Methylation Main Data QC Analysis
Aaron Kovacs & Mark Ziemann
2021-10-04
This code is available at https://github.com/aaronsk7/guppy-methylation
This script performs differential methylation analysis using MethylKit.
library("R.utils")## Loading required package: R.oo## Loading required package: R.methodsS3## R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.## R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.##
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## attach, detach, load, save## R.utils v2.11.0 (2021-09-26 08:30:02 UTC) successfully loaded. See ?R.utils for help.##
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## timestamp## The following objects are masked from 'package:base':
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## cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,
## warningslibrary("parallel")
library("reshape2")
# library("kableExtra") having some trouble installing dependancies on bio2
library("dplyr")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary("RColorBrewer")
library("GenomicRanges")## Loading required package: stats4## Loading required package: BiocGenerics##
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
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## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which, which.max, which.min## Loading required package: S4Vectors##
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## trim## Loading required package: GenomeInfoDblibrary("limma")##
## Attaching package: 'limma'## The following object is masked from 'package:BiocGenerics':
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## plotMAlibrary("methylKit")##
## Attaching package: 'methylKit'## The following object is masked from 'package:dplyr':
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## selectlibrary("gplots")##
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## lowesslibrary("seqinr")##
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## getNameImport biscuit data with a function
Define and test the function
vcfz <- "meth_data/test-R1.bam.vcf.gz.cg.bed.gz"
biscuit2methylkit <- function(vcfz) {
vcf <- gsub(".gz$","",vcfz)
gunzip(vcfz)
tdata <- read.table(vcf)
gzip(vcf)
chrBase <- paste(tdata$V1,tdata$V2,sep=".")
chr <- tdata$V1
base <- tdata$V2
strand="F"
coverage <- tdata$V5
freqC <- tdata$V4 *100
freqT <- 100-freqC
mk <- data.frame( chrBase , chr , base, strand, coverage, freqC, freqT )
mk
mkname <- paste(vcf,".mk",sep="")
mkname <- gsub(".bam.vcf.gz.cg.bed","",mkname)
write.table(mk,file=mkname,quote=FALSE,sep="\t",row.names = FALSE)
}
biscuit2methylkit(vcfz)
getwd()## [1] "/home/ziemannm/guppy_methylseq/main_data1"myobj=methRead("meth_data/test-R1.mk",
sample.id="test1",
assembly="hg18",
treatment=1,
context="CpG",
mincov = 10 )## Received single location.## Reading file.myobj## chr start end strand coverage numCs numTs
## 1 KK215283.1 113 113 + 126 3 123
## 2 KK215283.1 2071 2071 + 10 8 2
## 3 KK215283.1 2111 2111 + 13 11 2
## 4 KK215283.1 2139 2139 + 13 13 0
## 5 KK215283.1 4814 4814 + 10 8 2
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## 18 KK215283.1 12497 12497 + 10 10 0
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## 30 KK215283.1 16188 16188 + 10 7 3
## 31 KK215283.1 16647 16647 + 10 8 2
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## 75 KK215283.1 33150 33150 + 10 0 10
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## 81 KK215283.1 37831 37831 + 13 10 3
## 82 KK215283.1 38700 38700 + 10 9 1
## 83 KK215283.1 38720 38720 + 11 11 0
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## 85 KK215283.1 38856 38856 + 10 10 0
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## 89 KK215283.1 41681 41681 + 10 10 0
## 90 KK215283.1 42120 42120 + 10 9 1
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## 98 KK215283.1 42374 42374 + 11 10 1
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## 375 KK215283.1 173311 173311 + 11 4 7
## 376 KK215283.1 173315 173315 + 12 5 7
## 377 KK215283.1 173327 173327 + 11 6 5
## 378 KK215283.1 173338 173338 + 11 7 4
## 379 KK215283.1 173341 173341 + 11 7 4
## 380 KK215283.1 173346 173346 + 10 6 4
## 381 KK215283.1 173349 173349 + 10 7 3
## 382 KK215283.1 173355 173355 + 11 7 4
## 383 KK215283.1 173675 173675 + 10 8 2
## 384 KK215283.1 174662 174662 + 10 9 1
## 385 KK215283.1 174749 174749 + 10 10 0
## 386 KK215283.1 177479 177479 + 10 6 4
## 387 KK215283.1 178687 178687 + 10 7 3
## 388 KK215283.1 178696 178696 + 11 7 4
## 389 KK215283.1 182396 182396 + 11 7 4
## 390 KK215283.1 182423 182423 + 10 6 4
## 391 KK215283.1 182886 182886 + 10 5 5
## 392 KK215283.1 182890 182890 + 10 7 3
## 393 KK215283.1 183275 183275 + 10 3 7
## 394 KK215283.1 187169 187169 + 10 10 0
## 395 KK215283.1 192320 192320 + 11 4 7
## 396 KK215283.1 192351 192351 + 11 6 5
## 397 KK215283.1 192396 192396 + 13 4 9
## 398 KK215283.1 192432 192432 + 12 5 7
## 399 KK215283.1 192441 192441 + 11 4 7
## 400 KK215283.1 192449 192449 + 13 7 6
## 401 KK215283.1 193447 193447 + 11 8 3
## 402 KK215283.1 193738 193738 + 10 6 4
## 403 KK215283.1 197639 197639 + 10 9 1
## 404 KK215283.1 197640 197640 + 10 9 1
## 405 KK215283.1 197644 197644 + 10 10 0
## 406 KK215283.1 197645 197645 + 11 10 1
## 407 KK215283.1 197674 197674 + 13 12 1
## 408 KK215283.1 197678 197678 + 13 13 0
## 409 KK215283.1 197683 197683 + 12 12 0
## 410 KK215283.1 197685 197685 + 12 12 0
## 411 KK215283.1 197697 197697 + 11 10 1
## 412 KK215283.1 197704 197704 + 10 10 0
## 413 KK215283.1 197720 197720 + 10 10 0
## 414 KK215283.1 199955 199955 + 10 0 10
## 415 KK215283.1 199959 199959 + 10 4 6
## 416 KK215283.1 200127 200127 + 10 1 9
## 417 KK215283.1 200180 200180 + 10 1 9
## 418 KK215283.1 200215 200215 + 10 0 10
## 419 KK215283.1 200587 200587 + 11 6 5
## 420 KK215283.1 200628 200628 + 12 7 5
## 421 KK215283.1 200639 200639 + 11 9 2
## 422 KK215283.1 200663 200663 + 11 8 3
## 423 KK215283.1 201801 201801 + 10 9 1
## 424 KK215283.1 201805 201805 + 10 9 1
## 425 KK215283.1 203495 203495 + 12 11 1
## 426 KK215283.1 203500 203500 + 11 10 1
## 427 KK215283.1 203570 203570 + 10 5 5
## 428 KK215283.1 203645 203645 + 11 11 0
## 429 KK215283.1 203724 203724 + 11 11 0
## 430 KK215283.1 203777 203777 + 10 9 1
## 431 KK215283.1 204841 204841 + 10 6 4
## 432 KK215283.1 206562 206562 + 10 1 9
## 433 KK215283.1 206637 206637 + 10 0 10
## 434 KK215283.1 206678 206678 + 11 1 10
## 435 KK215283.1 206681 206681 + 12 0 12
## 436 KK215283.1 206686 206686 + 12 0 12
## 437 KK215283.1 206703 206703 + 10 0 10
## 438 KK215283.1 206712 206712 + 11 1 10
## 439 KK215283.1 206719 206719 + 10 1 9
## 440 KK215283.1 206727 206727 + 10 0 10
## 441 KK215283.1 208819 208819 + 11 10 1
## 442 KK215283.1 208839 208839 + 10 7 3
## 443 KK215283.1 208901 208901 + 10 0 10
## 444 KK215283.1 208911 208911 + 10 5 5
## 445 KK215283.1 209476 209476 + 12 9 3
## 446 KK215283.1 209577 209577 + 10 7 3
## 447 KK215283.1 209602 209602 + 11 10 1
## 448 KK215283.1 209608 209608 + 12 11 1Now format all the data into methylkit format
This might take a few minutes - get a cuppa.
basenames <- c("Clear2F-R1","Clear2F-R2","Clear2F-R3","Foundation","Green3F-R1","Green3F-R2","Green3F-R3",
"Lilac4F-R1","Lilac4F-R2","Lilac4F-R3")
for (i in basenames){
bedgz <- gsub("$","\\.bam\\.vcf\\.gz\\.cg\\.bed\\.gz",i)
bedgz <- paste("meth_data/",bedgz,sep = "")
mk <- gsub("$","\\.mk",i)
mk <- paste("meth_data/",mk,sep = "")
message(mk)
if(file.exists(mk)){
message(paste(mk,"exists"))
}else{
biscuit2methylkit(vcfz = bedgz)
}}## meth_data/Clear2F-R1.mk## meth_data/Clear2F-R1.mk exists## meth_data/Clear2F-R2.mk## meth_data/Clear2F-R2.mk exists## meth_data/Clear2F-R3.mk## meth_data/Clear2F-R3.mk exists## meth_data/Foundation.mk## meth_data/Foundation.mk exists## meth_data/Green3F-R1.mk## meth_data/Green3F-R1.mk exists## meth_data/Green3F-R2.mk## meth_data/Green3F-R2.mk exists## meth_data/Green3F-R3.mk## meth_data/Green3F-R3.mk exists## meth_data/Lilac4F-R1.mk## meth_data/Lilac4F-R1.mk exists## meth_data/Lilac4F-R2.mk## meth_data/Lilac4F-R2.mk exists## meth_data/Lilac4F-R3.mk## meth_data/Lilac4F-R3.mk existsmyfiles2 <- list.files("meth_data",pattern = "mk$",full.names = TRUE)
myfiles2## [1] "meth_data/Clear2F-R1.mk" "meth_data/Clear2F-R2.mk"
## [3] "meth_data/Clear2F-R3.mk" "meth_data/Foundation.mk"
## [5] "meth_data/Green3F-R1.mk" "meth_data/Green3F-R2.mk"
## [7] "meth_data/Green3F-R3.mk" "meth_data/Lilac4F-R1.mk"
## [9] "meth_data/Lilac4F-R2.mk" "meth_data/Lilac4F-R3.mk"
## [11] "meth_data/test-R1.mk"AllSamples <- as.list(myfiles2[c(grep("Clear",myfiles2) , grep("Green",myfiles2), grep("Lilac",myfiles2), grep("Foundation",myfiles2))])
AllSamples## [[1]]
## [1] "meth_data/Clear2F-R1.mk"
##
## [[2]]
## [1] "meth_data/Clear2F-R2.mk"
##
## [[3]]
## [1] "meth_data/Clear2F-R3.mk"
##
## [[4]]
## [1] "meth_data/Green3F-R1.mk"
##
## [[5]]
## [1] "meth_data/Green3F-R2.mk"
##
## [[6]]
## [1] "meth_data/Green3F-R3.mk"
##
## [[7]]
## [1] "meth_data/Lilac4F-R1.mk"
##
## [[8]]
## [1] "meth_data/Lilac4F-R2.mk"
##
## [[9]]
## [1] "meth_data/Lilac4F-R3.mk"
##
## [[10]]
## [1] "meth_data/Foundation.mk"myobj <- methRead(AllSamples,
sample.id = list("clear1","clear2","clear3","green1","green2","green3","Lilac1","Lilac2","Lilac3","Foundation"),
assembly = "hg19",
treatment = c(0,0,0,0,0,1,1,1,1,1),
context = "CpG",
mincov = 3 )## Received list of locations.## Reading file.
## Reading file.
## Reading file.
## Reading file.
## Reading file.
## Reading file.
## Reading file.
## Reading file.
## Reading file.
## Reading file.Simple stats
And prepare for differential methylation analysis.
lapply(myobj,function(x){
getMethylationStats(x,plot=FALSE,both.strands=FALSE)
getMethylationStats(x,plot=TRUE,both.strands=FALSE)
getCoverageStats(x,plot=TRUE,both.strands=FALSE)
})## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 60.00 83.33 73.34 100.00 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 16.66667 50.00000 66.66667 75.00000 83.33333 90.90909 100.00000
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 63.64 84.21 73.47 94.44 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 54.54545 70.00000 77.77778 84.21053 88.88889 92.30769
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 62.50 83.33 72.98 93.33 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 54.54545 68.75000 77.77778 83.33333 88.23529 91.66667
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 63.64 83.33 72.87 92.86 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 54.54545 69.23077 77.77778 83.33333 87.50000 91.66667
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 94.73684 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 62.50 83.33 73.35 94.12 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 54.54545 69.23077 77.77778 83.33333 88.88889 92.30769
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 62.50 82.14 72.06 92.31 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 54.54545 68.75000 76.47059 82.14286 86.66667 90.47619
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 93.75000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 63.64 84.21 73.39 93.33 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 55.55556 70.00000 78.57143 84.21053 88.88889 92.30769
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 96.15385 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 61.54 83.33 72.36 92.86 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 18.75000 53.33333 66.66667 76.92308 83.33333 87.50000 90.90909
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 62.50 83.33 73.18 100.00 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 16.66667 50.00000 66.66667 77.77778 83.33333 88.88889 94.44444
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 68.18 88.00 75.45 95.45 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 20.00000 57.69231 75.00000 83.33333 88.00000 91.30435 94.44444
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 96.15385 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## NULL
##
## [[5]]
## NULL
##
## [[6]]
## NULL
##
## [[7]]
## NULL
##
## [[8]]
## NULL
##
## [[9]]
## NULL
##
## [[10]]
## NULLmyobj <- filterByCoverage(myobj,lo.count=1,lo.perc=NULL,
hi.count=NULL,hi.perc=99.9)
tiles <- tileMethylCounts(myobj,win.size=1000,step.size=1000,cov.bases = 10)
meth <- unite(myobj, destrand=FALSE)## uniting...remove(myobj)
head(meth)## chr start end strand coverage1 numCs1 numTs1 coverage2 numCs2 numTs2
## 1 KK215283.1 652 652 + 3 3 0 7 7 0
## 2 KK215283.1 772 772 + 3 3 0 9 7 2
## 3 KK215283.1 775 775 + 4 2 2 9 7 2
## 4 KK215283.1 846 846 + 4 4 0 7 6 1
## 5 KK215283.1 1662 1662 + 3 2 1 11 10 1
## 6 KK215283.1 1693 1693 + 4 2 2 7 7 0
## coverage3 numCs3 numTs3 coverage4 numCs4 numTs4 coverage5 numCs5 numTs5
## 1 10 8 2 16 15 1 7 5 2
## 2 11 11 0 12 10 2 7 7 0
## 3 10 9 1 12 8 4 6 6 0
## 4 4 3 1 9 9 0 7 7 0
## 5 3 2 1 9 6 3 5 5 0
## 6 4 3 1 12 11 1 4 4 0
## coverage6 numCs6 numTs6 coverage7 numCs7 numTs7 coverage8 numCs8 numTs8
## 1 16 14 2 12 12 0 13 11 2
## 2 18 17 1 15 15 0 11 9 2
## 3 18 17 1 15 14 1 12 10 2
## 4 13 9 4 8 7 1 4 3 1
## 5 8 8 0 7 6 1 6 5 1
## 6 8 7 1 9 5 4 5 4 1
## coverage9 numCs9 numTs9 coverage10 numCs10 numTs10
## 1 8 8 0 16 15 1
## 2 11 10 1 10 9 1
## 3 10 8 2 8 6 2
## 4 3 3 0 11 11 0
## 5 4 4 0 12 11 1
## 6 5 4 1 12 12 0# plots
getCorrelation(meth,plot=TRUE)## clear1 clear2 clear3 green1 green2 green3
## clear1 1.0000000 0.8010335 0.7972176 0.8089492 0.8004106 0.8102666
## clear2 0.8010335 1.0000000 0.8514886 0.8636977 0.8549608 0.8652150
## clear3 0.7972176 0.8514886 1.0000000 0.8594294 0.8506624 0.8617598
## green1 0.8089492 0.8636977 0.8594294 1.0000000 0.8631310 0.8739499
## green2 0.8004106 0.8549608 0.8506624 0.8631310 1.0000000 0.8647369
## green3 0.8102666 0.8652150 0.8617598 0.8739499 0.8647369 1.0000000
## Lilac1 0.8100390 0.8646971 0.8612553 0.8733649 0.8642651 0.8753751
## Lilac2 0.7959160 0.8496160 0.8472358 0.8580093 0.8493268 0.8606130
## Lilac3 0.7811309 0.8341844 0.8299141 0.8427774 0.8334585 0.8438452
## Foundation 0.8236716 0.8803310 0.8714570 0.8894215 0.8790812 0.8872556
## Lilac1 Lilac2 Lilac3 Foundation
## clear1 0.8100390 0.7959160 0.7811309 0.8236716
## clear2 0.8646971 0.8496160 0.8341844 0.8803310
## clear3 0.8612553 0.8472358 0.8299141 0.8714570
## green1 0.8733649 0.8580093 0.8427774 0.8894215
## green2 0.8642651 0.8493268 0.8334585 0.8790812
## green3 0.8753751 0.8606130 0.8438452 0.8872556
## Lilac1 1.0000000 0.8602507 0.8432553 0.8865881
## Lilac2 0.8602507 1.0000000 0.8283482 0.8654853
## Lilac3 0.8432553 0.8283482 1.0000000 0.8595416
## Foundation 0.8865881 0.8654853 0.8595416 1.0000000
clusterSamples(meth, dist="correlation", method="ward", plot=TRUE)## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
##
## Call:
## hclust(d = d, method = HCLUST.METHODS[hclust.method])
##
## Cluster method : ward.D
## Distance : pearson
## Number of objects: 10hc = clusterSamples(meth, dist="correlation", method="ward", plot=FALSE)## The "ward" method has been renamed to "ward.D"; note new "ward.D2"PCASamples(meth, screeplot=TRUE)
PCASamples(meth)
Correlation heatmap
perc <- percMethylation(meth)
heatmap.2(cor(perc,method="pearson"),trace="none",scale="none",margins=c(7,7),main="Pearson")
heatmap.2(cor(perc,method="spearman"),trace="none",scale="none",margins=c(7,7),main="Spearman")
Aggregate to 1kbp tiles
#tiles <- tileMethylCounts(myobj,win.size=1000,step.size=1000,cov.bases = 10)
tiles <- unite(tiles, destrand=FALSE)## uniting...head(tiles)## chr start end strand coverage1 numCs1 numTs1 coverage2 numCs2 numTs2
## 1 KK215283.1 1001 2000 * 91 67 24 333 302 31
## 2 KK215283.1 2001 3000 * 90 73 17 144 108 36
## 3 KK215283.1 3001 4000 * 111 97 14 336 284 52
## 4 KK215283.1 4001 5000 * 139 115 24 269 235 34
## 5 KK215283.1 5001 6000 * 463 439 24 1133 1078 55
## 6 KK215283.1 6001 7000 * 403 370 33 978 919 59
## coverage3 numCs3 numTs3 coverage4 numCs4 numTs4 coverage5 numCs5 numTs5
## 1 230 201 29 316 278 38 214 198 16
## 2 153 126 27 180 131 49 147 107 40
## 3 338 308 30 366 326 40 348 320 28
## 4 227 204 23 308 277 31 294 257 37
## 5 812 778 34 1570 1460 110 1084 973 111
## 6 1011 904 107 1286 1184 102 756 718 38
## coverage6 numCs6 numTs6 coverage7 numCs7 numTs7 coverage8 numCs8 numTs8
## 1 355 321 34 326 277 49 221 183 38
## 2 145 101 44 214 170 44 133 93 40
## 3 480 411 69 387 344 43 371 316 55
## 4 423 359 64 379 323 56 325 286 39
## 5 1674 1544 130 1253 1169 84 1018 913 105
## 6 1368 1240 128 1253 1162 91 699 643 56
## coverage9 numCs9 numTs9 coverage10 numCs10 numTs10
## 1 166 151 15 559 508 51
## 2 83 61 22 314 240 74
## 3 290 255 35 677 622 55
## 4 231 198 33 640 558 82
## 5 915 847 68 2215 2139 76
## 6 865 792 73 1680 1572 108getCorrelation(tiles,plot=TRUE)## clear1 clear2 clear3 green1 green2 green3
## clear1 1.0000000 0.9540509 0.9529642 0.9558521 0.9542974 0.9561720
## clear2 0.9540509 1.0000000 0.9702236 0.9733221 0.9715975 0.9734557
## clear3 0.9529642 0.9702236 1.0000000 0.9717734 0.9704185 0.9730248
## green1 0.9558521 0.9733221 0.9717734 1.0000000 0.9735249 0.9755674
## green2 0.9542974 0.9715975 0.9704185 0.9735249 1.0000000 0.9737691
## green3 0.9561720 0.9734557 0.9730248 0.9755674 0.9737691 1.0000000
## Lilac1 0.9558441 0.9731208 0.9726377 0.9750421 0.9733495 0.9758018
## Lilac2 0.9517172 0.9688151 0.9696202 0.9706265 0.9693359 0.9722097
## Lilac3 0.9495700 0.9668239 0.9649684 0.9689414 0.9668143 0.9686881
## Foundation 0.9431059 0.9612601 0.9548864 0.9636135 0.9606208 0.9597633
## Lilac1 Lilac2 Lilac3 Foundation
## clear1 0.9558441 0.9517172 0.9495700 0.9431059
## clear2 0.9731208 0.9688151 0.9668239 0.9612601
## clear3 0.9726377 0.9696202 0.9649684 0.9548864
## green1 0.9750421 0.9706265 0.9689414 0.9636135
## green2 0.9733495 0.9693359 0.9668143 0.9606208
## green3 0.9758018 0.9722097 0.9686881 0.9597633
## Lilac1 1.0000000 0.9720180 0.9681887 0.9591494
## Lilac2 0.9720180 1.0000000 0.9635826 0.9485543
## Lilac3 0.9681887 0.9635826 1.0000000 0.9578280
## Foundation 0.9591494 0.9485543 0.9578280 1.0000000
clusterSamples(tiles, dist="correlation", method="ward", plot=TRUE)## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
##
## Call:
## hclust(d = d, method = HCLUST.METHODS[hclust.method])
##
## Cluster method : ward.D
## Distance : pearson
## Number of objects: 10hc = clusterSamples(tiles, dist="correlation", method="ward", plot=FALSE)## The "ward" method has been renamed to "ward.D"; note new "ward.D2"PCASamples(tiles, screeplot=TRUE)
PCASamples(tiles)
perc <- percMethylation(tiles)
heatmap.2(cor(perc,method="pearson"),trace="none",scale="none",margins=c(7,7),main="Pearson")
heatmap.2(cor(perc,method="spearman"),trace="none",scale="none",margins=c(7,7),main="Spearman")
Session information
For reproducibility
sessionInfo()## R version 4.0.1 (2020-06-06)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS/LAPACK: /ceph-g/opt/miniconda3/envs/R40/lib/libopenblasp-r0.3.9.so
##
## 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] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] seqinr_4.2-8 gplots_3.1.1 methylKit_1.14.2
## [4] limma_3.44.3 GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
## [7] IRanges_2.22.2 S4Vectors_0.26.1 BiocGenerics_0.34.0
## [10] RColorBrewer_1.1-2 dplyr_1.0.0 reshape2_1.4.4
## [13] R.utils_2.11.0 R.oo_1.24.0 R.methodsS3_1.8.1
##
## loaded via a namespace (and not attached):
## [1] mclust_5.4.7 Rcpp_1.0.7
## [3] bdsmatrix_1.3-4 mvtnorm_1.1-2
## [5] lattice_0.20-41 Rsamtools_2.4.0
## [7] Biostrings_2.56.0 gtools_3.9.2
## [9] digest_0.6.25 R6_2.4.1
## [11] plyr_1.8.6 emdbook_1.3.12
## [13] evaluate_0.14 coda_0.19-4
## [15] ggplot2_3.3.5 pillar_1.4.4
## [17] zlibbioc_1.34.0 rlang_0.4.11
## [19] data.table_1.12.8 Matrix_1.2-18
## [21] bbmle_1.0.24 qvalue_2.20.0
## [23] rmarkdown_2.3 splines_4.0.1
## [25] BiocParallel_1.22.0 stringr_1.4.0
## [27] fastseg_1.34.0 RCurl_1.98-1.5
## [29] munsell_0.5.0 DelayedArray_0.14.1
## [31] compiler_4.0.1 numDeriv_2016.8-1.1
## [33] rtracklayer_1.48.0 xfun_0.15
## [35] pkgconfig_2.0.3 htmltools_0.5.0
## [37] SummarizedExperiment_1.18.2 tidyselect_1.1.0
## [39] tibble_3.0.1 GenomeInfoDbData_1.2.3
## [41] matrixStats_0.61.0 XML_3.99-0.8
## [43] crayon_1.3.4 GenomicAlignments_1.24.0
## [45] MASS_7.3-51.6 bitops_1.0-7
## [47] grid_4.0.1 gtable_0.3.0
## [49] lifecycle_0.2.0 magrittr_1.5
## [51] scales_1.1.1 KernSmooth_2.23-17
## [53] stringi_1.4.6 XVector_0.28.0
## [55] ellipsis_0.3.1 generics_0.0.2
## [57] vctrs_0.3.1 tools_4.0.1
## [59] ade4_1.7-18 Biobase_2.48.0
## [61] glue_1.4.1 purrr_0.3.4
## [63] yaml_2.2.1 colorspace_1.4-1
## [65] caTools_1.18.2 knitr_1.28