
interpolate_points(row,y,pred)
},mc.cores=8)
y$interpolated <- unlist(res)
y$diff=y$cv-y$interpolated
yy <- y[order(-y$diff),]
yy <- head(yy,50)
write.table(yy,file="ERP011529.1e6_regions.tsv")
yy %>% kbl() %>% kable_paper("hover", full_width = F)| logMean | cv | interpolated | diff | |
|---|---|---|---|---|
| 10:81000000-82000000 | 2.571062 | 0.3216520 | 0.1842859 | 0.1373660 |
| 7:19000000-20000000 | 2.619336 | 0.3102692 | 0.2075714 | 0.1026979 |
| X:7000000-8000000 | 2.535835 | 0.3885471 | 0.2873824 | 0.1011647 |
| 17:35000000-36000000 | 2.601035 | 0.2572826 | 0.1741790 | 0.0831035 |
| X:75000000-76000000 | 2.545626 | 0.3207221 | 0.2587277 | 0.0619944 |
| Y:5000000-6000000 | 1.869926 | 1.1897168 | 1.1406635 | 0.0490533 |
| Y:6000000-7000000 | 1.867554 | 1.1918538 | 1.1430646 | 0.0487893 |
| 7:144000000-144995196 | 2.585262 | 0.1895250 | 0.1454005 | 0.0441245 |
| 6:114000000-115000000 | 2.585146 | 0.1819751 | 0.1451877 | 0.0367874 |
| 8:121000000-122000000 | 2.668497 | 0.3236955 | 0.3063825 | 0.0173129 |
| X:100000000-101000000 | 2.512460 | 0.3566692 | 0.3477013 | 0.0089679 |
| X:20000000-21000000 | 2.516969 | 0.3455540 | 0.3383227 | 0.0072314 |
| 4:154000000-155000000 | 2.655814 | 0.2830282 | 0.2777679 | 0.0052603 |
| 17:27000000-28000000 | 2.664107 | 0.2976338 | 0.2964776 | 0.0011561 |
| 11:114000000-115000000 | 2.599093 | 0.1709293 | 0.1706353 | 0.0002939 |
| Y:1000000-2000000 | 1.854265 | 1.1567325 | 1.1565163 | 0.0002162 |
| JH584303.1:0-158099 | -2.054971 | 1.3853152 | 1.3852983 | 0.0000169 |
| 2:98000000-99000000 | 4.089085 | 0.5208948 | 0.5208942 | 0.0000006 |
| 8:124000000-125000000 | 2.609549 | 0.1886490 | 0.1897143 | -0.0010653 |
| 15:87000000-88000000 | 2.584004 | 0.1448419 | 0.1464108 | -0.0015689 |
| 3:47000000-48000000 | 2.584922 | 0.1406880 | 0.1447803 | -0.0040923 |
| 5:120000000-121000000 | 2.641111 | 0.2426812 | 0.2473034 | -0.0046222 |
| X:12000000-13000000 | 2.512036 | 0.3435656 | 0.3485847 | -0.0050191 |
| 13:57000000-58000000 | 2.589495 | 0.1472951 | 0.1531238 | -0.0058287 |
| 5:36000000-37000000 | 2.611260 | 0.1867220 | 0.1928356 | -0.0061136 |
| 2:86000000-87000000 | 2.586676 | 0.1412998 | 0.1479795 | -0.0066797 |
| X:120000000-121000000 | 2.504563 | 0.3573124 | 0.3641281 | -0.0068157 |
| 14:89000000-90000000 | 2.582973 | 0.1419924 | 0.1494254 | -0.0074330 |
| 4:139000000-140000000 | 2.649527 | 0.2556154 | 0.2635835 | -0.0079681 |
| 11:117000000-118000000 | 2.659181 | 0.2769278 | 0.2853653 | -0.0084375 |
| 19:55000000-56000000 | 2.588269 | 0.1396402 | 0.1508858 | -0.0112456 |
| 8:122000000-123000000 | 2.666959 | 0.2909094 | 0.3029134 | -0.0120040 |
| 6:74000000-75000000 | 2.581178 | 0.1426702 | 0.1546789 | -0.0120087 |
| Y:9000000-10000000 | 1.808391 | 1.1900439 | 1.2022160 | -0.0121721 |
| 17:39000000-40000000 | 2.588557 | 0.1388697 | 0.1514121 | -0.0125424 |
| 15:78000000-79000000 | 2.656553 | 0.2660906 | 0.2794358 | -0.0133453 |
| 4:17000000-18000000 | 2.586644 | 0.1344869 | 0.1479212 | -0.0134342 |
| 1:109000000-110000000 | 2.586430 | 0.1338710 | 0.1475303 | -0.0136594 |
| 12:106000000-107000000 | 2.589545 | 0.1392224 | 0.1532142 | -0.0139918 |
| 12:48000000-49000000 | 2.585132 | 0.1310594 | 0.1451626 | -0.0141032 |
| 4:153000000-154000000 | 2.615801 | 0.1869827 | 0.2011213 | -0.0141386 |
| 5:139000000-140000000 | 2.667719 | 0.2894899 | 0.3046264 | -0.0151366 |
| 4:27000000-28000000 | 2.578555 | 0.1468356 | 0.1623569 | -0.0155213 |
| 10:60000000-61000000 | 2.650034 | 0.2490168 | 0.2647281 | -0.0157114 |
| 6:20000000-21000000 | 2.585969 | 0.1307955 | 0.1466893 | -0.0158937 |
| 1:26000000-27000000 | 2.583022 | 0.1323432 | 0.1492825 | -0.0169393 |
| 11:14000000-15000000 | 2.589774 | 0.1366771 | 0.1536323 | -0.0169552 |
| 6:99000000-100000000 | 2.587116 | 0.1318044 | 0.1487830 | -0.0169786 |
| 12:60000000-61000000 | 2.584515 | 0.1278449 | 0.1449146 | -0.0170697 |
| 9:17000000-18000000 | 2.585081 | 0.1278213 | 0.1450697 | -0.0172484 |
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
zz<-x[which(rownames(x) %in% rownames(yy)),]
heatmap.2(as.matrix(zz),margin=c(8, 22),cexRow=0.65,trace="none",
cexCol=0.8,col=my_palette,scale="row")
heatmap.2(cor(t(zz)),trace="none",scale="none",margins=c(12,12),
cexRow=0.8, cexCol=0.8)
Keeping autosomes only
x <- read.table("ERP011529.1e6_fmt.tsv",header=T,row.names=1)
x <- x[grep("X",rownames(x),invert=TRUE),]
x <- x[grep("Y",rownames(x),invert=TRUE),]
x <- x[grep("M",rownames(x),invert=TRUE),]
x <- x[grep("J",rownames(x),invert=TRUE),]
x <- x[grep("G",rownames(x),invert=TRUE),]
x <- x[which(rowSums(x)>=10),]
x <- sweep(x, 2, colSums(x), FUN="/")*1000000
mysd <- apply(x,1,sd)
mean <- apply(x,1,mean)
y <- data.frame(log10(mean),mysd/mean)
colnames(y) = c("logMean","cv")
yy <- y[order(-y$cv),]
yy <- subset(yy,cv>0.3 & logMean > 0)
write.table(yy,file="ERP011529.1e6_regions.tsv")
yy %>% kbl() %>% kable_paper("hover", full_width = F)| logMean | cv | |
|---|---|---|
| 2:98000000-99000000 | 4.113368 | 0.5241233 |
| 6:103000000-104000000 | 2.870230 | 0.4190821 |
| 10:80000000-81000000 | 2.757917 | 0.4052646 |
| 9:3000000-4000000 | 3.427792 | 0.3302138 |
| 8:121000000-122000000 | 2.693012 | 0.3264869 |
| 10:81000000-82000000 | 2.595841 | 0.3262456 |
| 7:44000000-45000000 | 2.720154 | 0.3210505 |
| 7:19000000-20000000 | 2.644127 | 0.3151955 |
| 17:40000000-41000000 | 2.981729 | 0.3096937 |
| 4:140000000-141000000 | 2.700222 | 0.3071216 |
| 17:27000000-28000000 | 2.688616 | 0.3002579 |
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV",xlim=c(2,4.5),ylim=c(0.3,0.55))
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
zz <- x[which(rownames(x) %in% rownames(yy)),]
heatmap.2(as.matrix(zz),margin=c(8, 22),cexRow=0.65,trace="none",
cexCol=0.8,col=my_palette,scale="row")
heatmap.2(cor(t(zz)),trace="none",scale="none",margins=c(12,12),
cexRow=0.8, cexCol=0.8)
for (i in 1:19){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymax = max(chr)
plot(chr[,1], xaxt = "n", pch=19, col="gray",
ylim=c(0,mymax),ylab="RPM of 1Mbp bins of chr",main=mychr)
axis(1, at=chr[,1], labels=rownames(chr), xlab="chr", las=1)
points( chr[,1], xaxt = "n", las=1, pch=19, col="gray" )
points( chr[,2], xaxt = "n", las=1, pch=19, col="lightblue" )
points( chr[,3], xaxt = "n", las=1, pch=19, col="lightgreen" )
points( chr[,4], xaxt = "n", las=1, pch=19, col="pink" )
points( chr[,5], xaxt = "n", las=1, pch=19, col="orange" )
points( chr[,6], xaxt = "n", las=1, pch=19, col="black" )
points( chr[,7], xaxt = "n", las=1, pch=19, col="yellow" )
points( chr[,8], xaxt = "n", las=1, pch=19, col="red" )
points( chr[,9], xaxt = "n", las=1, pch=19, col="blue" )
grid()
}
lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymedian <- median(rowMeans(chr))
mymax <- max(rowMeans(chr))
rat <- mymax / mymedian
return(c(mymax,mymedian,rat))
})## [[1]]
## [1] 618.975207 406.517948 1.522627
##
## [[2]]
## [1] 12982.78574 405.77433 31.99509
##
## [[3]]
## [1] 515.206687 405.886563 1.269337
##
## [[4]]
## [1] 553.181554 407.171386 1.358596
##
## [[5]]
## [1] 499.564332 409.740409 1.219222
##
## [[6]]
## [1] 741.703733 403.501560 1.838168
##
## [[7]]
## [1] 524.993637 411.917220 1.274512
##
## [[8]]
## [1] 519.105349 402.969168 1.288201
##
## [[9]]
## [1] 2677.887968 415.285655 6.448304
##
## [[10]]
## [1] 572.687029 414.118869 1.382905
##
## [[11]]
## [1] 877.139869 427.516773 2.051709
##
## [[12]]
## [1] 498.310102 401.752174 1.240342
##
## [[13]]
## [1] 492.999768 405.738396 1.215068
##
## [[14]]
## [1] 547.883582 398.641460 1.374377
##
## [[15]]
## [1] 502.092811 401.943060 1.249164
##
## [[16]]
## [1] 532.459609 395.045654 1.347843
##
## [[17]]
## [1] 958.802948 407.466396 2.353085
##
## [[18]]
## [1] 505.331992 403.447758 1.252534
##
## [[19]]
## [1] 522.291490 411.542607 1.269107lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
top <- head(chr[order(-rowMeans(chr)),])
return(top)
})## [[1]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 1:88000000-89000000 556.5272 579.5795 691.3734
## 1:36000000-37000000 605.0038 446.8667 536.8263
## 1:135000000-136000000 604.2029 428.6154 548.7034
## 1:180000000-181000000 547.5692 443.7839 532.9654
## 1:133000000-134000000 566.0234 439.5163 520.8475
## 1:74000000-75000000 574.8839 444.2633 531.9830
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 1:88000000-89000000 594.5203 779.4637 567.1991
## 1:36000000-37000000 424.8600 552.0983 414.5577
## 1:135000000-136000000 406.8705 560.1132 407.3855
## 1:180000000-181000000 428.4973 544.3092 409.9168
## 1:133000000-134000000 411.4632 550.9862 402.5632
## 1:74000000-75000000 429.5102 513.5024 408.2504
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 1:88000000-89000000 566.6334 593.2706 642.2098
## 1:36000000-37000000 415.1103 372.4262 412.3460
## 1:135000000-136000000 405.2342 367.7554 405.7673
## 1:180000000-181000000 418.3829 374.7568 409.6343
## 1:133000000-134000000 414.9313 377.2791 406.1620
## 1:74000000-75000000 422.8214 364.6253 396.2987
##
## [[2]]
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## 2:32000000-33000000 795.7574 465.7066 597.8363
## 2:167000000-168000000 794.6599 474.0859 609.9045
## 2:31000000-32000000 745.2935 452.9700 590.0992
## 2:30000000-31000000 727.4961 461.7780 605.0803
## 2:26000000-27000000 680.8586 444.6297 563.9406
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 2:98000000-99000000 9536.1986 8484.7862 14889.1818
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## 2:31000000-32000000 428.5916 578.2905 433.2476
## 2:30000000-31000000 441.3077 559.2183 425.7505
## 2:26000000-27000000 432.2330 555.4261 427.9022
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 2:98000000-99000000 11753.7481 27733.9193 19363.4651
## 2:32000000-33000000 438.5807 363.1009 404.7202
## 2:167000000-168000000 439.0138 340.3904 386.4576
## 2:31000000-32000000 430.7323 360.0683 403.4853
## 2:30000000-31000000 432.3686 348.5164 385.7224
## 2:26000000-27000000 423.2044 375.7839 415.3187
##
## [[3]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
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## 3:5000000-6000000 492.3466 528.0525 433.3197
## 3:108000000-109000000 562.8156 456.8245 507.3764
## 3:88000000-89000000 620.5044 450.3315 525.8858
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 3:106000000-107000000 520.4544 405.1374 550.2221
## 3:89000000-90000000 433.3156 541.8890 423.8984
## 3:95000000-96000000 453.0766 506.2374 430.2353
## 3:5000000-6000000 514.9431 360.3759 484.3053
## 3:108000000-109000000 445.0721 504.1836 426.5226
## 3:88000000-89000000 425.9508 515.5647 410.8872
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 3:106000000-107000000 599.3637 513.9352 491.8795
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## 3:95000000-96000000 453.4824 382.2912 410.8629
## 3:5000000-6000000 531.7504 447.9523 427.4704
## 3:108000000-109000000 434.6336 388.4832 414.7522
## 3:88000000-89000000 427.9593 362.6393 395.3820
##
## [[4]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 4:147000000-148000000 566.6208 481.1362 523.7031
## 4:152000000-153000000 823.7374 458.9524 637.9515
## 4:140000000-141000000 845.3569 467.0628 612.3204
## 4:155000000-156000000 760.9975 460.1061 602.9969
## 4:149000000-150000000 726.9664 470.0989 591.0243
## 4:141000000-142000000 768.7732 462.7524 591.0434
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 4:147000000-148000000 562.9535 561.4852 584.0116
## 4:152000000-153000000 450.0339 580.2420 447.6511
## 4:140000000-141000000 421.3786 563.8372 440.9683
## 4:155000000-156000000 451.6250 565.2945 445.4277
## 4:149000000-150000000 435.6160 561.4042 441.3691
## 4:141000000-142000000 423.1542 553.0357 430.3576
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 4:147000000-148000000 530.2515 590.2640 578.2081
## 4:152000000-153000000 429.8996 349.5500 397.4731
## 4:140000000-141000000 438.1685 338.4597 385.4360
## 4:155000000-156000000 434.0715 364.4205 400.6145
## 4:149000000-150000000 443.5146 363.9687 407.1614
## 4:141000000-142000000 432.4935 342.2984 383.3640
##
## [[5]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 5:114000000-115000000 829.3563 461.6260 606.0283
## 5:115000000-116000000 729.4115 493.6116 596.3990
## 5:146000000-147000000 625.2249 493.3778 585.5922
## 5:139000000-140000000 812.2666 456.7426 597.3737
## 5:142000000-143000000 731.5641 466.5679 596.0626
## 5:134000000-135000000 747.3274 482.2041 571.6509
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 5:114000000-115000000 426.4798 564.6426 444.6978
## 5:115000000-116000000 453.2652 536.9164 441.5421
## 5:146000000-147000000 490.0481 550.1425 434.3867
## 5:139000000-140000000 426.6971 551.2162 435.8338
## 5:142000000-143000000 433.7872 550.4322 436.5890
## 5:134000000-135000000 415.2850 533.6355 429.7881
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 5:114000000-115000000 433.6885 342.6852 386.8746
## 5:115000000-116000000 463.3002 351.4612 387.5334
## 5:146000000-147000000 453.3991 389.9881 425.5067
## 5:139000000-140000000 431.6566 336.5940 382.5556
## 5:142000000-143000000 440.6709 349.2770 393.9307
## 5:134000000-135000000 448.6401 340.4002 380.8337
##
## [[6]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 6:103000000-104000000 1076.8453 1269.3839 1031.4274
## 6:83000000-84000000 639.1704 450.6706 544.6475
## 6:113000000-114000000 613.7329 440.5063 529.4333
## 6:88000000-89000000 604.5673 426.5187 540.5420
## 6:29000000-30000000 555.8407 457.7092 509.6700
## 6:72000000-73000000 529.6532 448.9518 532.6176
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 6:103000000-104000000 551.6849 370.5766 630.4453
## 6:83000000-84000000 450.1118 524.4957 429.7164
## 6:113000000-114000000 430.8962 536.0728 420.2111
## 6:88000000-89000000 420.6774 555.9800 412.0348
## 6:29000000-30000000 438.9376 498.4058 420.8988
## 6:72000000-73000000 417.8069 531.6456 401.8798
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 6:103000000-104000000 732.7708 527.5575 484.6419
## 6:83000000-84000000 423.4334 377.8024 408.5140
## 6:113000000-114000000 415.2436 378.6117 411.6363
## 6:88000000-89000000 400.6833 378.7970 414.4530
## 6:29000000-30000000 430.3784 388.2361 409.8253
## 6:72000000-73000000 425.2904 369.8357 401.5884
##
## [[7]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 7:44000000-45000000 913.4021 492.4502 636.3727
## 7:45000000-46000000 840.2253 469.4051 615.9672
## 7:28000000-29000000 840.1787 487.0212 595.5924
## 7:126000000-127000000 742.7552 487.9137 586.1007
## 7:24000000-25000000 817.0083 471.5682 605.5160
## 7:27000000-28000000 737.6533 481.2297 564.7892
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 7:44000000-45000000 475.6220 546.8189 476.8715
## 7:45000000-46000000 453.6958 554.3651 450.3132
## 7:28000000-29000000 450.7597 533.8741 453.7390
## 7:126000000-127000000 463.6112 544.2197 446.9845
## 7:24000000-25000000 450.0380 526.8051 445.9635
## 7:27000000-28000000 463.1396 512.0281 447.7903
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 7:44000000-45000000 456.7384 343.4653 383.2017
## 7:45000000-46000000 439.9797 349.3712 394.2967
## 7:28000000-29000000 451.0841 343.2670 384.0833
## 7:126000000-127000000 456.1263 363.6144 401.9448
## 7:24000000-25000000 441.2663 335.1345 377.2850
## 7:27000000-28000000 457.9000 364.0728 397.2726
##
## [[8]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 8:71000000-72000000 820.1949 479.3317 604.3195
## 8:70000000-71000000 791.4649 487.2550 622.6302
## 8:84000000-85000000 763.3747 490.4859 602.9816
## 8:123000000-124000000 832.1784 455.3201 607.9855
## 8:121000000-122000000 855.4251 436.0282 609.2279
## 8:122000000-123000000 799.2830 444.6141 602.8249
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 8:71000000-72000000 483.5157 520.0345 489.3554
## 8:70000000-71000000 476.6553 543.7681 469.2943
## 8:84000000-85000000 476.7209 524.9261 465.7715
## 8:123000000-124000000 440.5778 575.5549 458.8904
## 8:121000000-122000000 411.8979 562.1499 438.6183
## 8:122000000-123000000 411.4673 583.7147 429.1974
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 8:71000000-72000000 471.5609 383.7051 419.9305
## 8:70000000-71000000 467.3515 372.7480 412.8648
## 8:84000000-85000000 461.7764 377.2368 409.4179
## 8:123000000-124000000 422.8047 354.4256 398.1415
## 8:121000000-122000000 412.4206 331.7996 381.1234
## 8:122000000-123000000 411.7086 345.8901 393.4756
##
## [[9]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 9:3000000-4000000 2148.8090 2225.9700 3523.4848
## 9:35000000-36000000 584.2995 604.9200 682.9100
## 9:21000000-22000000 818.7880 488.4710 622.6952
## 9:107000000-108000000 761.6246 446.2666 553.9978
## 9:108000000-109000000 683.7316 468.6023 562.9811
## 9:119000000-120000000 652.6244 447.4163 554.9420
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 9:3000000-4000000 2291.1761 1201.9262 3676.8901
## 9:35000000-36000000 505.4214 440.0816 580.2947
## 9:21000000-22000000 459.2768 549.0218 461.0251
## 9:107000000-108000000 435.3249 535.4762 446.4656
## 9:108000000-109000000 447.0405 523.0470 442.3858
## 9:119000000-120000000 422.1249 549.5843 422.7467
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 9:3000000-4000000 2632.6836 3969.1305 2430.9215
## 9:35000000-36000000 547.0018 506.5243 477.1625
## 9:21000000-22000000 464.2496 345.5910 388.7842
## 9:107000000-108000000 427.1266 366.1855 400.7640
## 9:108000000-109000000 452.8995 371.7209 406.7953
## 9:119000000-120000000 419.7819 364.5441 404.5865
##
## [[10]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 10:80000000-81000000 1054.3486 499.3563 850.9549
## 10:79000000-80000000 846.2171 505.4167 721.1100
## 10:61000000-62000000 653.1795 461.0999 704.4317
## 10:76000000-77000000 629.4666 440.5102 647.1603
## 10:58000000-59000000 487.6430 481.9118 582.4347
## 10:127000000-128000000 631.0599 466.6341 661.8279
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 10:80000000-81000000 505.0605 567.0543 495.2112
## 10:79000000-80000000 491.2618 492.9816 483.7357
## 10:61000000-62000000 424.2613 545.5747 411.4357
## 10:76000000-77000000 422.1905 566.8071 422.4598
## 10:58000000-59000000 465.0710 496.1560 457.2492
## 10:127000000-128000000 437.2809 526.5793 424.4131
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 10:80000000-81000000 470.2036 332.5732 379.4206
## 10:79000000-80000000 493.7114 369.6147 396.8461
## 10:61000000-62000000 431.0029 356.6911 396.0631
## 10:76000000-77000000 417.5086 385.7333 420.5957
## 10:58000000-59000000 473.3513 445.4820 456.4683
## 10:127000000-128000000 432.6850 361.4107 395.5539
##
## [[11]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 11:3000000-4000000 1237.9875 865.3335 1049.8030
## 11:115000000-116000000 837.0684 478.4743 604.0749
## 11:120000000-121000000 803.8933 466.3847 613.8151
## 11:116000000-117000000 778.0745 465.8118 601.5328
## 11:119000000-120000000 744.7002 434.9447 593.4096
## 11:97000000-98000000 734.1023 485.4934 587.2819
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 11:3000000-4000000 839.9113 888.0247 796.0557
## 11:115000000-116000000 447.3931 555.1704 457.2365
## 11:120000000-121000000 448.1886 565.3584 446.1913
## 11:116000000-117000000 429.0304 553.3126 434.2813
## 11:119000000-120000000 418.0161 592.2281 434.6905
## 11:97000000-98000000 450.3907 524.7258 432.6105
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 11:3000000-4000000 820.8400 673.0449 723.2582
## 11:115000000-116000000 456.0888 344.5249 388.2304
## 11:120000000-121000000 438.6557 354.3313 397.5495
## 11:116000000-117000000 439.0637 344.1999 383.1508
## 11:119000000-120000000 411.7419 362.3273 407.9889
## 11:97000000-98000000 454.0028 345.9811 384.4971
##
## [[12]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 12:111000000-112000000 717.5805 484.3944 591.7582
## 12:112000000-113000000 808.1350 444.6258 605.1873
## 12:110000000-111000000 638.1831 465.0908 578.0960
## 12:108000000-109000000 654.0948 437.9886 546.6162
## 12:84000000-85000000 589.0285 458.5588 548.1415
## 12:76000000-77000000 574.9136 445.7248 513.9438
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 12:111000000-112000000 449.2753 565.3968 443.0060
## 12:112000000-113000000 428.8336 583.2758 435.1757
## 12:110000000-111000000 427.7428 573.8037 419.3631
## 12:108000000-109000000 418.7870 553.4746 416.1229
## 12:84000000-85000000 434.0455 538.7486 418.6669
## 12:76000000-77000000 415.9452 540.4360 420.6456
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 12:111000000-112000000 445.7464 372.7253 414.9081
## 12:112000000-113000000 407.7532 352.1211 399.0167
## 12:110000000-111000000 429.0085 378.8718 413.7751
## 12:108000000-109000000 405.4673 367.1639 408.8005
## 12:84000000-85000000 426.9684 381.3616 411.2830
## 12:76000000-77000000 420.9811 375.8359 409.0455
##
## [[13]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 13:55000000-56000000 712.8472 478.6536 585.7413
## 13:49000000-50000000 562.2181 448.8973 507.5943
## 13:58000000-59000000 499.2875 433.6469 517.8162
## 13:100000000-101000000 510.9109 445.8612 469.5510
## 13:54000000-55000000 521.8309 429.6015 519.2841
## 13:53000000-54000000 483.3632 422.7538 510.0791
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 13:55000000-56000000 458.4567 530.1202 447.8620
## 13:49000000-50000000 429.7562 518.3429 424.1347
## 13:58000000-59000000 418.7501 536.7545 401.3018
## 13:100000000-101000000 424.6591 467.0535 424.3752
## 13:54000000-55000000 417.1836 520.6012 395.7539
## 13:53000000-54000000 413.9359 547.7606 391.5433
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 13:55000000-56000000 449.5811 370.3493 403.3866
## 13:49000000-50000000 417.4878 397.2852 422.5658
## 13:58000000-59000000 410.5470 385.4278 420.1724
## 13:100000000-101000000 440.6626 392.8419 411.1079
## 13:54000000-55000000 406.6665 371.1553 403.4375
## 13:53000000-54000000 396.5155 385.1905 416.8146
##
## [[14]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 14:25000000-26000000 661.9214 513.8545 631.4491
## 14:55000000-56000000 639.7383 453.0441 525.9623
## 14:70000000-71000000 595.4652 433.8028 524.6817
## 14:30000000-31000000 524.3776 435.4514 511.1991
## 14:20000000-21000000 528.3226 454.5250 486.6728
## 14:47000000-48000000 479.6978 436.9675 499.7196
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 14:25000000-26000000 517.1328 678.7641 484.3053
## 14:55000000-56000000 439.1385 515.6797 431.8047
## 14:70000000-71000000 423.0558 523.4091 410.7902
## 14:30000000-31000000 430.6871 528.8888 412.5115
## 14:20000000-21000000 433.2582 490.9917 418.2029
## 14:47000000-48000000 427.1687 535.6509 413.1022
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 14:25000000-26000000 486.3168 455.2299 501.9783
## 14:55000000-56000000 429.1210 379.5056 411.5567
## 14:70000000-71000000 404.6055 366.8096 395.8594
## 14:30000000-31000000 419.3822 390.7617 419.5772
## 14:20000000-21000000 432.3686 390.5862 413.0558
## 14:47000000-48000000 418.3413 400.2528 426.9898
##
## [[15]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 15:76000000-77000000 799.5838 464.9193 601.8386
## 15:79000000-80000000 769.9555 468.7972 612.8327
## 15:99000000-100000000 685.0961 477.9520 571.4292
## 15:78000000-79000000 742.2213 433.0272 601.1429
## 15:85000000-86000000 683.1129 449.8989 572.0638
## 15:100000000-101000000 675.9559 460.9830 570.6570
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 15:76000000-77000000 446.8149 554.3566 456.4729
## 15:79000000-80000000 450.5424 550.6154 434.5091
## 15:99000000-100000000 441.7834 534.1638 430.6572
## 15:78000000-79000000 426.2707 563.9949 412.0474
## 15:85000000-86000000 438.5070 558.4343 422.6834
## 15:100000000-101000000 435.5627 547.3643 422.9618
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 15:76000000-77000000 441.3371 357.0129 396.4992
## 15:79000000-80000000 436.5031 345.3895 386.1521
## 15:99000000-100000000 444.8720 355.0269 394.3795
## 15:78000000-79000000 399.4051 348.5489 391.1013
## 15:85000000-86000000 422.8255 358.6869 399.0836
## 15:100000000-101000000 431.6858 357.7735 396.5437
##
## [[16]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 16:3000000-4000000 506.3938 522.8690 525.5456
## 16:17000000-18000000 662.1926 482.7770 590.3591
## 16:91000000-92000000 651.9040 463.6293 578.9675
## 16:4000000-5000000 603.8681 454.7316 531.2758
## 16:18000000-19000000 597.3170 454.4939 530.4998
## 16:10000000-11000000 565.0953 440.9077 543.0420
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 16:3000000-4000000 526.6053 541.9316 540.4763
## 16:17000000-18000000 475.9008 552.1963 443.4617
## 16:91000000-92000000 461.1098 568.2985 439.2174
## 16:4000000-5000000 436.1778 531.0448 418.7007
## 16:18000000-19000000 442.1402 500.4936 428.6321
## 16:10000000-11000000 423.8103 551.4889 409.5456
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 16:3000000-4000000 538.6662 542.5613 547.0873
## 16:17000000-18000000 453.5532 389.6533 427.3526
## 16:91000000-92000000 429.0294 384.6022 424.2399
## 16:4000000-5000000 427.9093 380.8968 412.3015
## 16:18000000-19000000 439.0929 388.3369 415.3219
## 16:10000000-11000000 415.5475 381.5663 418.6510
##
## [[17]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 17:40000000-41000000 1565.4417 863.8953 1242.9247
## 17:56000000-57000000 798.2787 478.8679 591.5480
## 17:24000000-25000000 741.5560 475.7617 605.3823
## 17:25000000-26000000 733.1955 440.4673 584.2352
## 17:28000000-29000000 733.9074 469.8923 591.1810
## 17:27000000-28000000 817.4956 447.6969 590.2330
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 17:40000000-41000000 1106.4166 754.0853 907.5666
## 17:56000000-57000000 470.0902 526.2214 467.4717
## 17:24000000-25000000 465.4606 558.4855 453.0851
## 17:25000000-26000000 429.3913 584.2388 436.5468
## 17:28000000-29000000 432.0895 557.9188 433.0789
## 17:27000000-28000000 412.4023 552.9547 429.9189
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 17:40000000-41000000 853.9284 650.6822 684.2856
## 17:56000000-57000000 467.3431 375.2378 405.1626
## 17:24000000-25000000 447.0537 372.6310 409.5038
## 17:25000000-26000000 416.1762 372.6245 416.7732
## 17:28000000-29000000 444.9678 351.5198 395.1402
## 17:27000000-28000000 421.8471 336.3664 385.0700
##
## [[18]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 18:37000000-38000000 780.5153 478.0884 506.6004
## 18:34000000-35000000 524.1742 450.2613 506.8527
## 18:75000000-76000000 588.3039 428.9896 525.8170
## 18:67000000-68000000 521.0130 447.9073 496.2639
## 18:35000000-36000000 512.1059 458.0560 489.4901
## 18:36000000-37000000 549.5693 451.0408 502.0935
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 18:37000000-38000000 477.8773 452.0720 517.2130
## 18:34000000-35000000 433.4263 518.1085 417.5658
## 18:75000000-76000000 408.3016 549.6269 404.2002
## 18:67000000-68000000 433.3566 490.7275 420.5950
## 18:35000000-36000000 436.0138 483.8801 419.9706
## 18:36000000-37000000 430.6666 486.0830 412.5453
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 18:37000000-38000000 480.4211 423.3696 431.8308
## 18:34000000-35000000 429.2126 391.8733 420.0419
## 18:75000000-76000000 403.3023 371.8802 408.6477
## 18:67000000-68000000 430.0328 390.4562 415.1468
## 18:35000000-36000000 439.2511 395.1562 411.0952
## 18:36000000-37000000 424.5451 382.6747 405.4745
##
## [[19]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 19:5000000-6000000 847.8655 499.6057 617.5613
## 19:4000000-5000000 771.6717 478.5561 569.0745
## 19:37000000-38000000 487.8167 493.4284 546.5168
## 19:6000000-7000000 814.9023 462.4484 605.4358
## 19:7000000-8000000 607.2242 473.2362 505.7327
## 19:45000000-46000000 627.0979 448.9245 535.4502
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 19:5000000-6000000 480.2065 556.9217 469.7162
## 19:4000000-5000000 452.4738 531.4794 459.6455
## 19:37000000-38000000 482.5603 569.7174 463.9784
## 19:6000000-7000000 455.3033 535.9833 439.6013
## 19:7000000-8000000 438.0190 461.0498 447.7608
## 19:45000000-46000000 430.2893 514.3248 420.8608
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 19:5000000-6000000 463.7416 361.1409 403.8640
## 19:4000000-5000000 450.7802 373.9084 410.1245
## 19:37000000-38000000 481.2747 451.6870 486.9462
## 19:6000000-7000000 432.0896 332.1052 374.9043
## 19:7000000-8000000 467.6346 388.3109 404.2937
## 19:45000000-46000000 429.0294 359.6100 392.8263100 kbp bins
x <- read.table("ERP011529.1e5_fmt.tsv",header=T,row.names=1)
x <- x[grep("X",rownames(x),invert=TRUE),]
x <- x[grep("Y",rownames(x),invert=TRUE),]
x <- x[grep("M",rownames(x),invert=TRUE),]
x <- x[grep("J",rownames(x),invert=TRUE),]
x <- x[grep("G",rownames(x),invert=TRUE),]
x <- x[which(rowSums(x)>=10),]
x <- sweep(x, 2, colSums(x), FUN="/")*1000000
mysd <- apply(x,1,sd)
mean <- apply(x,1,mean)
y <- data.frame(log10(mean),mysd/mean)
colnames(y) = c("logMean","cv")
yy <- y[order(-y$cv),]
yy <- subset(yy,cv>0.38 & logMean > 1)
write.table(yy,file="ERP011529.1e5_regions.tsv")
yy %>% kbl() %>% kable_paper("hover", full_width = F)| logMean | cv | |
|---|---|---|
| 6:103600000-103700000 | 2.604641 | 0.8086995 |
| 6:114500000-114600000 | 1.305968 | 0.6331736 |
| 2:98400000-98500000 | 4.101792 | 0.5388632 |
| 17:40100000-40200000 | 2.776084 | 0.5148306 |
| 10:81100000-81200000 | 1.814078 | 0.5064536 |
| 10:79800000-79900000 | 1.803675 | 0.4879893 |
| 10:79700000-79800000 | 1.775478 | 0.4812383 |
| 17:56700000-56800000 | 1.759860 | 0.4778195 |
| 4:137200000-137300000 | 1.759118 | 0.4690299 |
| 19:5700000-5800000 | 1.774782 | 0.4685897 |
| 10:80100000-80200000 | 1.784199 | 0.4679228 |
| 4:140700000-140800000 | 1.817482 | 0.4677833 |
| 10:79900000-80000000 | 1.751636 | 0.4548343 |
| 8:71200000-71300000 | 1.779619 | 0.4535690 |
| 8:121200000-121300000 | 1.696105 | 0.4442537 |
| 8:121000000-121100000 | 1.720923 | 0.4431564 |
| 7:19100000-19200000 | 1.782312 | 0.4429117 |
| 7:59400000-59500000 | 1.402028 | 0.4390919 |
| 5:114500000-114600000 | 1.751529 | 0.4368539 |
| 5:125100000-125200000 | 1.732961 | 0.4354800 |
| 5:115700000-115800000 | 1.788406 | 0.4344887 |
| 10:80700000-80800000 | 1.758241 | 0.4313488 |
| 10:80900000-81000000 | 1.751297 | 0.4300790 |
| 11:120100000-120200000 | 1.753394 | 0.4295051 |
| 2:31300000-31400000 | 1.733281 | 0.4290332 |
| 17:56400000-56500000 | 1.755581 | 0.4285788 |
| 10:80300000-80400000 | 1.746633 | 0.4284657 |
| 9:35200000-35300000 | 2.193756 | 0.4271380 |
| 10:81200000-81300000 | 1.752012 | 0.4256706 |
| 17:26000000-26100000 | 1.760665 | 0.4243019 |
| 10:81300000-81400000 | 1.752435 | 0.4203738 |
| 1:75400000-75500000 | 1.726695 | 0.4200642 |
| 11:115700000-115800000 | 1.734441 | 0.4188875 |
| 5:114700000-114800000 | 1.712995 | 0.4178668 |
| 8:71900000-72000000 | 1.771567 | 0.4173932 |
| 17:57000000-57100000 | 1.742032 | 0.4165555 |
| 7:27000000-27100000 | 1.758960 | 0.4162848 |
| 11:99500000-99600000 | 1.778988 | 0.4161720 |
| 8:121100000-121200000 | 1.715457 | 0.4159605 |
| 10:80800000-80900000 | 1.758256 | 0.4158568 |
| 7:44100000-44200000 | 1.718090 | 0.4150485 |
| 18:37800000-37900000 | 1.789537 | 0.4150053 |
| 11:119900000-120000000 | 1.762394 | 0.4146531 |
| 12:111200000-111300000 | 1.774084 | 0.4130621 |
| 10:79500000-79600000 | 1.801192 | 0.4123746 |
| 9:107500000-107600000 | 1.732760 | 0.4091235 |
| 10:80200000-80300000 | 1.759559 | 0.4059980 |
| 10:81000000-81100000 | 1.756698 | 0.4053753 |
| 8:72100000-72200000 | 1.750547 | 0.4037457 |
| 7:30200000-30300000 | 1.753637 | 0.4018879 |
| 7:19200000-19300000 | 1.756986 | 0.3995277 |
| 5:139700000-139800000 | 1.738178 | 0.3986980 |
| 11:117900000-118000000 | 1.694597 | 0.3982203 |
| 2:166400000-166500000 | 1.707026 | 0.3976250 |
| 7:28700000-28800000 | 1.759365 | 0.3969948 |
| 19:6900000-7000000 | 1.756776 | 0.3967747 |
| 7:25000000-25100000 | 1.741206 | 0.3938193 |
| 4:152400000-152500000 | 1.728549 | 0.3920597 |
| 10:80600000-80700000 | 1.760489 | 0.3915387 |
| 4:141000000-141100000 | 1.699205 | 0.3910751 |
| 5:24600000-24700000 | 1.743402 | 0.3901147 |
| 15:76500000-76600000 | 1.739295 | 0.3896748 |
| 7:24600000-24700000 | 1.700546 | 0.3894201 |
| 11:119100000-119200000 | 1.719783 | 0.3888724 |
| 5:142400000-142500000 | 1.749845 | 0.3878642 |
| 4:137400000-137500000 | 1.718977 | 0.3875702 |
| 17:34100000-34200000 | 1.734111 | 0.3873039 |
| 11:99600000-99700000 | 1.690492 | 0.3866968 |
| 13:55600000-55700000 | 1.752371 | 0.3864869 |
| 7:19000000-19100000 | 1.762851 | 0.3862144 |
| 5:142700000-142800000 | 1.735164 | 0.3859169 |
| 9:3000000-3100000 | 3.356678 | 0.3858033 |
| 9:21700000-21800000 | 1.740296 | 0.3853730 |
| 9:107400000-107500000 | 1.731319 | 0.3837529 |
| 10:80000000-80100000 | 1.755217 | 0.3836025 |
| 17:27300000-27400000 | 1.700330 | 0.3831684 |
| 8:71100000-71200000 | 1.752856 | 0.3830693 |
| 15:98700000-98800000 | 1.737612 | 0.3830244 |
| 10:80400000-80500000 | 1.768241 | 0.3812511 |
| 7:24900000-25000000 | 1.747325 | 0.3809469 |
| 15:78800000-78900000 | 1.748174 | 0.3807026 |
| 17:46900000-47000000 | 1.717431 | 0.3803577 |
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV",xlim=c(1,4.5),ylim=c(0.38,0.9))
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
zz<-x[which(rownames(x) %in% rownames(yy)),]
heatmap.2(as.matrix(zz),margin=c(8, 22),cexRow=0.65,trace="none",
cexCol=0.8,col=my_palette,scale="row")
heatmap.2(cor(t(zz)),trace="none",scale="none",margins=c(12,12),
cexRow=0.8, cexCol=0.8)
for (i in 1:19){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymax = max(chr)
plot(chr[,1], xaxt = "n", pch=19, col="gray",
ylim=c(0,mymax),ylab="RPM of 1Mbp bins of chr",main=mychr)
axis(1, at=chr[,1], labels=rownames(chr), xlab="chr", las=1)
points( chr[,1], xaxt = "n", las=1, pch=19, col="gray" )
points( chr[,2], xaxt = "n", las=1, pch=19, col="lightblue" )
points( chr[,3], xaxt = "n", las=1, pch=19, col="lightgreen" )
points( chr[,4], xaxt = "n", las=1, pch=19, col="pink" )
points( chr[,5], xaxt = "n", las=1, pch=19, col="orange" )
points( chr[,6], xaxt = "n", las=1, pch=19, col="black" )
points( chr[,7], xaxt = "n", las=1, pch=19, col="yellow" )
points( chr[,8], xaxt = "n", las=1, pch=19, col="red" )
points( chr[,9], xaxt = "n", las=1, pch=19, col="blue" )
grid()
}
lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymedian <- median(rowMeans(chr))
mymax <- max(rowMeans(chr))
rat <- mymax / mymedian
return(c(mymax,mymedian,rat))
})## [[1]]
## [1] 174.263914 40.605124 4.291673
##
## [[2]]
## [1] 12641.31589 40.71214 310.50485
##
## [[3]]
## [1] 95.408231 40.368340 2.363442
##
## [[4]]
## [1] 129.448964 41.011888 3.156377
##
## [[5]]
## [1] 100.849169 41.183349 2.448785
##
## [[6]]
## [1] 402.38454 40.33295 9.97657
##
## [[7]]
## [1] 65.779710 41.306392 1.592483
##
## [[8]]
## [1] 60.20314 40.53456 1.48523
##
## [[9]]
## [1] 2273.40954 41.52713 54.74516
##
## [[10]]
## [1] 82.820581 41.394417 2.000767
##
## [[11]]
## [1] 472.28552 42.48272 11.11712
##
## [[12]]
## [1] 59.440654 40.272483 1.475962
##
## [[13]]
## [1] 95.853801 40.389391 2.373242
##
## [[14]]
## [1] 133.81951 40.00224 3.34530
##
## [[15]]
## [1] 55.998246 40.407541 1.385837
##
## [[16]]
## [1] 120.565874 39.774359 3.031246
##
## [[17]]
## [1] 597.15109 41.32504 14.45010
##
## [[18]]
## [1] 62.690962 40.514601 1.547367
##
## [[19]]
## [1] 81.032426 41.169712 1.968253lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
top <- head(chr[order(-rowMeans(chr)),])
return(top)
})## [[1]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 1:88100000-88200000 161.90200 152.22600 197.36110
## 1:88200000-88300000 97.18790 106.70539 123.64607
## 1:75400000-75500000 107.61059 44.68898 64.67635
## 1:185000000-185100000 76.41893 48.96813 62.41668
## 1:36500000-36600000 88.24264 50.90859 62.61966
## 1:75100000-75200000 88.38698 46.88712 57.87436
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 1:88100000-88200000 160.62020 231.33910 157.60956
## 1:88200000-88300000 108.54837 141.75776 102.78296
## 1:75400000-75500000 49.57774 52.56930 48.77234
## 1:185000000-185100000 49.26135 60.35007 48.70046
## 1:36500000-36600000 47.38770 58.38566 45.24209
## 1:75100000-75200000 51.05282 55.61841 47.90563
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 1:88100000-88200000 148.34930 172.72846 186.23952
## 1:88200000-88300000 103.74856 107.50046 116.69993
## 1:75400000-75500000 42.57325 32.72475 36.47152
## 1:185000000-185100000 45.79805 40.55576 44.82227
## 1:36500000-36600000 45.99412 35.75422 40.28645
## 1:75100000-75200000 43.96246 37.24289 41.65166
##
## [[2]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 2:98400000-98500000 6331.80517 7003.96563 10715.87141 9163.86908
## 2:22600000-22700000 82.93577 68.89194 75.20488 74.95828
## 2:32800000-32900000 106.84640 49.89736 63.84908 46.89053
## 2:32600000-32700000 95.18827 49.23753 68.01223 49.40927
## 2:31300000-31400000 111.29568 43.93545 61.40940 42.67071
## 2:25100000-25200000 93.02731 46.82465 68.87397 50.99530
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 2:98400000-98500000 8231.21262 14511.04656 11350.80741 27409.59126
## 2:22600000-22700000 50.44689 57.54933 62.41017 48.80373
## 2:32800000-32900000 61.05897 50.88203 46.57400 37.08654
## 2:32600000-32700000 59.51306 48.90340 45.78553 35.84869
## 2:31300000-31400000 60.49954 49.47416 42.78601 35.36658
## 2:25100000-25200000 59.23121 47.57586 43.40761 35.33726
## ERX1059314.bam
## 2:98400000-98500000 19053.67385
## 2:22600000-22700000 50.94976
## 2:32800000-32900000 42.35979
## 2:32600000-32700000 40.35663
## 2:31300000-31400000 39.55600
## 2:25100000-25200000 40.49060
##
## [[3]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 3:5900000-6000000 146.41018 116.20463 111.16810
## 3:106100000-106200000 87.13456 83.01391 67.80924
## 3:106000000-106100000 80.12100 86.23498 68.94674
## 3:3000000-3100000 48.57697 48.97594 87.20794
## 3:106200000-106300000 45.63908 60.65770 48.46417
## 3:88400000-88500000 82.43480 50.11601 62.67711
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 3:5900000-6000000 122.96643 61.52445 79.34806
## 3:106100000-106200000 82.53506 61.40487 91.11413
## 3:106000000-106100000 75.90333 61.08886 81.32246
## 3:3000000-3100000 50.69946 66.67889 50.29013
## 3:106200000-106300000 54.86176 39.70242 57.36753
## 3:88400000-88500000 49.38051 54.98211 43.00556
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 3:5900000-6000000 99.82706 59.30588 61.91929
## 3:106100000-106200000 89.17645 84.42114 78.42940
## 3:106000000-106100000 92.87267 75.55425 72.56984
## 3:3000000-3100000 49.62776 51.78433 55.69291
## 3:106200000-106300000 65.42638 53.40657 50.79028
## 3:88400000-88500000 45.94406 35.63369 39.75377
##
## [[4]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 4:146500000-146600000 115.51995 110.07093 119.42164
## 4:146400000-146500000 96.13077 73.72941 88.55608
## 4:147500000-147600000 99.94323 80.81186 82.60434
## 4:147600000-147700000 65.15138 90.98265 77.18113
## 4:147400000-147500000 74.79715 74.79529 83.70736
## 4:147800000-147900000 88.59926 64.92904 72.01070
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 4:146500000-146600000 134.59456 134.24603 135.44724
## 4:146400000-146500000 92.99626 97.60979 102.82101
## 4:147500000-147600000 92.24845 90.09806 96.76675
## 4:147600000-147700000 97.95568 80.77139 100.33082
## 4:147400000-147500000 83.85812 98.31869 86.11683
## 4:147800000-147900000 79.12880 66.58067 79.39879
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 4:146500000-146600000 122.38817 147.28746 146.06471
## 4:146400000-146500000 88.65081 107.19751 106.82449
## 4:147500000-147600000 88.81768 97.57163 95.23615
## 4:147600000-147700000 95.45084 105.45801 97.16276
## 4:147400000-147500000 83.51114 92.12185 92.70669
## 4:147800000-147900000 71.25439 77.76608 73.51400
##
## [[5]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 5:146100000-146200000 157.43149 115.50966 136.33086
## 5:15000000-15100000 59.43270 60.07596 59.23016
## 5:115700000-115800000 126.25681 57.98324 73.55800
## 5:114500000-114600000 116.84455 46.36004 66.70239
## 5:142400000-142500000 108.48516 49.67482 67.33816
## 5:145500000-145600000 50.48744 49.30391 47.38413
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 5:146100000-146200000 125.51804 84.89665 74.86232
## 5:15000000-15100000 70.33169 75.94150 80.16403
## 5:115700000-115800000 51.66916 60.04260 54.60252
## 5:114500000-114600000 44.81144 61.20843 50.68332
## 5:142400000-142500000 46.37281 59.36787 52.10810
## 5:145500000-145600000 57.84891 53.32517 60.08603
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 5:146100000-146200000 90.63241 57.84327 64.61781
## 5:15000000-15100000 72.26396 85.20619 88.19320
## 5:115700000-115800000 53.16545 35.51317 40.11102
## 5:114500000-114600000 45.31829 35.72165 40.24180
## 5:142400000-142500000 47.22063 35.15810 40.20033
## 5:145500000-145600000 54.76325 67.01310 64.82514
##
## [[6]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 6:103600000-103700000 791.24586 915.95630 749.49036
## 6:83000000-83100000 96.01190 49.60063 69.00419
## 6:29400000-29500000 103.52642 47.35564 60.27191
## 6:83100000-83200000 97.01383 48.94080 64.17845
## 6:48400000-48500000 90.72201 45.62602 62.04134
## 6:88800000-88900000 89.01956 47.83978 61.59707
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 6:103600000-103700000 179.32382 97.76353 255.83068
## 6:83000000-83100000 53.78523 55.89172 47.89295
## 6:29400000-29500000 44.47451 57.30524 51.60921
## 6:83100000-83200000 49.09289 52.50098 48.22694
## 6:48400000-48500000 44.83609 59.63691 46.96705
## 6:88800000-88900000 44.31015 57.39919 47.30950
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 6:103600000-103700000 368.04894 140.33597 123.46538
## 6:83000000-83100000 44.60491 34.23949 39.89730
## 6:29400000-29500000 44.97203 36.61746 41.06475
## 6:83100000-83200000 46.40296 33.91048 37.77931
## 6:48400000-48500000 43.40343 37.28198 41.38053
## 6:88800000-88900000 43.34086 36.21027 40.05998
##
## [[7]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 7:42800000-42900000 57.70903 74.72501 65.77937 66.60904
## 7:19100000-19200000 122.95381 55.53131 78.97738 58.37485
## 7:22500000-22600000 64.45088 51.35758 47.52584 57.70921
## 7:44500000-44600000 109.05831 54.34049 73.25160 54.26186
## 7:19000000-19100000 110.31922 54.18042 70.55149 56.35739
## 7:28700000-28800000 112.67971 55.19945 67.64073 50.36253
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 7:42800000-42900000 57.31805 65.27781 75.88926 64.43969
## 7:19100000-19200000 54.62339 54.21356 50.72078 32.93323
## 7:22500000-22600000 50.39137 66.55039 66.25240 67.43006
## 7:44500000-44600000 59.08175 54.06559 49.61108 36.20376
## 7:19000000-19100000 54.58069 52.96635 51.04200 33.72480
## 7:28700000-28800000 54.03834 52.81415 51.06704 33.85836
## ERX1059314.bam
## 7:42800000-42900000 64.27013
## 7:19100000-19200000 36.87024
## 7:22500000-22600000 64.60186
## 7:44500000-44600000 41.13492
## 7:19000000-19100000 37.58474
## 7:28700000-28800000 39.47945
##
## [[8]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 8:71200000-71300000 125.69641 50.95153 74.96742 56.12729
## 8:19700000-19800000 33.44177 46.72705 52.47030 58.76108
## 8:71900000-72000000 117.28608 52.77876 74.80656 56.00813
## 8:84900000-85000000 108.06486 54.15309 73.51204 57.76262
## 8:71300000-71400000 101.42490 47.84368 76.46493 54.11394
## 8:71100000-71200000 108.79933 50.69385 67.20794 50.79397
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 8:71200000-71300000 58.19349 55.76941 47.71708 32.74756
## 8:19700000-19800000 75.85609 62.74957 58.24671 71.64851
## 8:71900000-72000000 54.05542 53.65549 50.97943 33.74761
## 8:84900000-85000000 56.47677 52.09119 50.14507 35.71513
## 8:71300000-71400000 58.16360 50.88203 44.91780 36.26565
## 8:71100000-71200000 57.22410 52.52666 47.32910 34.42842
## ERX1059314.bam
## 8:71200000-71300000 39.65807
## 8:19700000-19800000 72.55389
## 8:71900000-72000000 38.55761
## 8:84900000-85000000 39.38376
## 8:71300000-71400000 40.08231
## 8:71100000-71200000 40.44275
##
## [[9]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 9:3000000-3100000 1741.54923 1779.52427 3155.15784
## 9:35200000-35300000 170.82178 190.70319 301.22931
## 9:123200000-123300000 90.18283 71.76162 85.20487
## 9:3300000-3400000 51.59551 61.95004 51.97624
## 9:21700000-21800000 103.89578 50.81488 70.83873
## 9:21000000-21100000 94.32643 50.85002 67.51434
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 9:3000000-3100000 1883.46917 866.49670 3256.33976
## 9:35200000-35300000 115.23763 79.83189 188.81523
## 9:123200000-123300000 81.86942 70.14223 55.57493
## 9:3300000-3400000 58.95831 53.22695 61.75602
## 9:21700000-21800000 47.42058 54.66610 48.53135
## 9:21000000-21100000 48.46012 57.14296 49.30927
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 9:3000000-3100000 2158.84944 3572.59137 2046.70808
## 9:35200000-35300000 127.25667 132.71996 99.42747
## 9:123200000-123300000 63.06097 50.92110 55.57170
## 9:3300000-3400000 69.40211 59.70004 57.46641
## 9:21700000-21800000 48.24690 33.13846 37.37103
## 9:21000000-21100000 49.62776 35.09621 39.51772
##
## [[10]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 10:58000000-58100000 88.90494 90.38138 88.95056
## 10:81100000-81200000 138.19515 51.49814 98.52543
## 10:79800000-79900000 133.36802 53.92273 93.37798
## 10:79500000-79600000 114.99775 57.35073 97.45688
## 10:80100000-80200000 121.47638 51.60356 92.92605
## 10:79700000-79800000 124.49068 49.44446 86.18151
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 10:58000000-58100000 82.00912 65.81198 84.81888
## 10:81100000-81200000 59.44316 57.61271 59.29965
## 10:79800000-79900000 53.91260 58.77000 56.71221
## 10:79500000-79600000 58.63371 59.82054 53.79501
## 10:80100000-80200000 56.33273 55.08887 53.71468
## 10:79700000-79800000 53.54691 55.16147 53.26230
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 10:58000000-58100000 91.58775 78.42735 74.49325
## 10:81100000-81200000 50.63317 33.54238 37.82078
## 10:79800000-79900000 50.50801 33.54890 38.56718
## 10:79500000-79600000 51.90557 35.55877 39.90368
## 10:80100000-80200000 47.81303 32.00811 36.60868
## 10:79700000-79800000 45.75633 32.00811 36.83515
##
## [[11]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 11:3100000-3200000 713.71036 479.41293 583.07908
## 11:108900000-109000000 163.88464 115.95085 130.21443
## 11:99500000-99600000 123.92179 50.23704 59.62847
## 11:105900000-106000000 82.50273 58.14331 76.35003
## 11:119900000-120000000 113.82600 47.37517 74.47719
## 11:88800000-88900000 77.32322 58.66649 66.26195
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 11:3100000-3200000 462.98030 426.60139 425.25705
## 11:108900000-109000000 133.41120 82.24043 77.11153
## 11:99500000-99600000 58.58851 51.06610 63.75156
## 11:105900000-106000000 61.20176 58.66751 52.40405
## 11:119900000-120000000 51.06104 61.64402 51.90516
## 11:88800000-88900000 52.28138 63.18138 51.62613
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 11:3100000-3200000 452.05653 342.31670 365.15538
## 11:108900000-109000000 94.55391 56.82042 65.48223
## 11:99500000-99600000 47.92150 41.59165 44.33424
## 11:105900000-106000000 52.26851 43.20410 47.80149
## 11:119900000-120000000 44.70504 34.74766 41.01690
## 11:88800000-88900000 57.38314 43.70250 46.70740
##
## [[12]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 12:111200000-111300000 119.53194 50.03402 69.17271
## 12:114700000-114800000 47.62598 63.11743 53.78397
## 12:3100000-3200000 81.39890 80.47609 84.43505
## 12:111700000-111800000 103.88305 50.99448 71.12215
## 12:78300000-78400000 61.02901 62.74652 54.36229
## 12:112600000-112700000 100.83903 51.27950 68.93525
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 12:111200000-111300000 46.19202 64.01412 55.30012
## 12:114700000-114800000 61.37022 45.56576 62.96941
## 12:3100000-3200000 40.66558 45.51878 43.12817
## 12:111700000-111800000 51.09802 58.91093 51.08073
## 12:78300000-78400000 61.61264 44.77999 53.82037
## 12:112600000-112700000 49.49967 59.58993 48.23540
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 12:111200000-111300000 49.01451 38.09310 43.61335
## 12:114700000-114800000 68.05044 60.58608 58.62747
## 12:3100000-3200000 42.88196 52.53356 49.11247
## 12:111700000-111800000 47.48346 35.37309 40.76491
## 12:78300000-78400000 63.99962 47.81671 47.14759
## 12:112600000-112700000 45.05130 34.42842 39.35186
##
## [[13]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 13:12800000-12900000 83.89525 96.60489 79.68209
## 13:120000000-120100000 88.40397 63.76946 79.18036
## 13:55600000-55700000 107.16481 48.45276 72.72690
## 13:45000000-45100000 69.85539 49.95983 58.77823
## 13:55800000-55900000 73.91409 54.64894 63.94483
## 13:55700000-55800000 77.54823 53.69237 59.06164
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 13:12800000-12900000 101.53452 74.94649 108.41021
## 13:120000000-120100000 60.43751 56.85257 60.54264
## 13:55600000-55700000 52.20742 57.27107 51.28367
## 13:45000000-45100000 53.35380 57.13442 48.82307
## 13:55800000-55900000 51.02817 57.30524 45.93546
## 13:55700000-55800000 50.31323 54.68318 47.63082
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 13:12800000-12900000 107.88282 110.38660 99.34135
## 13:120000000-120100000 59.48573 51.49116 44.22260
## 13:55600000-55700000 45.25571 34.79326 39.72187
## 13:45000000-45100000 47.05376 44.49081 47.92270
## 13:55800000-55900000 49.74875 38.87164 41.85900
## 13:55700000-55800000 48.60984 38.96611 41.55597
##
## [[14]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 14:25900000-26000000 162.88695 136.34708 145.55338
## 14:43100000-43200000 50.54263 62.55521 64.04441
## 14:3000000-3100000 55.11928 56.86269 64.19760
## 14:25800000-25900000 82.56641 65.26872 78.63268
## 14:26000000-26100000 57.31420 58.38148 64.08270
## 14:54800000-54900000 94.98873 53.99301 68.60587
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 14:25900000-26000000 140.91812 145.02893 120.41304
## 14:43100000-43200000 88.99422 86.32298 97.54044
## 14:3000000-3100000 78.64395 42.61914 97.65459
## 14:25800000-25900000 66.19815 82.37708 57.37176
## 14:26000000-26100000 55.58903 67.24259 51.02577
## 14:54800000-54900000 52.07594 54.63193 48.91608
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 14:25900000-26000000 129.89325 107.29198 116.04285
## 14:43100000-43200000 82.58918 114.54641 113.01259
## 14:3000000-3100000 79.05566 104.18759 48.61806
## 14:25800000-25900000 62.31422 52.25016 60.04372
## 14:26000000-26100000 57.80033 49.63113 53.13474
## 14:54800000-54900000 49.45672 34.80303 38.59589
##
## [[15]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 15:78800000-78900000 105.2374 47.18385 70.71618
## 15:76000000-76100000 104.2439 48.40981 68.03521
## 15:85000000-85100000 73.9056 57.92857 68.30714
## 15:76500000-76600000 106.7275 47.28537 64.09802
## 15:101100000-101200000 98.8861 51.89248 66.43429
## 15:98700000-98800000 103.8873 50.80707 67.23092
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 15:78800000-78900000 50.20640 59.90594 51.40205
## 15:76000000-76100000 47.36305 58.24901 50.56494
## 15:85000000-85100000 59.36098 57.91164 45.44080
## 15:76500000-76600000 47.46166 58.35577 50.16752
## 15:101100000-101200000 49.44214 56.14367 49.26699
## 15:98700000-98800000 49.14220 53.89741 50.40428
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 15:78800000-78900000 45.44345 34.74766 39.14133
## 15:76000000-76100000 46.28615 36.08974 40.64689
## 15:85000000-85100000 50.44127 39.12898 43.21145
## 15:76500000-76600000 46.02750 35.01151 38.65011
## 15:101100000-101200000 46.04836 35.16462 39.50816
## 15:98700000-98800000 47.17057 33.11565 36.21953
##
## [[16]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 16:3100000-3200000 98.67807 117.92254 109.30291
## 16:57200000-57300000 133.89446 106.35400 127.42623
## 16:91300000-91400000 135.06622 78.85579 107.56028
## 16:3000000-3100000 58.71521 76.91144 73.97163
## 16:17000000-17100000 91.37581 61.25116 78.84333
## 16:17300000-17400000 93.11647 50.74851 62.17156
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 16:3100000-3200000 123.36088 116.34001 128.97442
## 16:57200000-57300000 122.57198 79.72086 74.75240
## 16:91300000-91400000 84.39638 75.19844 65.05797
## 16:3000000-3100000 83.04456 64.76999 83.27149
## 16:17000000-17100000 65.08465 59.71804 51.76564
## 16:17300000-17400000 48.16017 60.55505 50.94544
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 16:3100000-3200000 127.03139 132.83397 130.64868
## 16:57200000-57300000 84.42477 59.36777 66.56993
## 16:91300000-91400000 66.36504 43.91424 52.09807
## 16:3000000-3100000 79.33517 86.48313 79.66383
## 16:17000000-17100000 57.12866 38.98891 45.11253
## 16:17300000-17400000 47.26652 38.90096 42.18116
##
## [[17]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 17:40100000-40200000 1216.63608 474.94638 934.31220
## 17:36500000-36600000 145.01766 94.50436 125.86744
## 17:13400000-13500000 57.26750 59.77142 76.67941
## 17:13500000-13600000 46.52215 44.29465 63.22863
## 17:13600000-13700000 43.45265 41.97157 66.49940
## 17:23700000-23800000 77.48031 57.05010 63.61545
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 17:40100000-40200000 728.84944 474.31518 509.00206
## 17:36500000-36600000 102.50832 78.70876 72.84142
## 17:13400000-13500000 63.33016 96.48666 63.74311
## 17:13500000-13600000 54.91106 93.23258 58.83459
## 17:13600000-13700000 52.88949 96.08097 58.58092
## 17:23700000-23800000 57.68045 59.16716 59.81968
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 17:40100000-40200000 433.32931 270.97113 331.99801
## 17:36500000-36600000 76.09785 53.71928 60.00863
## 17:13400000-13500000 62.49777 67.90891 76.08175
## 17:13500000-13600000 53.46165 67.71021 74.04031
## 17:13600000-13700000 50.64568 66.54403 74.09773
## 17:23700000-23800000 58.98095 52.86908 54.87634
##
## [[18]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 18:73600000-73700000 77.84117 62.93002 81.85750
## 18:37800000-37900000 126.99128 49.28048 63.64609
## 18:37000000-37100000 92.38200 51.40053 58.94291
## 18:37100000-37200000 107.47474 50.18628 60.99194
## 18:37600000-37700000 91.36308 52.19311 54.40825
## 18:3000000-3100000 45.33765 47.82026 47.36881
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 18:73600000-73700000 70.80010 60.36288 53.34263
## 18:37800000-37900000 54.49607 59.09456 62.60582
## 18:37000000-37100000 53.66196 50.18212 56.47546
## 18:37100000-37200000 54.01122 43.76362 57.08004
## 18:37600000-37700000 52.26494 46.62056 58.16236
## 18:3000000-3100000 49.98452 53.28674 55.47769
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 18:73600000-73700000 54.25846 48.83631 53.98959
## 18:37800000-37900000 48.39708 43.11289 46.72016
## 18:37000000-37100000 52.05575 44.60157 45.26883
## 18:37100000-37200000 50.36617 39.26905 40.09507
## 18:37600000-37700000 54.14583 43.53637 45.69945
## 18:3000000-3100000 52.79416 54.70956 54.84444
##
## [[19]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 19:37200000-37300000 77.16189 78.15692 90.76213
## 19:37300000-37400000 74.44053 78.25843 85.21636
## 19:36900000-37000000 63.29186 70.75040 65.98619
## 19:5700000-5800000 127.36064 49.17116 73.09840
## 19:6900000-7000000 112.17875 51.13504 67.29603
## 19:6300000-6400000 106.68507 48.73778 69.05015
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 19:37200000-37300000 74.21869 104.19484 73.81805
## 19:37300000-37400000 73.86121 78.26890 67.68345
## 19:36900000-37000000 68.31834 64.26608 63.36260
## 19:5700000-5800000 52.95524 58.62908 55.20288
## 19:6900000-7000000 53.46474 56.34439 50.97927
## 19:6300000-6400000 50.56798 59.25257 49.95190
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 19:37200000-37300000 75.83920 74.72033 80.41980
## 19:37300000-37400000 76.51503 63.51456 66.86020
## 19:36900000-37000000 67.44136 61.02909 61.88420
## 19:5700000-5800000 47.45843 33.15149 38.80003
## 19:6900000-7000000 48.71414 35.51968 38.43321
## 19:6300000-6400000 46.32370 34.64342 40.7585310 kbp bins
x<-read.table("ERP011529.1e4_fmt.tsv",header=T,row.names=1)
x <- x[grep("X",rownames(x),invert=TRUE),]
x <- x[grep("Y",rownames(x),invert=TRUE),]
x <- x[grep("M",rownames(x),invert=TRUE),]
x <- x[grep("J",rownames(x),invert=TRUE),]
x <- x[grep("G",rownames(x),invert=TRUE),]
x<-x[which(rowSums(x)>=10),]
x<-sweep(x, 2, colSums(x), FUN="/")*1000000
mysd<-apply(x,1,sd)
mean<-apply(x,1,mean)
y<-data.frame(log10(mean),mysd/mean)
colnames(y)=c("logMean","cv")
yy <- y[order(-y$cv),]
yy1 <- subset(yy,cv>0.4 & logMean > 1)
yy2 <- subset(yy,cv>0.6 & logMean > 0 & logMean < 1)
yy <- rbind(yy1,yy2)
write.table(yy,file="ERP011529.1e4_regions.tsv")
yy %>% kbl() %>% kable_paper("hover", full_width = F)| logMean | cv | |
|---|---|---|
| 6:103620000-103630000 | 2.5766681 | 0.8858035 |
| 9:3030000-3040000 | 2.2157590 | 0.7559011 |
| 9:35210000-35220000 | 2.0928172 | 0.5612517 |
| 3:5920000-5930000 | 1.7513271 | 0.5507766 |
| 17:40150000-40160000 | 2.7554414 | 0.5496405 |
| 11:108900000-108910000 | 1.8156715 | 0.5439322 |
| 1:170890000-170900000 | 1.0384176 | 0.5433383 |
| 16:91330000-91340000 | 1.5229965 | 0.5421049 |
| 2:98490000-98500000 | 4.1097005 | 0.5415297 |
| 16:57210000-57220000 | 1.7864497 | 0.5383862 |
| 5:146190000-146200000 | 1.7879667 | 0.5305683 |
| 17:36540000-36550000 | 1.6797435 | 0.5189303 |
| 2:90220000-90230000 | 1.0072894 | 0.4900906 |
| 18:73660000-73670000 | 1.4548479 | 0.4899055 |
| 13:120050000-120060000 | 1.0764627 | 0.4821736 |
| 9:123290000-123300000 | 1.5134883 | 0.4756352 |
| 9:3020000-3030000 | 2.6694716 | 0.4705209 |
| 11:105950000-105960000 | 1.2564555 | 0.4661507 |
| 2:22630000-22640000 | 1.4314697 | 0.4642970 |
| 15:85060000-85070000 | 1.2210213 | 0.4629963 |
| 16:17040000-17050000 | 1.2161622 | 0.4530765 |
| 13:120060000-120070000 | 1.2107485 | 0.4355830 |
| 9:3010000-3020000 | 1.4028729 | 0.4319614 |
| 17:3130000-3140000 | 1.0060136 | 0.4318672 |
| 7:59410000-59420000 | 1.0053320 | 0.4289122 |
| 9:17890000-17900000 | 1.0714229 | 0.4193877 |
| 4:147820000-147830000 | 1.0052477 | 0.4168345 |
| 9:3000000-3010000 | 3.2174123 | 0.4064944 |
| 6:114540000-114550000 | 0.2347983 | 0.8010136 |
| 10:57750000-57760000 | 0.4043757 | 0.7852146 |
| 6:147130000-147140000 | 0.9595081 | 0.7718390 |
| 8:55570000-55580000 | 0.7791794 | 0.6949242 |
| 6:114480000-114490000 | 0.3993175 | 0.6948682 |
| 6:114500000-114510000 | 0.3788053 | 0.6648612 |
| 8:55800000-55810000 | 0.9426675 | 0.6644633 |
| 11:120700000-120710000 | 0.7980596 | 0.6604791 |
| 8:55810000-55820000 | 0.9318977 | 0.6536283 |
| 6:114560000-114570000 | 0.0423560 | 0.6494797 |
| 6:114520000-114530000 | 0.3815883 | 0.6482632 |
| 6:114490000-114500000 | 0.4129321 | 0.6331237 |
| 3:108300000-108310000 | 0.7941670 | 0.6263144 |
| 6:114510000-114520000 | 0.6018086 | 0.6249555 |
| 8:55820000-55830000 | 0.6496244 | 0.6244466 |
| 8:71160000-71170000 | 0.8061978 | 0.6232191 |
| 15:91800000-91810000 | 0.0948023 | 0.6224485 |
| 4:149020000-149030000 | 0.8155489 | 0.6135908 |
| 3:151660000-151670000 | 0.1950615 | 0.6130050 |
| 11:120170000-120180000 | 0.8589414 | 0.6110155 |
| 6:114470000-114480000 | 0.4139267 | 0.6109841 |
| 8:124320000-124330000 | 0.1070492 | 0.6088155 |
| 5:139740000-139750000 | 0.8117237 | 0.6087392 |
| 14:19290000-19300000 | 0.1458429 | 0.6045441 |
| 2:31310000-31320000 | 0.7886790 | 0.6045056 |
| 6:114530000-114540000 | 0.5213903 | 0.6043882 |
| 4:149800000-149810000 | 0.7947997 | 0.6026965 |
| 8:121300000-121310000 | 0.8281864 | 0.6018621 |
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
#text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
grid()
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
grid()
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV",xlim=c(0,4.5),ylim=c(0.4,1))
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
grid()
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
zz<-x[which(rownames(x) %in% rownames(yy)),]
heatmap.2(as.matrix(zz),margin=c(8, 22),cexRow=0.65,trace="none",
cexCol=0.8,col=my_palette,scale="row")
heatmap.2(cor(t(zz)),trace="none",scale="none",margins=c(12,12),
cexRow=0.8, cexCol=0.8)
for (i in 1:19){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymax = max(chr)
plot(chr[,1], xaxt = "n", pch=19, col="gray",
ylim=c(0,mymax),ylab="RPM of 1Mbp bins of chr",main=mychr)
axis(1, at=chr[,1], labels=rownames(chr), xlab="chr", las=1)
points( chr[,1], xaxt = "n", las=1, pch=19, col="gray" )
points( chr[,2], xaxt = "n", las=1, pch=19, col="lightblue" )
points( chr[,3], xaxt = "n", las=1, pch=19, col="lightgreen" )
points( chr[,4], xaxt = "n", las=1, pch=19, col="pink" )
points( chr[,5], xaxt = "n", las=1, pch=19, col="orange" )
points( chr[,6], xaxt = "n", las=1, pch=19, col="black" )
points( chr[,7], xaxt = "n", las=1, pch=19, col="yellow" )
points( chr[,8], xaxt = "n", las=1, pch=19, col="red" )
points( chr[,9], xaxt = "n", las=1, pch=19, col="blue" )
grid()
}
lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymedian <- median(rowMeans(chr))
mymax <- max(rowMeans(chr))
rat <- mymax / mymedian
return(c(mymax,mymedian,rat))
})## [[1]]
## [1] 30.518499 4.029977 7.572872
##
## [[2]]
## [1] 12873.614128 4.059134 3171.517063
##
## [[3]]
## [1] 56.406234 4.002976 14.091074
##
## [[4]]
## [1] 31.796419 4.109172 7.737914
##
## [[5]]
## [1] 61.371493 4.113542 14.919379
##
## [[6]]
## [1] 377.283711 4.007043 94.155142
##
## [[7]]
## [1] 10.649690 4.097179 2.599274
##
## [[8]]
## [1] 18.205798 4.045219 4.500572
##
## [[9]]
## [1] 1649.727790 4.095313 402.833117
##
## [[10]]
## [1] 31.833595 4.129102 7.709568
##
## [[11]]
## [1] 293.982970 4.198036 70.028690
##
## [[12]]
## [1] 16.068714 3.993910 4.023304
##
## [[13]]
## [1] 25.183776 4.016554 6.269995
##
## [[14]]
## [1] 54.879416 3.961816 13.852086
##
## [[15]]
## [1] 16.634944 4.038830 4.118753
##
## [[16]]
## [1] 61.157500 3.976444 15.379949
##
## [[17]]
## [1] 569.431360 4.144299 137.401135
##
## [[18]]
## [1] 28.500200 4.011011 7.105490
##
## [[19]]
## [1] 12.000844 4.084158 2.938389lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
top <- head(chr[order(-rowMeans(chr)),])
return(top)
})## [[1]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 1:88140000-88150000 21.84822 26.63176 28.41675 30.60519
## 1:88150000-88160000 22.04731 29.13669 32.94203 29.06612
## 1:88180000-88190000 43.97776 21.64576 28.51045 20.15464
## 1:88170000-88180000 17.15657 22.82268 32.47349 22.83437
## 1:88160000-88170000 22.83935 18.73925 29.84578 20.59078
## 1:88220000-88230000 18.60215 25.25604 29.98244 22.93921
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 1:88140000-88150000 38.11849 30.69301 27.40596 34.66490
## 1:88150000-88160000 27.12017 26.09294 27.95067 27.57806
## 1:88180000-88190000 35.89961 22.88370 21.10769 21.54026
## 1:88170000-88180000 38.79552 21.08686 20.32041 23.78820
## 1:88160000-88170000 38.53781 19.50168 18.34156 23.52178
## 1:88220000-88230000 30.71057 20.17549 24.59727 20.96412
## ERX1059314.bam
## 1:88140000-88150000 36.28222
## 1:88150000-88160000 29.63830
## 1:88180000-88190000 23.88481
## 1:88170000-88180000 26.80396
## 1:88160000-88170000 26.38321
## 1:88220000-88230000 23.96962
##
## [[2]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 2:98490000-98500000 6420.52859 7090.13280 10892.04347 9314.02984
## 2:22630000-22640000 45.73930 29.98757 42.42599 37.08856
## 2:3050000-3060000 21.44571 18.27007 14.84090 16.30068
## 2:90220000-90230000 21.76599 11.41929 10.62016 11.86381
## 2:72840000-72850000 18.04382 10.54853 11.33078 12.68157
## 2:5380000-5390000 13.16173 10.35768 10.69825 11.20960
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 2:98490000-98500000 8392.638161 14784.462329 11534.358616 27985.108992
## 2:22630000-22640000 18.947866 19.475763 24.729195 11.582719
## 2:3050000-3060000 9.220596 18.249079 21.614108 14.992928
## 2:90220000-90230000 6.333427 8.940969 9.447395 5.082010
## 2:72840000-72850000 7.390454 8.068469 7.898363 5.038716
## 2:5380000-5390000 7.835978 6.889297 7.838785 4.975441
## ERX1059314.bam
## 2:98490000-98500000 19449.224361
## 2:22630000-22640000 13.082341
## 2:3050000-3060000 13.750973
## 2:90220000-90230000 6.050297
## 2:72840000-72850000 6.206855
## 2:5380000-5390000 5.972019
##
## [[3]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 3:5920000-5930000 109.357955 71.93916 79.221733
## 3:106150000-106160000 19.571642 11.39941 9.690899
## 3:106040000-106050000 14.693880 11.18868 7.410688
## 3:106140000-106150000 10.067147 12.93020 9.237980
## 3:106090000-106100000 8.751406 10.85867 10.877859
## 3:106080000-106090000 9.829102 10.55251 7.602007
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 3:5920000-5930000 83.310866 30.767352 37.681649
## 3:106150000-106160000 9.695697 10.286359 11.752839
## 3:106040000-106050000 8.181793 7.207003 10.802591
## 3:106140000-106150000 9.578275 6.346531 10.819868
## 3:106090000-106100000 9.859249 9.190021 9.442009
## 3:106080000-106090000 9.146330 7.045392 10.184930
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 3:5920000-5930000 51.60746 18.935981 24.833943
## 3:106150000-106160000 12.57525 9.531266 10.039254
## 3:106040000-106050000 12.88165 8.232456 8.822671
## 3:106140000-106150000 13.26040 8.495548 8.405184
## 3:106090000-106100000 11.80499 8.485558 9.161879
## 3:106080000-106090000 12.34970 9.571229 9.090124
##
## [[4]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 4:3080000-3090000 27.05492 26.32163 54.33463
## 4:147420000-147430000 20.36368 26.40115 29.75208
## 4:146480000-146490000 21.70106 26.00354 25.08623
## 4:147480000-147490000 30.44814 22.54436 26.12873
## 4:146560000-146570000 29.30985 20.66765 32.56720
## 4:146570000-146580000 12.86310 18.87444 16.47297
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 4:3080000-3090000 26.91059 40.02289 28.13598
## 4:147420000-147430000 24.92281 29.56182 26.31323
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##
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##
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## ERX1059312.bam ERX1059313.bam ERX1059314.bam
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## 19:37290000-37300000 8.777290 11.946154 7.995040
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 19:36890000-36900000 9.864442 7.556409 8.300812
## 19:36900000-36910000 9.498462 5.801351 6.559110
## 19:37330000-37340000 9.149504 7.563070 8.121424
## 19:37280000-37290000 8.847358 8.688705 9.673953
## 19:37220000-37230000 9.992110 7.493134 8.662852
## 19:37290000-37300000 9.230360 9.338110 9.4684711 kbp bins
x<-read.table("ERP011529.1e3_fmt.tsv",header=T,row.names=1)
x <- x[grep("X",rownames(x),invert=TRUE),]
x <- x[grep("Y",rownames(x),invert=TRUE),]
x <- x[grep("M",rownames(x),invert=TRUE),]
x <- x[grep("J",rownames(x),invert=TRUE),]
x <- x[grep("G",rownames(x),invert=TRUE),]
x<-x[which(rowSums(x)>=10),]
x<-sweep(x, 2, colSums(x), FUN="/")*1000000
mysd<-apply(x,1,sd)
mean<-apply(x,1,mean)
y<-data.frame(log10(mean),mysd/mean)
colnames(y)=c("logMean","cv")
yy <- y[order(-y$cv),]
yy <- subset(yy,cv>0.53 & logMean > 0)
write.table(yy,file="ERP011529.1e3_regions.tsv")
yy %>% kbl() %>% kable_paper("hover", full_width = F)| logMean | cv | |
|---|---|---|
| 11:46566000-46567000 | 0.0297409 | 1.8745713 |
| 18:85716000-85717000 | 0.1031568 | 1.4054489 |
| 6:147138000-147139000 | 0.8034578 | 1.3714401 |
| 19:8229000-8230000 | 0.5992795 | 1.1397681 |
| 11:31872000-31873000 | 0.1306509 | 1.0502747 |
| 9:3004000-3005000 | 0.6520011 | 0.9662882 |
| 9:3034000-3035000 | 1.1110555 | 0.9323705 |
| 9:3035000-3036000 | 1.7964103 | 0.9128191 |
| 9:3006000-3007000 | 1.4264385 | 0.9028032 |
| 6:103626000-103627000 | 2.6693286 | 0.8811466 |
| 9:3024000-3025000 | 2.4712257 | 0.8622344 |
| 3:23328000-23329000 | 0.1015037 | 0.8370116 |
| 8:55813000-55814000 | 0.2190739 | 0.8362783 |
| 9:33639000-33640000 | 0.0028172 | 0.8043697 |
| 9:3025000-3026000 | 2.0859247 | 0.7791326 |
| 2:98493000-98494000 | 1.3394362 | 0.7765708 |
| 10:57750000-57751000 | 0.5052384 | 0.7707765 |
| 5:146198000-146199000 | 0.5114178 | 0.7605949 |
| 4:49734000-49735000 | 0.3743835 | 0.7580677 |
| 4:147591000-147592000 | 0.2039802 | 0.7545856 |
| 5:145591000-145592000 | 0.0848854 | 0.7506301 |
| 4:147823000-147824000 | 0.1229583 | 0.7497844 |
| 6:90286000-90287000 | 0.0222663 | 0.7393192 |
| 8:120063000-120064000 | 0.1198268 | 0.7387753 |
| 8:55800000-55801000 | 0.4144676 | 0.7381758 |
| 9:3020000-3021000 | 0.2787756 | 0.7375221 |
| 4:147887000-147888000 | 0.1566280 | 0.7311161 |
| 9:4977000-4978000 | 0.0500681 | 0.7294202 |
| 2:98496000-98497000 | 2.8692170 | 0.7272710 |
| 15:91442000-91443000 | 0.0300322 | 0.7272188 |
| 1:85522000-85523000 | 0.1719096 | 0.7226625 |
| 3:68996000-68997000 | 0.0022655 | 0.7214291 |
| 2:133810000-133811000 | 0.0844612 | 0.7129524 |
| 9:124044000-124045000 | 0.0672226 | 0.7111147 |
| 2:98495000-98496000 | 2.2482694 | 0.7098609 |
| 2:99487000-99488000 | 0.0039930 | 0.7095648 |
| 10:49320000-49321000 | 0.0854885 | 0.7093575 |
| 1:115817000-115818000 | 0.0453417 | 0.7068706 |
| 11:104599000-104600000 | 0.0007804 | 0.7064729 |
| 4:147308000-147309000 | 0.1885000 | 0.7033757 |
| 13:75826000-75827000 | 0.0986174 | 0.7025536 |
| 4:146417000-146418000 | 0.4523724 | 0.7001601 |
| 4:145563000-145564000 | 0.1620894 | 0.6990849 |
| 4:146004000-146005000 | 0.2127167 | 0.6986389 |
| 11:102076000-102077000 | 0.0083836 | 0.6958884 |
| 4:146176000-146177000 | 0.2951452 | 0.6958113 |
| 10:19992000-19993000 | 0.0709734 | 0.6953154 |
| 13:7683000-7684000 | 0.0145409 | 0.6924377 |
| 12:110943000-110944000 | 0.0084075 | 0.6907250 |
| 6:35343000-35344000 | 0.0038307 | 0.6905956 |
| 2:98069000-98070000 | 0.0077892 | 0.6901899 |
| 4:145501000-145502000 | 0.1904881 | 0.6897883 |
| 9:97456000-97457000 | 0.0408970 | 0.6881389 |
| 9:3003000-3004000 | 2.2681421 | 0.6859513 |
| 8:55569000-55570000 | 0.3326841 | 0.6859036 |
| 15:89649000-89650000 | 0.0156698 | 0.6855434 |
| 2:98497000-98498000 | 4.0381168 | 0.6843167 |
| 3:56155000-56156000 | 0.2050134 | 0.6838010 |
| 1:115536000-115537000 | 0.0196312 | 0.6837610 |
| 7:109090000-109091000 | 0.0013036 | 0.6805542 |
| 4:147311000-147312000 | 0.0314611 | 0.6776700 |
| 4:145614000-145615000 | 0.1220466 | 0.6775113 |
| 1:85125000-85126000 | 0.0550200 | 0.6769448 |
| 3:106156000-106157000 | 0.1922763 | 0.6765372 |
| 4:146178000-146179000 | 0.1153467 | 0.6761842 |
| 9:3013000-3014000 | 0.1124147 | 0.6727877 |
| 1:170893000-170894000 | 0.0815771 | 0.6709748 |
| 8:31094000-31095000 | 0.0431838 | 0.6688751 |
| 7:22478000-22479000 | 0.0364087 | 0.6680024 |
| 8:55567000-55568000 | 0.3177577 | 0.6673842 |
| 19:36896000-36897000 | 0.1204259 | 0.6661058 |
| 3:120641000-120642000 | 0.0623750 | 0.6655965 |
| 3:148588000-148589000 | 0.0037413 | 0.6638566 |
| 8:55799000-55800000 | 0.0542436 | 0.6629088 |
| 3:43330000-43331000 | 0.0618217 | 0.6627409 |
| 3:106172000-106173000 | 0.0062494 | 0.6608142 |
| 9:96696000-96697000 | 0.0230817 | 0.6607612 |
| 4:147360000-147361000 | 0.1808634 | 0.6599066 |
| 6:132929000-132930000 | 0.0078061 | 0.6566827 |
| 10:35551000-35552000 | 0.0028044 | 0.6559150 |
| 9:78729000-78730000 | 0.0187177 | 0.6559102 |
| 1:170892000-170893000 | 0.2774133 | 0.6555487 |
| 3:16527000-16528000 | 0.0303055 | 0.6549874 |
| 12:99611000-99612000 | 0.0378915 | 0.6540451 |
| 1:181653000-181654000 | 0.0119429 | 0.6540150 |
| 7:80157000-80158000 | 0.0453006 | 0.6535129 |
| 1:170905000-170906000 | 0.0160688 | 0.6527207 |
| 5:9311000-9312000 | 0.0516652 | 0.6514037 |
| 11:3077000-3078000 | 0.4261685 | 0.6492256 |
| 9:122605000-122606000 | 0.0047930 | 0.6490940 |
| 13:7223000-7224000 | 0.0112156 | 0.6490113 |
| 10:122852000-122853000 | 0.0042628 | 0.6448025 |
| 1:24626000-24627000 | 0.0244233 | 0.6444923 |
| 17:71728000-71729000 | 0.0023317 | 0.6438006 |
| 13:72773000-72774000 | 0.0006041 | 0.6437436 |
| 8:21090000-21091000 | 0.0181353 | 0.6420686 |
| 3:11080000-11081000 | 0.0152782 | 0.6413564 |
| 4:152795000-152796000 | 0.0357495 | 0.6399797 |
| 9:3015000-3016000 | 0.0355211 | 0.6397512 |
| 18:4052000-4053000 | 0.0344216 | 0.6391106 |
| 5:59037000-59038000 | 0.0027261 | 0.6369548 |
| 16:32878000-32879000 | 0.0041043 | 0.6365674 |
| 4:146559000-146560000 | 0.3024779 | 0.6364584 |
| 4:147309000-147310000 | 0.0559388 | 0.6363905 |
| 4:83715000-83716000 | 0.0189917 | 0.6357940 |
| 12:115957000-115958000 | 0.0483615 | 0.6357378 |
| 7:25771000-25772000 | 0.0333515 | 0.6356574 |
| 7:104643000-104644000 | 0.0482556 | 0.6356355 |
| 7:61997000-61998000 | 0.0536365 | 0.6356012 |
| 1:190899000-190900000 | 0.0333909 | 0.6348463 |
| 8:20016000-20017000 | 0.2071780 | 0.6332896 |
| 11:56291000-56292000 | 0.0318203 | 0.6329232 |
| 18:13148000-13149000 | 0.0090747 | 0.6321443 |
| 2:150342000-150343000 | 0.0002247 | 0.6318372 |
| 11:94632000-94633000 | 0.0299457 | 0.6302685 |
| 10:34956000-34957000 | 0.0525748 | 0.6302396 |
| 4:19189000-19190000 | 0.0039489 | 0.6300551 |
| 5:3782000-3783000 | 0.0429232 | 0.6300425 |
| 12:113345000-113346000 | 0.0009101 | 0.6299615 |
| 6:133909000-133910000 | 0.0091670 | 0.6291100 |
| 5:61226000-61227000 | 0.0615231 | 0.6286139 |
| 2:180209000-180210000 | 0.0127433 | 0.6285141 |
| 13:99558000-99559000 | 0.0245280 | 0.6275703 |
| 1:193090000-193091000 | 0.0266302 | 0.6266300 |
| 8:108044000-108045000 | 0.0108068 | 0.6258985 |
| 13:25679000-25680000 | 0.0461402 | 0.6254157 |
| 16:57212000-57213000 | 0.1594356 | 0.6250346 |
| 6:47725000-47726000 | 0.2288640 | 0.6244660 |
| 5:63158000-63159000 | 0.0172709 | 0.6242899 |
| 11:30704000-30705000 | 0.0349943 | 0.6240407 |
| 1:188943000-188944000 | 0.0004824 | 0.6237018 |
| 2:65604000-65605000 | 0.0043914 | 0.6234428 |
| 17:56380000-56381000 | 0.0048160 | 0.6231482 |
| 8:19834000-19835000 | 0.3603051 | 0.6228362 |
| 10:80983000-80984000 | 0.0040163 | 0.6218394 |
| 9:3007000-3008000 | 0.1739980 | 0.6216743 |
| 4:147209000-147210000 | 0.0150709 | 0.6214484 |
| 4:156390000-156391000 | 0.0544732 | 0.6214039 |
| 2:111012000-111013000 | 0.0045720 | 0.6211926 |
| 9:119754000-119755000 | 0.0241396 | 0.6210118 |
| 1:153322000-153323000 | 0.0019556 | 0.6206129 |
| 17:50599000-50600000 | 0.0154122 | 0.6204541 |
| 13:116877000-116878000 | 0.0154152 | 0.6200769 |
| 5:29773000-29774000 | 0.1384646 | 0.6197945 |
| 4:81414000-81415000 | 0.0275890 | 0.6194435 |
| 15:74957000-74958000 | 0.2823025 | 0.6188998 |
| 3:56091000-56092000 | 0.0063445 | 0.6185502 |
| 3:30050000-30051000 | 0.1021518 | 0.6179222 |
| 4:126942000-126943000 | 0.0047959 | 0.6179082 |
| 2:131583000-131584000 | 0.0218766 | 0.6166522 |
| 10:20829000-20830000 | 0.0033139 | 0.6165422 |
| 18:42519000-42520000 | 0.0102081 | 0.6163174 |
| 5:77857000-77858000 | 0.0013130 | 0.6162721 |
| 6:64860000-64861000 | 0.0164839 | 0.6162497 |
| 4:147886000-147887000 | 0.0796112 | 0.6157209 |
| 12:19754000-19755000 | 0.0027979 | 0.6156686 |
| 6:131545000-131546000 | 0.0025889 | 0.6151316 |
| 4:156570000-156571000 | 0.1397050 | 0.6146103 |
| 4:145994000-145995000 | 0.0625236 | 0.6144173 |
| 5:100822000-100823000 | 0.0076486 | 0.6144069 |
| 4:125917000-125918000 | 0.0060428 | 0.6143625 |
| 1:170906000-170907000 | 0.2466952 | 0.6138884 |
| 2:155407000-155408000 | 0.0195214 | 0.6132383 |
| 1:177497000-177498000 | 0.0157385 | 0.6130005 |
| 15:82400000-82401000 | 0.0167299 | 0.6128455 |
| 1:146745000-146746000 | 0.0146018 | 0.6122950 |
| 11:94432000-94433000 | 0.0033196 | 0.6122366 |
| 15:12965000-12966000 | 0.1022465 | 0.6120947 |
| 12:57771000-57772000 | 0.0330095 | 0.6103558 |
| 13:47158000-47159000 | 0.1219891 | 0.6099312 |
| 6:34758000-34759000 | 0.0290111 | 0.6098182 |
| 2:48356000-48357000 | 0.0517706 | 0.6098126 |
| 1:25442000-25443000 | 0.0134130 | 0.6097917 |
| 14:77777000-77778000 | 0.0009012 | 0.6097489 |
| 5:10852000-10853000 | 0.0961002 | 0.6096184 |
| 16:61422000-61423000 | 0.0369275 | 0.6095241 |
| 7:79359000-79360000 | 0.0447554 | 0.6091828 |
| 17:79059000-79060000 | 0.0500202 | 0.6091091 |
| 10:23342000-23343000 | 0.0128505 | 0.6088424 |
| 4:147359000-147360000 | 0.3302623 | 0.6086757 |
| 8:128341000-128342000 | 0.1196438 | 0.6085723 |
| 4:145995000-145996000 | 0.2243701 | 0.6085072 |
| 11:27234000-27235000 | 0.0127917 | 0.6083794 |
| 8:21097000-21098000 | 0.2804952 | 0.6083406 |
| 8:91503000-91504000 | 0.0021115 | 0.6078278 |
| 14:42979000-42980000 | 0.0672371 | 0.6074755 |
| 1:122403000-122404000 | 0.0865847 | 0.6068139 |
| 3:106173000-106174000 | 0.0683753 | 0.6065684 |
| 4:147822000-147823000 | 0.2696658 | 0.6063002 |
| 5:77555000-77556000 | 0.0124154 | 0.6059061 |
| 2:27901000-27902000 | 0.0377253 | 0.6058865 |
| 15:50754000-50755000 | 0.0211838 | 0.6054995 |
| 2:46171000-46172000 | 0.1384873 | 0.6053559 |
| 4:147017000-147018000 | 0.0437433 | 0.6050700 |
| 19:51538000-51539000 | 0.0001778 | 0.6046535 |
| 9:17899000-17900000 | 0.9801434 | 0.6044260 |
| 13:52845000-52846000 | 0.0397198 | 0.6044142 |
| 4:147527000-147528000 | 0.0634413 | 0.6039994 |
| 14:30138000-30139000 | 0.0125956 | 0.6037514 |
| 1:151068000-151069000 | 0.1006183 | 0.6036689 |
| 5:136864000-136865000 | 0.1073580 | 0.6036183 |
| 16:18406000-18407000 | 0.0060254 | 0.6034702 |
| 9:77662000-77663000 | 0.0003064 | 0.6026093 |
| 6:87003000-87004000 | 0.0791696 | 0.6022806 |
| 14:103622000-103623000 | 0.0632584 | 0.6018102 |
| 9:10707000-10708000 | 0.0224680 | 0.6016496 |
| 12:11734000-11735000 | 0.0079010 | 0.6015574 |
| 2:150312000-150313000 | 0.0218668 | 0.6014123 |
| 12:92857000-92858000 | 0.1331953 | 0.6010800 |
| 17:33916000-33917000 | 0.0242260 | 0.6007873 |
| 14:34225000-34226000 | 0.0302290 | 0.6007073 |
| 8:61834000-61835000 | 0.0107939 | 0.6003514 |
| 14:108460000-108461000 | 0.0883152 | 0.6002780 |
| 12:84456000-84457000 | 0.0136361 | 0.6000701 |
| 2:168962000-168963000 | 0.0188109 | 0.5998481 |
| 3:80790000-80791000 | 0.1688292 | 0.5995542 |
| 15:52748000-52749000 | 0.1963463 | 0.5992513 |
| 4:72272000-72273000 | 0.0111980 | 0.5991371 |
| 2:72843000-72844000 | 0.8113276 | 0.5987263 |
| 19:14575000-14576000 | 0.0087011 | 0.5986078 |
| 9:23892000-23893000 | 0.0104106 | 0.5984774 |
| 4:85124000-85125000 | 0.0075147 | 0.5984649 |
| 15:14799000-14800000 | 0.0164248 | 0.5984452 |
| 1:170894000-170895000 | 0.1201148 | 0.5983268 |
| 10:119325000-119326000 | 0.0224967 | 0.5983069 |
| 6:131231000-131232000 | 0.0488149 | 0.5977740 |
| 5:3593000-3594000 | 0.0112793 | 0.5975642 |
| 16:19420000-19421000 | 0.1009719 | 0.5973851 |
| 18:86226000-86227000 | 0.0350746 | 0.5973741 |
| 8:85838000-85839000 | 0.0405652 | 0.5973625 |
| 8:20697000-20698000 | 0.2386592 | 0.5973089 |
| 15:5055000-5056000 | 0.0156349 | 0.5970791 |
| 2:92054000-92055000 | 0.0600920 | 0.5970717 |
| 14:68359000-68360000 | 0.0066266 | 0.5969541 |
| 1:142962000-142963000 | 0.0049324 | 0.5966817 |
| 17:51846000-51847000 | 0.0151957 | 0.5964896 |
| 6:79349000-79350000 | 0.0323803 | 0.5959105 |
| 6:16299000-16300000 | 0.0714408 | 0.5956601 |
| 16:27394000-27395000 | 0.0002965 | 0.5950290 |
| 1:38147000-38148000 | 0.0545377 | 0.5946508 |
| 11:3143000-3144000 | 1.7206997 | 0.5944333 |
| 7:59421000-59422000 | 0.5760054 | 0.5942839 |
| 11:99822000-99823000 | 0.0262870 | 0.5937341 |
| 1:170891000-170892000 | 0.2357186 | 0.5937241 |
| 4:146560000-146561000 | 0.5623604 | 0.5933676 |
| 1:84991000-84992000 | 0.0085102 | 0.5933119 |
| 12:111752000-111753000 | 0.0014208 | 0.5932044 |
| 4:136787000-136788000 | 0.0529839 | 0.5929298 |
| 10:129430000-129431000 | 0.0005721 | 0.5929295 |
| 17:86997000-86998000 | 0.0358149 | 0.5925618 |
| 5:31357000-31358000 | 0.0456623 | 0.5925043 |
| 2:26051000-26052000 | 0.1037266 | 0.5921526 |
| 19:36895000-36896000 | 0.2251925 | 0.5918509 |
| 2:78987000-78988000 | 0.0028374 | 0.5913279 |
| 1:170901000-170902000 | 0.1111578 | 0.5906555 |
| 4:147027000-147028000 | 0.0540739 | 0.5904947 |
| 5:106607000-106608000 | 0.0090269 | 0.5903169 |
| 5:3783000-3784000 | 0.0029034 | 0.5900408 |
| 16:8378000-8379000 | 0.0153478 | 0.5895847 |
| 13:61544000-61545000 | 0.0042107 | 0.5895471 |
| 3:119981000-119982000 | 0.0162987 | 0.5893441 |
| 2:90225000-90226000 | 0.9267996 | 0.5888134 |
| 8:124327000-124328000 | 0.0804309 | 0.5886328 |
| 8:55574000-55575000 | 0.6235704 | 0.5876168 |
| 9:3018000-3019000 | 1.3107542 | 0.5871910 |
| 15:34712000-34713000 | 0.0923514 | 0.5871445 |
| 4:5927000-5928000 | 0.0038284 | 0.5869129 |
| 19:37339000-37340000 | 0.0720140 | 0.5868686 |
| 14:25879000-25880000 | 0.2773714 | 0.5868290 |
| 5:100647000-100648000 | 0.0564032 | 0.5867640 |
| 5:60388000-60389000 | 0.0006791 | 0.5867302 |
| 7:39237000-39238000 | 0.0678775 | 0.5867060 |
| 16:91331000-91332000 | 1.5455166 | 0.5866905 |
| 7:22501000-22502000 | 0.0234308 | 0.5862276 |
| 11:56008000-56009000 | 0.1534229 | 0.5859651 |
| 15:8943000-8944000 | 0.0746241 | 0.5856960 |
| 16:69227000-69228000 | 0.0115878 | 0.5855647 |
| 15:85061000-85062000 | 1.2023503 | 0.5849309 |
| 10:130247000-130248000 | 0.0378791 | 0.5849255 |
| 6:115337000-115338000 | 0.0357302 | 0.5848137 |
| 4:95856000-95857000 | 0.0066519 | 0.5845323 |
| 3:68022000-68023000 | 0.0798182 | 0.5843116 |
| 2:74487000-74488000 | 0.0306579 | 0.5842579 |
| 6:51399000-51400000 | 0.0026282 | 0.5840873 |
| 1:137353000-137354000 | 0.1508016 | 0.5840625 |
| 11:25456000-25457000 | 0.0171895 | 0.5836028 |
| 6:3182000-3183000 | 0.1798565 | 0.5831148 |
| 2:98492000-98493000 | 1.8148568 | 0.5828350 |
| 5:143072000-143073000 | 0.1356904 | 0.5828050 |
| 4:156391000-156392000 | 0.1701857 | 0.5826056 |
| 6:31830000-31831000 | 0.0821580 | 0.5825952 |
| 13:55096000-55097000 | 0.0061604 | 0.5814229 |
| 8:20555000-20556000 | 0.0039419 | 0.5812467 |
| 9:122181000-122182000 | 0.0070358 | 0.5810833 |
| 19:44715000-44716000 | 0.0823141 | 0.5809282 |
| 8:20698000-20699000 | 0.4416338 | 0.5805481 |
| 4:61741000-61742000 | 0.1788988 | 0.5800572 |
| 6:57489000-57490000 | 0.0381550 | 0.5798483 |
| 5:145624000-145625000 | 0.0538569 | 0.5795355 |
| 6:116482000-116483000 | 0.0282180 | 0.5792019 |
| 7:88154000-88155000 | 0.0225853 | 0.5786348 |
| 7:89338000-89339000 | 0.0333554 | 0.5782708 |
| 1:88182000-88183000 | 0.6093088 | 0.5779481 |
| 1:32303000-32304000 | 0.0304552 | 0.5777119 |
| 2:73604000-73605000 | 0.0789063 | 0.5775843 |
| 2:162328000-162329000 | 0.0116888 | 0.5775617 |
| 18:6491000-6492000 | 0.0488731 | 0.5775609 |
| 4:23942000-23943000 | 0.0046917 | 0.5773802 |
| 4:114501000-114502000 | 0.1095943 | 0.5772233 |
| 10:50065000-50066000 | 0.0058571 | 0.5772054 |
| 5:6522000-6523000 | 0.0015770 | 0.5761880 |
| 12:77925000-77926000 | 0.0010461 | 0.5760577 |
| 12:99778000-99779000 | 0.0601591 | 0.5759686 |
| 3:132172000-132173000 | 0.0118697 | 0.5756790 |
| 1:85317000-85318000 | 0.1678353 | 0.5755390 |
| 17:80718000-80719000 | 0.0237586 | 0.5750377 |
| 4:146003000-146004000 | 0.3235628 | 0.5747023 |
| 12:119255000-119256000 | 0.0225181 | 0.5744667 |
| 1:85392000-85393000 | 0.0132355 | 0.5742757 |
| 1:77068000-77069000 | 0.0124628 | 0.5733712 |
| 2:127970000-127971000 | 0.0152902 | 0.5733334 |
| 3:136100000-136101000 | 0.0854353 | 0.5732530 |
| 7:50680000-50681000 | 0.0082315 | 0.5730681 |
| 13:93639000-93640000 | 0.0106607 | 0.5729143 |
| 19:41053000-41054000 | 0.1336929 | 0.5720982 |
| 15:86504000-86505000 | 0.0096707 | 0.5720189 |
| 4:147831000-147832000 | 0.1293738 | 0.5719017 |
| 5:135288000-135289000 | 0.0102303 | 0.5717923 |
| 10:79553000-79554000 | 0.0043258 | 0.5715408 |
| 7:140902000-140903000 | 0.0576406 | 0.5714126 |
| 9:99021000-99022000 | 0.0062471 | 0.5712776 |
| 16:50253000-50254000 | 0.0762357 | 0.5712310 |
| 7:79461000-79462000 | 0.0005640 | 0.5709815 |
| 11:75449000-75450000 | 0.0081276 | 0.5709314 |
| 1:170909000-170910000 | 0.2417681 | 0.5708715 |
| 1:42735000-42736000 | 0.0175459 | 0.5708089 |
| 11:87507000-87508000 | 0.0128601 | 0.5694858 |
| 14:25936000-25937000 | 0.2889835 | 0.5691353 |
| 9:35216000-35217000 | 2.1794373 | 0.5690631 |
| 15:25217000-25218000 | 0.1073093 | 0.5688879 |
| 4:119378000-119379000 | 0.0020797 | 0.5688119 |
| 15:34713000-34714000 | 0.0166292 | 0.5687425 |
| 18:73666000-73667000 | 1.4899119 | 0.5686771 |
| 4:10693000-10694000 | 0.0849212 | 0.5685850 |
| 7:77797000-77798000 | 0.1740742 | 0.5685612 |
| 8:117669000-117670000 | 0.0135332 | 0.5685271 |
| 10:128054000-128055000 | 0.0511973 | 0.5684930 |
| 4:147016000-147017000 | 0.0185616 | 0.5683021 |
| 2:173578000-173579000 | 0.0670671 | 0.5679618 |
| 4:147830000-147831000 | 0.1821783 | 0.5679473 |
| 19:45730000-45731000 | 0.0261171 | 0.5677510 |
| 15:78705000-78706000 | 0.0295060 | 0.5674762 |
| 11:16276000-16277000 | 0.0304345 | 0.5670408 |
| 3:98728000-98729000 | 0.0719920 | 0.5664736 |
| 4:146475000-146476000 | 0.2308550 | 0.5658757 |
| 8:92794000-92795000 | 0.0142867 | 0.5656208 |
| 10:32765000-32766000 | 0.0201160 | 0.5655846 |
| 5:30320000-30321000 | 0.0156426 | 0.5652630 |
| 2:5384000-5385000 | 0.8160162 | 0.5649458 |
| 7:7281000-7282000 | 0.3416462 | 0.5646435 |
| 17:40157000-40158000 | 2.0440910 | 0.5645862 |
| 8:55814000-55815000 | 0.1836302 | 0.5644954 |
| 6:100886000-100887000 | 0.0270544 | 0.5642456 |
| 12:37260000-37261000 | 0.0367469 | 0.5641761 |
| 9:86493000-86494000 | 0.0135226 | 0.5641224 |
| 10:53504000-53505000 | 0.0475241 | 0.5640680 |
| 1:170671000-170672000 | 0.0162308 | 0.5640239 |
| 2:19922000-19923000 | 0.0982005 | 0.5636578 |
| 6:18169000-18170000 | 0.0641469 | 0.5632383 |
| 4:21312000-21313000 | 0.0197038 | 0.5632126 |
| 1:55696000-55697000 | 0.0342794 | 0.5631779 |
| 8:89496000-89497000 | 0.0305448 | 0.5630717 |
| 19:37364000-37365000 | 0.0993244 | 0.5624378 |
| 2:127944000-127945000 | 0.0069725 | 0.5622412 |
| 13:39508000-39509000 | 0.0138338 | 0.5621171 |
| 9:123721000-123722000 | 0.5197329 | 0.5618676 |
| 18:57653000-57654000 | 0.0013758 | 0.5617061 |
| 3:25197000-25198000 | 0.0386831 | 0.5613661 |
| 1:22573000-22574000 | 0.0592297 | 0.5613073 |
| 12:53882000-53883000 | 0.1419354 | 0.5612074 |
| 13:62495000-62496000 | 0.0071514 | 0.5610331 |
| 8:19752000-19753000 | 0.1415334 | 0.5609480 |
| 3:5925000-5926000 | 1.8190182 | 0.5609209 |
| 17:38121000-38122000 | 0.0108934 | 0.5608958 |
| 4:147187000-147188000 | 0.0216246 | 0.5606902 |
| 16:17040000-17041000 | 1.1828163 | 0.5606360 |
| 1:181750000-181751000 | 0.0738204 | 0.5605646 |
| 12:63095000-63096000 | 0.0120265 | 0.5604707 |
| 11:108902000-108903000 | 1.8831404 | 0.5603640 |
| 3:106049000-106050000 | 0.1452322 | 0.5602648 |
| 10:65400000-65401000 | 0.0784255 | 0.5600994 |
| 9:107107000-107108000 | 0.0097969 | 0.5597108 |
| 8:67111000-67112000 | 0.0087276 | 0.5596049 |
| 18:68435000-68436000 | 0.0044887 | 0.5594728 |
| 4:147304000-147305000 | 0.0678378 | 0.5593109 |
| 14:86852000-86853000 | 0.0239017 | 0.5590915 |
| 11:121036000-121037000 | 0.0083005 | 0.5587734 |
| 2:111011000-111012000 | 0.0157192 | 0.5585071 |
| 8:55801000-55802000 | 0.4779291 | 0.5584852 |
| 11:42897000-42898000 | 0.0870498 | 0.5581158 |
| 13:95021000-95022000 | 0.0154070 | 0.5581028 |
| 14:43769000-43770000 | 0.0710565 | 0.5577619 |
| 19:37249000-37250000 | 0.0450675 | 0.5571919 |
| 16:38723000-38724000 | 0.0019262 | 0.5570371 |
| 3:16238000-16239000 | 0.0522574 | 0.5568866 |
| 6:47730000-47731000 | 0.2823366 | 0.5568834 |
| 12:119957000-119958000 | 0.0107205 | 0.5567794 |
| 10:108365000-108366000 | 0.0129061 | 0.5562906 |
| 6:10548000-10549000 | 0.0022774 | 0.5561515 |
| 15:52749000-52750000 | 0.0964257 | 0.5557156 |
| 4:14931000-14932000 | 0.0698754 | 0.5557027 |
| 2:151618000-151619000 | 0.0708210 | 0.5556772 |
| 5:3979000-3980000 | 0.0386798 | 0.5555992 |
| 14:83402000-83403000 | 0.0230487 | 0.5555830 |
| 14:25979000-25980000 | 0.2323794 | 0.5554164 |
| 13:104058000-104059000 | 0.0299991 | 0.5552077 |
| 2:19996000-19997000 | 0.1694273 | 0.5550169 |
| 16:14549000-14550000 | 0.0050179 | 0.5549421 |
| 6:42841000-42842000 | 0.0785416 | 0.5547537 |
| 11:120128000-120129000 | 0.0131248 | 0.5547529 |
| 9:3019000-3020000 | 0.0521316 | 0.5544405 |
| 5:63159000-63160000 | 0.1341026 | 0.5541751 |
| 12:51748000-51749000 | 0.0318254 | 0.5541635 |
| 8:65187000-65188000 | 0.0744547 | 0.5538875 |
| 10:66365000-66366000 | 0.0016597 | 0.5538223 |
| 12:61810000-61811000 | 0.0085174 | 0.5537302 |
| 16:44988000-44989000 | 0.0552407 | 0.5537020 |
| 9:3038000-3039000 | 0.2927692 | 0.5535822 |
| 11:11975000-11976000 | 0.0436543 | 0.5530957 |
| 5:66624000-66625000 | 0.1047068 | 0.5530811 |
| 11:62430000-62431000 | 0.0240105 | 0.5528690 |
| 16:57211000-57212000 | 1.5576139 | 0.5528261 |
| 3:39973000-39974000 | 0.0794106 | 0.5524869 |
| 14:106094000-106095000 | 0.0087597 | 0.5523452 |
| 2:6135000-6136000 | 0.0068365 | 0.5521247 |
| 10:57326000-57327000 | 0.0190208 | 0.5518390 |
| 17:40158000-40159000 | 2.0421138 | 0.5517687 |
| 1:84989000-84990000 | 0.1324951 | 0.5515270 |
| 11:98271000-98272000 | 0.0091617 | 0.5513600 |
| 10:79570000-79571000 | 0.0353722 | 0.5513232 |
| 8:48681000-48682000 | 0.0097318 | 0.5511144 |
| 13:6300000-6301000 | 0.0158053 | 0.5509485 |
| 2:97119000-97120000 | 0.0710739 | 0.5505466 |
| 7:27322000-27323000 | 0.0250504 | 0.5505259 |
| 15:25365000-25366000 | 0.0250307 | 0.5504840 |
| 6:81801000-81802000 | 0.0391027 | 0.5503586 |
| 13:58362000-58363000 | 0.0193459 | 0.5503380 |
| 10:116310000-116311000 | 0.0464460 | 0.5501450 |
| 4:8442000-8443000 | 0.0100691 | 0.5499960 |
| 5:117527000-117528000 | 0.0113940 | 0.5497971 |
| 14:43173000-43174000 | 0.0556632 | 0.5496703 |
| 10:24222000-24223000 | 0.0072077 | 0.5495691 |
| 13:110655000-110656000 | 0.0307331 | 0.5490173 |
| 4:146492000-146493000 | 0.6162532 | 0.5485358 |
| 16:65053000-65054000 | 0.0332150 | 0.5484668 |
| 4:61662000-61663000 | 0.1038063 | 0.5482922 |
| 7:77796000-77797000 | 0.1037153 | 0.5481996 |
| 8:21312000-21313000 | 0.0313171 | 0.5475962 |
| 6:11925000-11926000 | 0.0106051 | 0.5472833 |
| 11:80671000-80672000 | 0.0374384 | 0.5472338 |
| 16:8105000-8106000 | 0.0335831 | 0.5462592 |
| 17:40156000-40157000 | 1.8404000 | 0.5460945 |
| 13:46920000-46921000 | 0.0191992 | 0.5456294 |
| 17:24249000-24250000 | 0.0008041 | 0.5455354 |
| 15:94526000-94527000 | 0.0002917 | 0.5452791 |
| 11:83854000-83855000 | 0.0083137 | 0.5452500 |
| 3:51480000-51481000 | 0.0064735 | 0.5451320 |
| 1:112730000-112731000 | 0.0370757 | 0.5450140 |
| 9:68401000-68402000 | 0.0139979 | 0.5449392 |
| 4:49633000-49634000 | 0.0130231 | 0.5449136 |
| 13:12875000-12876000 | 0.2559199 | 0.5446903 |
| 5:77754000-77755000 | 0.0277609 | 0.5446688 |
| 11:38122000-38123000 | 0.0122288 | 0.5446662 |
| 10:116785000-116786000 | 0.0113129 | 0.5445913 |
| 19:37340000-37341000 | 0.0812947 | 0.5443623 |
| 4:33032000-33033000 | 0.0296334 | 0.5442719 |
| 13:63151000-63152000 | 0.0164583 | 0.5440232 |
| 14:54870000-54871000 | 0.0294279 | 0.5440139 |
| 17:21772000-21773000 | 0.0141141 | 0.5436382 |
| 1:63349000-63350000 | 0.0896974 | 0.5434998 |
| 1:55697000-55698000 | 0.0764016 | 0.5433903 |
| 10:81394000-81395000 | 0.0186650 | 0.5432666 |
| 10:22029000-22030000 | 0.0666009 | 0.5432293 |
| 3:106191000-106192000 | 0.1017937 | 0.5431247 |
| 10:29912000-29913000 | 0.0391929 | 0.5429659 |
| 7:58476000-58477000 | 0.0337895 | 0.5429498 |
| 11:71098000-71099000 | 0.0166237 | 0.5427776 |
| 1:63313000-63314000 | 0.0694414 | 0.5425463 |
| 11:105957000-105958000 | 1.2404413 | 0.5424973 |
| 14:119244000-119245000 | 0.0389004 | 0.5424593 |
| 11:42898000-42899000 | 0.1087789 | 0.5419091 |
| 3:49478000-49479000 | 0.0771033 | 0.5417678 |
| 4:107293000-107294000 | 0.0280880 | 0.5415991 |
| 10:81381000-81382000 | 0.0169124 | 0.5415457 |
| 4:156407000-156408000 | 0.1452455 | 0.5415358 |
| 9:48933000-48934000 | 0.0144642 | 0.5409108 |
| 9:86347000-86348000 | 0.0176341 | 0.5407968 |
| 18:9495000-9496000 | 0.0130599 | 0.5406448 |
| 4:137085000-137086000 | 0.0492066 | 0.5405163 |
| 11:95037000-95038000 | 0.0218529 | 0.5402807 |
| 7:27968000-27969000 | 0.0499853 | 0.5400610 |
| 5:109050000-109051000 | 0.0848381 | 0.5400264 |
| 6:144308000-144309000 | 0.0637492 | 0.5399961 |
| 6:144928000-144929000 | 0.0137766 | 0.5398076 |
| 8:115932000-115933000 | 0.0401781 | 0.5394348 |
| 16:35590000-35591000 | 0.0035875 | 0.5394077 |
| 11:50322000-50323000 | 0.0464151 | 0.5392741 |
| 5:143071000-143072000 | 0.0255558 | 0.5392347 |
| 3:16142000-16143000 | 0.0415738 | 0.5391268 |
| 5:26212000-26213000 | 0.2828765 | 0.5388858 |
| 11:76093000-76094000 | 0.0034321 | 0.5387256 |
| 3:80821000-80822000 | 0.0160348 | 0.5384126 |
| 17:15180000-15181000 | 0.0363518 | 0.5382877 |
| 2:154976000-154977000 | 0.0266111 | 0.5381449 |
| 5:145623000-145624000 | 0.0266878 | 0.5381307 |
| 2:89560000-89561000 | 0.0147145 | 0.5380269 |
| 9:3002000-3003000 | 1.6109736 | 0.5379995 |
| 9:91446000-91447000 | 0.0496716 | 0.5378216 |
| 5:146197000-146198000 | 1.5121634 | 0.5376433 |
| 14:20595000-20596000 | 0.0242658 | 0.5373980 |
| 12:50732000-50733000 | 0.0170346 | 0.5369430 |
| 1:191640000-191641000 | 0.0123779 | 0.5366218 |
| 1:166303000-166304000 | 0.0106411 | 0.5366193 |
| 10:80680000-80681000 | 0.0206048 | 0.5363965 |
| 14:121149000-121150000 | 0.0028026 | 0.5363759 |
| 11:58830000-58831000 | 0.0230617 | 0.5361399 |
| 1:85076000-85077000 | 0.0747989 | 0.5359982 |
| 16:34977000-34978000 | 0.0121085 | 0.5358428 |
| 4:156404000-156405000 | 0.1303858 | 0.5355486 |
| 7:53518000-53519000 | 0.0079046 | 0.5354922 |
| 1:65924000-65925000 | 0.0511229 | 0.5354583 |
| 18:53996000-53997000 | 0.0301112 | 0.5353193 |
| 13:12876000-12877000 | 0.2636541 | 0.5353186 |
| 8:26649000-26650000 | 0.0245420 | 0.5352062 |
| 8:63759000-63760000 | 0.0017625 | 0.5347008 |
| 13:87294000-87295000 | 0.0070566 | 0.5345907 |
| 5:3781000-3782000 | 0.0019475 | 0.5344912 |
| 17:36542000-36543000 | 1.7337529 | 0.5341173 |
| 2:78986000-78987000 | 0.1061993 | 0.5341012 |
| 16:88834000-88835000 | 0.0171995 | 0.5339155 |
| 13:112789000-112790000 | 0.0017184 | 0.5338082 |
| 1:9962000-9963000 | 0.1264066 | 0.5333802 |
| 2:11826000-11827000 | 0.0066876 | 0.5333766 |
| 17:40154000-40155000 | 1.9554137 | 0.5333418 |
| 11:46202000-46203000 | 0.0033876 | 0.5331840 |
| 7:42780000-42781000 | 0.1289957 | 0.5331119 |
| 9:70587000-70588000 | 0.0007419 | 0.5330887 |
| 10:9550000-9551000 | 0.0166581 | 0.5326938 |
| 19:45014000-45015000 | 0.0007485 | 0.5324930 |
| 1:72252000-72253000 | 0.0048459 | 0.5324633 |
| 7:64936000-64937000 | 0.1199999 | 0.5323614 |
| 10:67756000-67757000 | 0.0342600 | 0.5321448 |
| 1:103082000-103083000 | 0.0069834 | 0.5321352 |
| 8:87471000-87472000 | 0.0091948 | 0.5316486 |
| 3:134962000-134963000 | 0.0039403 | 0.5315362 |
| 8:109520000-109521000 | 0.0113650 | 0.5310538 |
| 11:3082000-3083000 | 0.3839890 | 0.5310365 |
| 1:118941000-118942000 | 0.0131180 | 0.5309530 |
| 3:152773000-152774000 | 0.0472257 | 0.5308491 |
| 2:151617000-151618000 | 0.0174393 | 0.5303010 |
| 5:24680000-24681000 | 0.0014000 | 0.5302211 |
| 1:190650000-190651000 | 0.0041274 | 0.5300751 |
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
#text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
grid()
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV")
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
grid()
plot(y$logMean,y$cv,pch=18,cex=0.5,xlab="log10(mean)",ylab="CV",xlim=c(-1,4.5),ylim=c(0.5,2))
points(yy$logMean,yy$cv)
text(yy$logMean,yy$cv+0.02,labels=rownames(yy),cex=0.8)
grid()
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
zz<-x[which(rownames(x) %in% rownames(yy)),]
heatmap.2(as.matrix(zz),margin=c(8, 22),cexRow=0.65,trace="none",
cexCol=0.8,col=my_palette,scale="row")
heatmap.2(cor(t(zz)),trace="none",scale="none",margins=c(12,12),
cexRow=0.8, cexCol=0.8)
for (i in 1:19){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymax = max(chr)
plot(chr[,1], xaxt = "n", pch=19, col="gray",
ylim=c(0,mymax),ylab="RPM of 1Mbp bins of chr",main=mychr)
axis(1, at=chr[,1], labels=rownames(chr), xlab="chr", las=1)
points( chr[,1], xaxt = "n", las=1, pch=19, col="gray" )
points( chr[,2], xaxt = "n", las=1, pch=19, col="lightblue" )
points( chr[,3], xaxt = "n", las=1, pch=19, col="lightgreen" )
points( chr[,4], xaxt = "n", las=1, pch=19, col="pink" )
points( chr[,5], xaxt = "n", las=1, pch=19, col="orange" )
points( chr[,6], xaxt = "n", las=1, pch=19, col="black" )
points( chr[,7], xaxt = "n", las=1, pch=19, col="yellow" )
points( chr[,8], xaxt = "n", las=1, pch=19, col="red" )
points( chr[,9], xaxt = "n", las=1, pch=19, col="blue" )
grid()
}
lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
mymedian <- median(rowMeans(chr))
mymax <- max(rowMeans(chr))
rat <- mymax / mymedian
return(c(mymax,mymedian,rat))
})## [[1]]
## [1] 9.1060461 0.3980865 22.8745395
##
## [[2]]
## [1] 1.091734e+04 4.011257e-01 2.721676e+04
##
## [[3]]
## [1] 65.9201579 0.3953286 166.7477395
##
## [[4]]
## [1] 11.65759 0.40583 28.72529
##
## [[5]]
## [1] 32.5209651 0.4079294 79.7220491
##
## [[6]]
## [1] 467.0126125 0.3969901 1176.3833788
##
## [[7]]
## [1] 4.4641873 0.4057538 11.0022058
##
## [[8]]
## [1] 4.2031070 0.4009495 10.4828847
##
## [[9]]
## [1] 1582.6649195 0.4059112 3899.0420460
##
## [[10]]
## [1] 10.0479879 0.4080726 24.6230368
##
## [[11]]
## [1] 76.4082733 0.4151408 184.0538718
##
## [[12]]
## [1] 14.1740149 0.3939939 35.9752140
##
## [[13]]
## [1] 12.6176231 0.3960173 31.8612911
##
## [[14]]
## [1] 16.2689007 0.3896999 41.7472486
##
## [[15]]
## [1] 15.9349362 0.3996783 39.8694104
##
## [[16]]
## [1] 36.1088684 0.3938448 91.6829975
##
## [[17]]
## [1] 110.6855744 0.4092534 270.4573013
##
## [[18]]
## [1] 30.8966861 0.3966464 77.8947885
##
## [[19]]
## [1] 3.9744729 0.4053219 9.8057203lapply(1:19,function(i){
mychr=as.character(i)
regex=paste("^",mychr,":",sep="")
chr <- x[grep(regex,rownames(x)),]
top <- head(chr[order(-rowMeans(chr)),])
return(top)
})## [[1]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 1:78585000-78586000 11.471556 8.779043 7.071574 11.036218
## 1:88182000-88183000 10.162215 3.432518 3.744919 2.472537
## 1:88178000-88179000 3.890307 4.443231 4.951075 3.390454
## 1:88147000-88148000 4.386025 3.447166 2.898665 3.751253
## 1:88222000-88223000 3.141342 4.203980 3.774100 3.411677
## 1:88144000-88145000 3.206001 3.857310 4.406359 3.167607
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 1:78585000-78586000 11.847622 12.120757 7.904431 5.881968
## 1:88182000-88183000 3.737474 3.694473 3.954891 2.585813
## 1:88178000-88179000 4.349988 3.156389 3.071864 3.538709
## 1:88147000-88148000 3.680759 4.149349 3.537460 4.062802
## 1:88222000-88223000 3.538974 3.012161 4.147552 2.984297
## 1:88144000-88145000 4.253574 3.078728 3.574922 2.806712
## ERX1059314.bam
## 1:78585000-78586000 5.841246
## 1:88182000-88183000 2.821080
## 1:88178000-88179000 3.443751
## 1:88147000-88148000 4.002884
## 1:88222000-88223000 3.384449
## 1:88144000-88145000 3.164184
##
## [[2]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 2:98497000-98498000 3685.07986 4081.44458 8312.72785 7614.06735
## 2:98496000-98497000 1268.08922 1460.59257 1283.42749 460.36417
## 2:98495000-98496000 290.47985 350.33168 291.71946 114.24608
## 2:98492000-98493000 77.55828 90.28059 147.95348 24.49722
## 2:22635000-22636000 49.22693 31.79107 47.98360 41.69886
## 2:98493000-98494000 37.85236 53.32854 33.94745 13.78466
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 2:98497000-98498000 8442.913435 11736.75404 8535.52238 26732.316759
## 2:98496000-98497000 121.408346 820.26740 956.48966 156.045394
## 2:98495000-98496000 27.217202 217.71876 229.71015 36.383304
## 2:98492000-98493000 30.262761 43.36291 43.74463 73.364335
## 2:22635000-22636000 20.235670 19.97567 26.05198 11.040600
## 2:98493000-98494000 4.259245 17.49050 24.23776 5.799672
## ERX1059314.bam
## 2:98497000-98498000 19115.236664
## 2:98496000-98497000 133.090598
## 2:98495000-98496000 36.280107
## 2:98492000-98493000 56.599509
## 2:22635000-22636000 12.677917
## 2:98493000-98494000 5.942906
##
## [[3]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 3:5925000-5926000 129.317685 82.6001526 92.280639
## 3:3041000-3042000 2.219954 2.9198375 5.062936
## 3:3202000-3203000 1.320118 1.1474278 1.113749
## 3:56155000-56156000 2.742613 2.9637816 1.459059
## 3:106156000-106157000 4.294425 0.9765343 1.191565
## 3:3116000-3117000 1.681130 1.9140072 1.313153
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 3:5925000-5926000 99.2782112 36.6317796 43.640274
## 3:3041000-3042000 2.9978189 4.3840170 3.084275
## 3:3202000-3203000 1.9843969 2.4614015 2.352037
## 3:56155000-56156000 1.2415746 0.4764003 1.725197
## 3:106156000-106157000 0.8383281 1.6106867 1.508854
## 3:3116000-3117000 1.2787157 1.0548864 1.625346
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 3:5925000-5926000 60.399057 20.8381045 28.2955186
## 3:3041000-3042000 2.745412 2.9496465 3.9012234
## 3:3202000-3203000 1.739296 2.9323211 2.9312124
## 3:56155000-56156000 3.039754 0.2772061 0.5040669
## 3:106156000-106157000 1.198776 1.1608007 1.2326341
## 3:3116000-3117000 1.771406 1.4683262 1.2707568
##
## [[4]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 4:7391000-7392000 20.238218 9.540740 22.182565
## 4:3083000-3084000 6.859226 6.786913 15.252033
## 4:3050000-3051000 4.612331 4.970560 10.792175
## 4:3081000-3082000 3.949578 3.940316 8.345819
## 4:147488000-147489000 6.837673 4.189332 4.416086
## 4:147422000-147423000 3.701719 5.126805 4.644672
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 4:7391000-7392000 14.166684 10.163206 8.770214
## 4:3083000-3084000 6.775601 11.110335 7.477703
## 4:3050000-3051000 5.029969 8.546848 5.880093
## 4:3081000-3082000 4.021853 6.028732 4.160443
## 4:147488000-147489000 4.112052 4.242231 3.711115
## 4:147422000-147423000 3.581465 3.470916 4.199274
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 4:7391000-7392000 8.188070 5.132645 6.535926
## 4:3083000-3084000 6.898315 7.341631 7.925287
## 4:3050000-3051000 4.490059 4.768812 6.239416
## 4:3081000-3082000 3.564219 3.374118 4.210441
## 4:147488000-147489000 4.094035 3.854898 4.434941
## 4:147422000-147423000 5.758407 3.746614 3.697902
##
## [[5]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 5:146197000-146198000 60.5583943 39.725415 50.512634
## 5:146198000-146199000 7.0532021 3.749892 6.852715
## 5:15069000-15070000 0.7705179 1.215785 1.026205
## 5:26212000-26213000 3.9388012 1.289025 3.302338
## 5:26211000-26212000 3.9334129 1.259729 2.203180
## 5:15104000-15105000 0.7759061 1.098601 1.235337
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 5:146197000-146198000 46.898622 20.615656 20.940897
## 5:146198000-146199000 4.318982 1.015186 2.291017
## 5:15069000-15070000 2.308055 2.070073 2.751440
## 5:26212000-26213000 1.968479 1.769487 1.714103
## 5:26211000-26212000 2.143573 1.570987 1.991465
## 5:15104000-15105000 1.915421 2.654230 1.997013
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 5:146197000-146198000 27.818031 11.7466097 13.8724286
## 5:146198000-146199000 2.670488 0.6063884 0.6607935
## 5:15069000-15070000 1.851681 2.8716822 2.5245702
## 5:26212000-26213000 1.268348 0.9832155 1.0293130
## 5:26211000-26212000 1.204128 1.3816993 1.3851249
## 5:15104000-15105000 1.942660 2.4818611 2.6601176
##
## [[6]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 6:103626000-103627000 969.0205206 1101.174240 915.1171424
## 6:147138000-147139000 0.9860473 1.069305 0.7441203
## 6:47720000-47721000 4.9895074 1.435505 2.3150408
## 6:115999000-116000000 1.1099768 1.132780 2.5679444
## 6:47730000-47731000 3.7448246 1.010713 3.6087401
## 6:47725000-47726000 4.3752483 1.186489 1.0991580
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 6:103626000-103627000 178.8185705 94.832015 282.000376
## 6:147138000-147139000 0.5411992 3.601359 16.996797
## 6:47720000-47721000 2.3558081 2.608859 2.235545
## 6:115999000-116000000 2.0109263 3.584345 1.847237
## 6:47730000-47731000 2.0958203 1.661730 1.791764
## 6:47725000-47726000 1.7244091 1.735458 1.625346
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 6:103626000-103627000 423.703184 127.5061567 110.941306
## 6:147138000-147139000 24.494638 0.5890630 8.217561
## 6:47720000-47721000 1.787461 1.7975085 1.651984
## 6:115999000-116000000 1.723241 2.1483475 2.329721
## 6:47730000-47731000 1.081039 1.0525170 1.194511
## 6:47725000-47726000 1.610856 0.8749318 1.012370
##
## [[7]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 7:59418000-59419000 1.2177415 2.158141 5.379065 3.730030
## 7:26479000-26480000 4.8332485 4.536002 4.430677 3.188830
## 7:59421000-59422000 0.9914356 1.284143 5.865418 3.013737
## 7:59419000-59420000 1.4494357 1.674756 2.932709 2.732525
## 7:7281000-7282000 5.2858604 1.689404 2.349086 2.302749
## 7:11744000-11745000 2.7480008 1.635695 3.122387 1.193822
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 7:59418000-59419000 8.955191 3.927458 3.403668 5.344881
## 7:26479000-26480000 1.440544 6.068700 5.485472 2.555494
## 7:59421000-59422000 7.843591 3.378280 2.204892 4.188411
## 7:59419000-59420000 5.660089 3.500319 2.927369 4.110447
## 7:7281000-7282000 1.786501 2.268828 1.680427 1.347049
## 7:11744000-11745000 2.007687 2.346490 2.151375 1.533296
## ERX1059314.bam
## 7:59418000-59419000 6.061510
## 7:26479000-26480000 1.914607
## 7:59421000-59422000 5.133858
## 7:59419000-59420000 4.943244
## 7:7281000-7282000 1.054728
## 7:11744000-11745000 1.414776
##
## [[8]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 8:55574000-55575000 10.221485 2.758709 1.726554 2.530902
## 8:71600000-71601000 4.504566 5.019386 3.453107 3.098631
## 8:74345000-74346000 2.796495 2.661056 5.379065 2.886396
## 8:21058000-21059000 3.022801 3.510641 2.169135 2.897007
## 8:55801000-55802000 7.166355 2.236264 1.590375 1.819915
## 8:72588000-72589000 2.710283 3.842662 2.305314 2.886396
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 8:55574000-55575000 4.185517 5.136760 3.526757 3.950187
## 8:71600000-71601000 2.654230 3.211862 4.789753 2.338927
## 8:74345000-74346000 3.845231 2.607211 2.659785 2.165673
## 8:21058000-21059000 2.693930 3.400469 4.003056 3.183539
## 8:55801000-55802000 2.693930 3.677832 3.002292 2.330264
## 8:72588000-72589000 1.230701 3.211862 4.329509 2.529506
## ERX1059314.bam
## 8:55574000-55575000 3.791091
## 8:71600000-71601000 2.766014
## 8:74345000-74346000 3.325147
## 8:21058000-21059000 3.079467
## 8:55801000-55802000 2.533042
## 8:72588000-72589000 2.266183
##
## [[9]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam ERX1059309.bam
## 9:3000000-3001000 1132.52657 1155.15217 2183.86163 1331.9920
## 9:3024000-3025000 429.30239 465.89474 846.01123 151.7533
## 9:3001000-3002000 150.05701 143.44800 231.10041 219.8277
## 9:3003000-3004000 58.80183 47.76229 91.22039 165.3204
## 9:3023000-3024000 157.89689 155.89882 230.50706 138.3507
## 9:35216000-35217000 179.97789 190.93198 328.67743 100.0582
## ERX1059310.bam ERX1059311.bam ERX1059312.bam ERX1059313.bam
## 9:3000000-3001000 571.16425 2391.9718 1472.3809 2561.0121
## 9:3024000-3025000 48.56447 327.2494 200.1903 115.4780
## 9:3001000-3002000 62.68634 389.1068 253.3967 393.9662
## 9:3003000-3004000 133.42611 247.5464 186.8271 443.8980
## 9:3023000-3024000 35.19691 261.8527 153.1062 167.3069
## 9:35216000-35217000 39.20094 203.5954 115.7408 124.7298
## ERX1059314.bam
## 9:3000000-3001000 1443.92285
## 9:3024000-3025000 79.15121
## 9:3001000-3002000 193.46002
## 9:3003000-3004000 293.92182
## 9:3023000-3024000 78.97330
## 9:35216000-35217000 77.52887
##
## [[10]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 10:57982000-57983000 19.591629 5.004738 14.006969
## 10:57981000-57982000 15.561228 4.882671 10.476046
## 10:72133000-72134000 5.592990 7.343538 4.771124
## 10:57980000-57981000 9.095344 3.134675 3.652512
## 10:57750000-57751000 9.461744 2.153258 3.433653
## 10:57985000-57986000 5.528331 1.870063 2.796530
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 10:57982000-57983000 5.900132 11.603750 11.760186
## 10:57981000-57982000 6.107061 11.700164 9.851929
## 10:72133000-72134000 7.131095 8.563862 10.722849
## 10:57980000-57981000 3.730030 4.559831 5.208875
## 10:57750000-57751000 1.846444 2.382001 3.772135
## 10:57985000-57986000 2.541514 3.408531 3.444847
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 10:57982000-57983000 6.759171 7.553867 8.251448
## 10:57981000-57982000 5.753056 8.350835 9.462902
## 10:72133000-72134000 9.493880 12.617210 12.025595
## 10:57980000-57981000 3.360855 3.495396 4.121488
## 10:57750000-57751000 2.536696 1.567947 1.651984
## 10:57985000-57986000 2.552751 2.914996 3.617421
##
## [[11]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 11:108902000-108903000 150.55811 91.17901 106.88582
## 11:3143000-3144000 121.11140 34.12987 83.98832
## 11:3144000-3145000 62.82145 36.15618 46.89903
## 11:3141000-3142000 59.29216 36.28801 36.61753
## 11:3142000-3143000 63.48960 24.85280 43.38756
## 11:3145000-3146000 42.08752 29.38392 38.70398
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 11:108902000-108903000 118.49078 46.01800 48.04480
## 11:3143000-3144000 55.62148 43.23900 46.80221
## 11:3144000-3145000 36.48850 28.63506 32.49029
## 11:3141000-3142000 31.18262 25.62353 29.97183
## 11:3142000-3143000 31.81933 26.84856 28.58502
## 11:3145000-3146000 29.80840 27.81837 26.77661
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 11:108902000-108903000 67.57566 25.23875 33.68353
## 11:3143000-3144000 34.29892 24.78829 29.10880
## 11:3144000-3145000 34.79662 19.63399 21.42920
## 11:3141000-3142000 32.99846 20.67351 21.50544
## 11:3142000-3143000 23.01222 17.77584 19.42564
## 11:3145000-3146000 29.60549 20.67784 20.76840
##
## [[12]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 12:78397000-78398000 26.682549 15.0776894 20.898593
## 12:67106000-67107000 6.600590 5.4197653 7.022939
## 12:74851000-74852000 2.257671 2.1434928 2.426902
## 12:119519000-119520000 1.923601 1.3817960 1.570921
## 12:53882000-53883000 2.737224 2.0116606 2.003775
## 12:92857000-92858000 3.399977 0.9130596 1.468786
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 12:78397000-78398000 21.584296 9.7491917 9.757626
## 12:67106000-67107000 9.868926 5.9209751 11.338594
## 12:74851000-74852000 2.737831 1.2590579 3.372732
## 12:119519000-119520000 2.154185 2.7449731 2.008107
## 12:53882000-53883000 1.692574 0.6975861 0.948581
## 12:92857000-92858000 1.406057 0.9528006 1.508854
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 12:78397000-78398000 11.8860806 5.2755791 6.6545299
## 12:67106000-67107000 10.0772129 15.4802298 8.8995336
## 12:74851000-74852000 3.1414360 4.2837010 2.2492396
## 12:119519000-119520000 1.5733938 2.1829983 2.5754005
## 12:53882000-53883000 1.3379199 0.5370869 0.5125386
## 12:92857000-92858000 0.9633023 0.7406601 0.8768222
##
## [[13]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 13:45023000-45024000 21.962454 13.246688 19.930750
## 13:120058000-120059000 12.926380 6.689260 11.531432
## 13:120059000-120060000 11.843345 7.441191 10.748404
## 13:120072000-120073000 3.351483 4.047735 3.273156
## 13:120077000-120078000 3.938801 3.935433 3.511469
## 13:120071000-120072000 1.153083 1.635695 2.743032
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 13:45023000-45024000 17.175115 10.270963 8.082354
## 13:120058000-120059000 7.693518 5.064589 7.028375
## 13:120059000-120060000 7.698823 3.879260 5.142308
## 13:120072000-120073000 3.279030 2.903773 3.611265
## 13:120077000-120078000 2.743137 3.442559 3.062086
## 13:120071000-120072000 2.504373 4.684603 2.923405
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 13:45023000-45024000 11.511463 4.478612 6.900210
## 13:120058000-120059000 6.063453 4.288032 3.964761
## 13:120059000-120060000 6.411312 2.603139 2.621995
## 13:120072000-120073000 4.634555 4.149429 2.753306
## 13:120077000-120078000 3.221711 3.530047 2.439853
## 13:120071000-120072000 2.028287 4.443961 2.910033
##
## [[14]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 14:3052000-3053000 13.707675 12.841426 17.625437
## 14:3050000-3051000 6.309625 6.225406 6.366362
## 14:3053000-3054000 5.258919 5.356291 5.398519
## 14:25931000-25932000 3.556236 3.657121 3.433653
## 14:43768000-43769000 5.081107 1.406209 1.964867
## 14:25992000-25993000 4.100448 2.060487 2.431766
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 14:3052000-3053000 17.647337 8.405062 22.039254
## 14:3050000-3051000 8.155129 2.818702 10.312351
## 14:3053000-3054000 3.942264 2.892430 5.314273
## 14:25931000-25932000 2.308055 1.956644 1.908257
## 14:43768000-43769000 1.979091 1.985001 2.585022
## 14:25992000-25993000 3.029654 1.690087 2.135694
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 14:3052000-3053000 18.254579 26.343245 9.556091
## 14:3050000-3051000 7.267581 11.854893 3.172656
## 14:3053000-3054000 4.875380 7.402270 3.617421
## 14:25931000-25932000 2.879204 1.186789 1.402068
## 14:43768000-43769000 1.691131 1.853816 1.647748
## 14:25992000-25993000 1.857033 1.282078 1.499493
##
## [[15]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
## 15:85061000-85062000 32.453351 20.2093771 19.561122
## 15:74958000-74959000 13.082639 13.1343862 12.484684
## 15:74957000-74958000 3.222166 3.1346751 2.499855
## 15:52748000-52749000 2.979695 2.4071570 2.485264
## 15:3054000-3055000 2.925813 0.9277076 1.040796
## 15:3055000-3056000 1.875106 1.0741877 1.084567
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 15:85061000-85062000 26.046536 8.7226625 10.0571776
## 15:74958000-74959000 12.845521 15.9423955 13.1969253
## 15:74957000-74958000 1.544009 0.6068432 1.6586300
## 15:52748000-52749000 2.037456 0.7372862 0.9929591
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##
## [[16]]
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## 16:17040000-17041000 30.168738 17.572734 22.206883
## 16:3158000-3159000 4.596166 6.957807 4.450131
## 16:3161000-3162000 4.105837 5.180514 5.281795
## 16:3156000-3157000 3.518519 4.369991 3.569832
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 16:57211000-57212000 55.510055 20.706398 23.764451
## 16:91331000-91332000 48.320596 22.010828 25.667160
## 16:17040000-17041000 22.597718 9.255777 10.417749
## 16:3158000-3159000 5.799320 2.586173 5.231064
## 16:3161000-3162000 4.716922 6.244247 4.892681
## 16:3156000-3157000 4.138582 2.858402 4.582035
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 16:57211000-57212000 32.104726 12.448288 15.168601
## 16:91331000-91332000 26.308857 10.533833 15.931055
## 16:17040000-17041000 13.919719 4.647534 6.319897
## 16:3158000-3159000 5.758407 5.427176 5.099971
## 16:3161000-3162000 5.330273 4.353002 5.015254
## 16:3156000-3157000 4.864677 5.158633 5.142329
##
## [[17]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
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## 17:40158000-40159000 246.7705 78.76726 155.9978
## 17:40154000-40155000 198.5565 65.32038 124.7885
## 17:40155000-40156000 140.2989 57.86454 142.6620
## 17:40156000-40157000 133.7307 61.15058 117.0166
## 17:40159000-40160000 121.8873 54.17324 111.7639
## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 17:40157000-40158000 137.61315 90.28920 89.81010
## 17:40158000-40159000 131.45303 96.14779 92.13995
## 17:40154000-40155000 103.67678 80.16569 82.78727
## 17:40155000-40156000 98.55661 66.61664 66.73351
## 17:40156000-40157000 96.26978 48.39433 58.93961
## 17:40159000-40160000 81.14804 54.46276 53.07061
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 17:40157000-40158000 73.43575 48.29884 61.53852
## 17:40158000-40159000 74.30272 50.12666 65.93957
## 17:40154000-40155000 65.75609 40.22954 50.90652
## 17:40155000-40156000 53.11542 32.89657 42.01546
## 17:40156000-40157000 48.83408 23.21168 35.67438
## 17:40159000-40160000 45.62307 26.69842 33.43361
##
## [[18]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
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## ERX1059309.bam ERX1059310.bam ERX1059311.bam
## 18:73666000-73667000 40.1124086 24.0185147 21.0573890
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## 18:3091000-3092000 0.9762808 0.7316147 1.4145506
## 18:3008000-3009000 1.3052450 1.5993438 1.1815658
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## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 18:73666000-73667000 20.9143642 11.5950126 14.8382038
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## 18:3091000-3092000 1.5145254 0.8749318 0.7836334
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## 18:66769000-66770000 1.1559628 0.4764480 0.5125386
##
## [[19]]
## ERX1059306.bam ERX1059307.bam ERX1059308.bam
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## 19:36904000-36905000 3.685554 1.8651805 2.062137
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## ERX1059309.bam ERX1059310.bam ERX1059311.bam
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## 19:36904000-36905000 2.493761 0.9641434 1.625346
## 19:36902000-36903000 2.456620 1.3384580 1.375720
## 19:36895000-36896000 2.509678 1.3498008 1.353531
## 19:36897000-36898000 1.862362 1.3327865 1.614252
## ERX1059312.bam ERX1059313.bam ERX1059314.bam
## 19:8229000-8230000 2.429663 0.3465077 0.9911903
## 19:39248000-39249000 2.873852 2.9799659 3.1006466
## 19:36904000-36905000 1.418195 0.8489438 1.0928509
## 19:36902000-36903000 1.541284 1.0178663 0.9149449
## 19:36895000-36896000 1.027523 0.8099617 1.0716716
## 19:36897000-36898000 1.241590 1.0568484 0.9361242Session information
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] kableExtra_1.3.4 dplyr_1.0.7 gplots_3.1.1 quantreg_5.86
## [5] SparseM_1.81 cobs_1.3-4
##
## loaded via a namespace (and not attached):
## [1] gtools_3.9.2 tidyselect_1.1.1 xfun_0.25 bslib_0.2.5.1
## [5] purrr_0.3.4 splines_4.1.1 lattice_0.20-44 colorspace_2.0-2
## [9] vctrs_0.3.8 generics_0.1.0 htmltools_0.5.1.1 viridisLite_0.4.0
## [13] yaml_2.2.1 utf8_1.2.2 rlang_0.4.11 jquerylib_0.1.4
## [17] pillar_1.6.2 glue_1.4.2 DBI_1.1.1 matrixStats_0.60.0
## [21] lifecycle_1.0.0 stringr_1.4.0 MatrixModels_0.5-0 munsell_0.5.0
## [25] rvest_1.0.1 caTools_1.18.2 evaluate_0.14 knitr_1.33
## [29] fansi_0.5.0 highr_0.9 Rcpp_1.0.7 KernSmooth_2.23-20
## [33] conquer_1.0.2 scales_1.1.1 jsonlite_1.7.2 webshot_0.5.2
## [37] systemfonts_1.0.2 digest_0.6.27 stringi_1.7.3 grid_4.1.1
## [41] tools_4.1.1 bitops_1.0-7 sass_0.4.0 magrittr_2.0.1
## [45] tibble_3.1.3 crayon_1.4.1 pkgconfig_2.0.3 ellipsis_0.3.2
## [49] Matrix_1.3-4 xml2_1.3.2 assertthat_0.2.1 rmarkdown_2.10
## [53] svglite_2.0.0 httr_1.4.2 rstudioapi_0.13 R6_2.5.0
## [57] compiler_4.1.1