Introductions

In this package, we provide e-value for four DMR (differentially methylated region) detection tools (MethylKit, Metilene, BiSeq and DMRfinder) and general purpose.

MethylKit
BiSeq
DMRfinder
Metilene
Other DNA methylation tools
RNA-seq data

For DMR (methylKit, biseq, DMRfinder or metilene), the met-evalue calculation is conducted by the metevalue.[DMR] function.

DMR	Method	Input.1 Example	Input.2 Example
MethylKit	`metevalue.methylKit`	`data(demo_methylkit_methyrate)`	`data(demo_methylkit_met_all)`
BiSeq	`metevalue.biseq`	`data(demo_biseq_methyrate)`	`data(demo_biseq_DMR)`
DMRfinder	`metevalue.DMRfinder`	`data(demo_DMRfinder_rate_combine)`	`data(demo_DMRfinder_DMRs)`
Metilene	`metevalue.metilene`	`data(demo_metilene_input)`	`data(demo_metilene_out)`
Other DNA methylation tools	`varevalue.single_general`	`data(demo_metilene_input)` or any data above
RNA-seq data	`metevalue.RNA_general`	`data(demo_desq_out)`

Two routines are supported to calculate the combined e-value:

Call by files: Here the files include the outputs of given DMR packages and its corresponding e-value of each region;
Call by R data frames: Here the R data frames are corresponding data.frame objects.

Other Demos

Please vist the metevalue-emo project for more demos.

Call by files

We design the metevalue.[DMR] function to accept similar parameter patterns:

metevalue.[DMR](
  methyrate,                # methylation rates of each CpG site
  [DMR].output,             # Output file name of [DMR] with e-value of each region
  adjust.methods = "BH",    # Adjust methods of e-value
  sep = "\t",               # seperator, default is the TAB key
  bheader = FALSE           # A logical value indicating whether the [DMR].output file
                            # contains the names of the variables as its first line
)

Here [DMR] could be one of methylKit, biseq, DMRfinder or metilene.

Call by R data frames

We provide the evalue_buildin_var_fmt_nm and varevalue.metilene function to handle the general DMR e-value calculation in DNA methylation studies:

# Here  `[DMR]` coudle be one of `methylKit`, `biseq`, `DMRfinder` or `metilene`.
method_in_use = "[DMR]"
result = evalue_buildin_var_fmt_nm(
          methyrate,              # Data frame of the methylation rate
          DMR_evalue_output,      # Data frame of output data corresponding to the
                                  # "method" option
          method = method_in_use) # DMR: "metilene", "biseq", "DMRfinder" or "methylKit"
result = list(a = result$a,
              b = result$b,
              a_b = evalue_buildin_sql(result$a, result$b, method = method_in_use))
result = varevalue.metilene(result$a, result$b, result$a_b)

Replace [DMR] to one of methylKit, biseq, DMRfinder or metilene accordingly.

For RNAseq user, metevalue.RNA_general could be called directly. Example is:

data("demo_desq_out")
evalue = metevalue.RNA_general(demo_desq_out, 'treated','untreated')

Notice: for different [DMR], the data.frame schemas are different!!! Check the R help document for details. Check the Demo data section for details.

Example: MethylKit

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing.

Currently, metevalue package supports the e-value calculation using the methylKit output file.

library(metevalue)

####Simulation Data ####
set.seed(1234)
simu_g_value <- function(n, r = 0.1){
  x = runif(n)
  x[runif(n) <= r] = 0
  return(x);
}


library(methylKit)
file.list=list( system.file("extdata", 
                            "test1.myCpG.txt", package = "methylKit"),
                system.file("extdata",
                            "test2.myCpG.txt", package = "methylKit"),
                system.file("extdata", 
                            "control1.myCpG.txt", package = "methylKit"),
                system.file("extdata", 
                            "control2.myCpG.txt", package = "methylKit") )


# read the files to a methylRawList object: myobj
myobj=methRead(file.list,
               sample.id=list("test1","test2","ctrl1","ctrl2"),
               assembly="hg18",
               treatment=c(1,1,0,0),
               context="CpG"
)

meth=unite(myobj, destrand=FALSE)
meth.C <- getData(meth)[,seq(6,ncol(meth),3)]
meth.T <- getData(meth)[,seq(7,ncol(meth),3)]
mr <- meth.C/(meth.C + meth.T)
chr_pos = getData(meth)[,1:2]
methyrate = data.frame(chr_pos,mr)
names(methyrate) = c('chr', 'pos', rep('g1',2), rep('g2',2))
region<-tileMethylCounts(myobj)
meth<-unite(region,destrand=F)
myDiff<-calculateDiffMeth(meth)
#> two groups detected:
#>    will calculate methylation difference as the difference of
#>    treatment (group: 1) - control (group: 0)
met_all<-getMethylDiff(myDiff,type="all")

example_tempfiles = tempfile(c("rate_combine", "methylKit_DMR_raw"))
tempdir()
write.table(methyrate, file=example_tempfiles[1], row.names=F, col.names=T, quote=F, sep='\t')
write.table (met_all, file=example_tempfiles[2], sep ="\t", row.names =F, col.names =T, quote =F)

evalue.methylKit function could be used to tackle the problem.

result = metevalue.methylKit(example_tempfiles[1], example_tempfiles[2], bheader = T)
#> Joining, by = c("start", "end")
str(result)
#> 'data.frame':    24 obs. of  9 variables:
#>  $ chr      : chr  "chr21" "chr21" "chr21" "chr21" ...
#>  $ start    : int  9927001 9944001 9959001 9967001 10011001 10077001 10087001 10186001 13664001 13991001 ...
#>  $ end      : int  9928000 9945000 9960000 9968000 10012000 10078000 10088000 10187000 13665000 13992000 ...
#>  $ strand   : chr  "*" "*" "*" "*" ...
#>  $ p        : num  2.47e-10 2.57e-21 4.39e-23 3.08e-04 2.02e-65 ...
#>  $ qvalue   : num  3.24e-10 9.58e-21 2.36e-22 2.37e-04 3.27e-64 ...
#>  $ meth.diff: num  -34.1 -40.2 -25.4 -25.9 25.8 ...
#>  $ e_value  : num  1.65 1.65 1.65 1.65 1.65 ...
#>  $ e_adjust : num  1.65 1.65 1.65 1.65 1.65 ...

Alternatively, one could use the build-in functions to derive functions which avoid the file operation:

result = evalue_buildin_var_fmt_nm(methyrate, met_all, method="methylKit")
result = list(a = result$a, 
              b = result$b, 
              a_b = evalue_buildin_sql(result$a, result$b, method="methylKit"))
result = varevalue.metilene(result$a, result$b, result$a_b)
#> Joining, by = c("start", "end")
str(result)
#> 'data.frame':    24 obs. of  9 variables:
#>  $ chr      : Factor w/ 1 level "chr21": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ start    : int  9927001 9944001 9959001 9967001 10011001 10077001 10087001 10186001 13664001 13991001 ...
#>  $ end      : int  9928000 9945000 9960000 9968000 10012000 10078000 10088000 10187000 13665000 13992000 ...
#>  $ strand   : Factor w/ 3 levels "+","-","*": 3 3 3 3 3 3 3 3 3 3 ...
#>  $ p        : num  2.47e-10 2.57e-21 4.39e-23 3.08e-04 2.02e-65 ...
#>  $ qvalue   : num  3.24e-10 9.58e-21 2.36e-22 2.37e-04 3.27e-64 ...
#>  $ meth.diff: num  -34.1 -40.2 -25.4 -25.9 25.8 ...
#>  $ e_value  : num  1.65 1.65 1.65 1.65 1.65 ...
#>  $ e_adjust : num  1.65 1.65 1.65 1.65 1.65 ...

Example: BiSeq

First, we load the methylation data at CpG site levels from ‘BiSeq’ package. Then we cluster CpG sites into DMRs using ‘BiSeq’.

library(BiSeq)
library(dplyr)
data(rrbs)
rrbs.rel <- rawToRel(rrbs)
methyrate <- methLevel(rrbs.rel)
methyrate <- data.frame(methyrate)
methyrateq = cbind(rows = as.numeric(row.names(methyrate)), methyrate)
methypos = data.frame(rows = as.numeric(row.names(methyrate)), rowRanges(rrbs))
methyrate = left_join(methypos, methyrateq)
methyrate = methyrate[,c(2,3,7:16)]
names(methyrate) <- c('chr','pos',rep('g1',5),rep('g2',5))

rrbs.clust.unlim <- clusterSites(object = rrbs,perc.samples = 3/4,min.sites = 20,max.dist = 100)

clusterSitesToGR(rrbs.clust.unlim)
ind.cov <- totalReads(rrbs.clust.unlim) > 0

quant <- quantile(totalReads(rrbs.clust.unlim)[ind.cov])
rrbs.clust.lim <- limitCov(rrbs.clust.unlim, maxCov = quant)
predictedMeth <- predictMeth(object = rrbs.clust.lim)

test<- predictedMeth[, colData(predictedMeth)$group == "test"]
control <- predictedMeth[, colData(predictedMeth)$group == "control"]
mean.test <- rowMeans(methLevel(test))
mean.control <- rowMeans(methLevel(control))

betaResults <- betaRegression(formula = ~group,link = "probit",object = predictedMeth,type = "BR")
vario <- makeVariogram(betaResults)
vario.sm <- smoothVariogram(vario, sill = 0.9)

locCor <- estLocCor(vario.sm)
clusters.rej <- testClusters(locCor)
clusters.trimmed <- trimClusters(clusters.rej)
DMRs <- findDMRs(clusters.trimmed,max.dist = 100,diff.dir = TRUE)


example_tempfiles = tempfile(c('rate_combine', 'BiSeq_DMR'))
write.table(methyrate, example_tempfiles[1], row.names=F, col.names=T, quote=F, sep='\t')
write.table(DMRs, example_tempfiles[2], quote=F, row.names = F,col.names = F, sep = '\t')

Finally, we add E-values and adjusted E-values as additional columns to the output file of ‘BiSeq’.metevalue.biseq function could be used to tackle the problem.

result = metevalue.biseq(example_tempfiles[1],example_tempfiles[2])
str(result)

Example: DMRfinder

Given the input file

rate_combine_DMRfinder: a file containing methylation rates at each CpG site
DMRfinder_DMR: the output file from ‘DMRfinder’

rate_combine <- read.table("rate_combine_DMRfinder", header = T)
head(rate_combine)

DMRs <- read.table("DMRfinder_DMR", header = T)
head(DMRs)

Adding E-values and adjusted E-values as additional columns to file ‘DMRfinder_DMR’

result <- metevalue.DMRfinder('rate_combine_DMRfinder', 'DMRfinder_DMR', bheader=T)
head(result)

Alternatively, function varevalue.metilene can also provide e-value and adjusted e-value.

result = evalue_buildin_var_fmt_nm(rate_combine, DMRs, method="DMRfinder")
result = list(a = result$a, 
              b = result$b, 
              a_b = evalue_buildin_sql(result$a, result$b, method="DMRfinder"))
result = varevalue.metilene(result$a, result$b, result$a_b)
head(result)

Example: Metilene

Given

metilene.input: the input file of Metilene containing methylation rates at each CpG site
metilene.out: the output file of Metilene

input <- read.table("metilene.input", header = T)
head(input)

out <- read.table("metilene.out", header = F)
head(out)

Adding E-values and adjusted E-values as additional columns to metilene.out

result <- metevalue.metilene('metilene.input', 'metilene.out')
head(result)

Alternatively, function varevalue.metilene can also provide e-value and adjusted e-value.

result = evalue_buildin_var_fmt_nm(input, out, method="metilene")
result = list(a = result$a, 
              b = result$b, 
              a_b = evalue_buildin_sql(result$a, result$b, method="metilene"))
result = varevalue.metilene(result$a, result$b, result$a_b)
head(result)

Example: Other DNA methylation tools

In above examples, we have already provided examples to calculate E-values directly from DMR detection tools including BiSeq, DMRfinder, MethylKit and Metilene. All of these require users to prepare an output file of different tools. However, users may wonder how to calculate the E-values directly from CpG sites or other DNA methylation tools not presented above. We then facilitate the purpose in the following example.

methyrate: a file containing methylation rates at each CpG site of 2 different groups

By changing the group name, start site and end site, function varevalue.single_general can calculate e-value of any site or region using a general methylation rates data without using an output file of a specific tool.

input <- read.table("methyrate", header = T)
e_value <- varevalue.single_general(methyrate=input, group1_name='g1', group2_name='g2', chr='chr21', start=9439679, end=9439679)
head(e_value)

Example: RNA-seq data

The framework of E-value calculation presented in this project is also able to be extended to other genomic data including RNA-seq. Here is an example to introduce the E-value calculation in RNA-seq.

desq_out: the RNA data

function metevalue.RNA_general can provide e-values for each row of the normalized expression level of RNA-seq data.

input <- read.table("desq_out", header = T)
data_e <- metevalue.RNA_general(input, group1_name='treated', group2_name='untreated')
head(data_e)

Misc

Demo data

Demo data for different metevalue.[DMR] functions are listed in the section.

Input Data Examples: MethylKit

methyrate Example

chr	pos	g1	g1	g2	g2
chr21	9853296	0.5882353	0.8048048	0.8888889	0.8632911
chr21	9853326	0.7058824	0.7591463	0.8750000	0.7493404

methylKit.output Example

chr	start	end	strand	pvalue	qvalue	meth.diff
chr21	9927001	9928000	*	0	0	-34.07557
chr21	9944001	9945000	*	0	0	-40.19089

Input Data Examples: BiSeq

methyrate Example

chr	pos	g1	g1	g1	g1	g1	g2	g2	g2	g2	g2
chr1	870425	0.8205128	1	0.7	NaN	NaN	0.3125	0.7419355	0.2461538	0.1794872	0.2413793
chr1	870443	0.8461538	1	0.7	NaN	NaN	0.3750	0.3225806	0.2923077	0.0512821	0.2413793

biseq.output Example

seqnames	start	end	width	strand	median.p	median.meth.group1	median.meth.group2	median.meth.diff
chr1	872369	872616	248	*	0.0753559	0.9385462	0.8666990	0.0710524
chr1	875227	875470	244	*	0.0000026	0.5136315	0.1991452	0.2942668

Input Data Examples: DMRfinder

methyrate Example

chr	pos	g1	g1.1	g2	g2.1
chr1	202833315	0	0.0000000	0	0
chr1	202833323	1	0.8095238	1	1

DMRfinder.output Example

chr	start	end	CpG	Control.mu	Exptl.mu	Control..Exptl.diff	Control..Exptl.pval
chr8	25164078	25164102	3	0.9241646	0.7803819	-0.1437827	0.0333849
chr21	9437432	9437538	14	0.7216685	0.1215506	-0.6001179	0.0000000

Input Data Examples: DMRfinder

methyrate Example

chr	pos	g1	g1.1	g1.2	g1.3	g1.4	g1.5	g1.6	g1.7	g2	g2.1	g2.2	g2.3	g2.4	g2.5	g2.6	g2.7
chr21	9437433	0.9285714	NA	0.7222222	0.75	1	0.6666667	1	0.8695652	0.0000000	0	0	0	0.0000000	0.0	NA	0.00
chr21	9437445	1.0000000	NA	0.9444444	0.75	1	0.6666667	0	0.8695652	0.6111111	0	0	0	0.7333333	0.6	NA	0.75

metilene.output Example

chr	start	end	q-value	methyl.diff	CpGs	p	p2	m1	m2
chr21	9437432	9437540	0	0.610989	26	0	0	0.73705	0.12606
chr21	9708982	9709189	0	0.475630	28	0	0	0.58862	0.11299

Input Data Examples: Metilene

metilene.input Example

chr	pos	g1	g1.1	g1.2	g1.3	g1.4	g1.5	g1.6	g1.7	g2	g2.1	g2.2	g2.3	g2.4	g2.5	g2.6	g2.7
chr21	9437433	0.9285714	NA	0.7222222	0.75	1	0.6666667	1	0.8695652	0.0000000	0	0	0	0.0000000	0.0	NA	0.00
chr21	9437445	1.0000000	NA	0.9444444	0.75	1	0.6666667	0	0.8695652	0.6111111	0	0	0	0.7333333	0.6	NA	0.75

metilene.output Example

chr	start	end	q-value	methyl.diff	CpGs	p	p2	m1	m2
chr21	9437432	9437540	0	0.610989	26	0	0	0.73705	0.12606
chr21	9708982	9709189	0	0.475630	28	0	0	0.58862	0.11299

Input Data Examples: Other DNA methylation tools

methyrate Example

chr	pos	g1	g1.1	g1.2	g1.3	g1.4	g1.5	g1.6	g1.7	g2	g2.1	g2.2	g2.3	g2.4	g2.5	g2.6	g2.7
chr21	9437433	0.9285714	NA	0.7222222	0.75	1	0.6666667	1	0.8695652	0.0000000	0	0	0	0.0000000	0.0	NA	0.00
chr21	9437445	1.0000000	NA	0.9444444	0.75	1	0.6666667	0	0.8695652	0.6111111	0	0	0	0.7333333	0.6	NA	0.75

Input Data Examples: RNA-seq data

desq_out Example

treated1fb	treated2fb	treated3fb	untreated1fb	untreated2fb	untreated3fb	untreated4fb
4.449648	4.750104	4.431634	4.392285	4.497514	4.762213	4.533928
6.090031	5.973211	5.913239	6.238684	6.050743	5.932738	6.022005

Other Demos

Please vist the metevalue-emo project for more demos.

E-value in DNA methylation studies

Introductions

Other Demos

Call by files

Call by R data frames

Example: MethylKit

Example: BiSeq

Example: DMRfinder

Example: Metilene

Example: Other DNA methylation tools

Example: RNA-seq data

Misc

Demo data

Input Data Examples: MethylKit

Input Data Examples: BiSeq

Input Data Examples: DMRfinder

Input Data Examples: DMRfinder

Input Data Examples: Metilene

Input Data Examples: Other DNA methylation tools

Input Data Examples: RNA-seq data

Other Demos