data-raw/example1/README.md
In samarafk/MLML2R: Maximum Likelihood Estimation of DNA Methylation and Hydroxymethylation Proportions
title: "MLML2R: an R package for maximum likelihood estimates of DNA methylation and hydroxymethylation"
author: Samara F. Kiihl, Maria Tellez-Plaza
output:
html_document:
keep_md: yes
toc: true
number_sections: true
Introduction
This document presents an example of the usage of the MLML2R package for R.
Install the R package using the following commands on the R console:
install.packages("devtools")
devtools::install_github("samarafk/MLML2R")
library(MLML2R)
Proposed analyses of single-base profiling of either 5-hmC or 5-mC require combining data obtained using bisulfite conversion, oxidative bisulfite conversion or Tet-Assisted bisulfite conversion methods, but doing so naively produces inconsistent estimates of 5-mC or 5-hmC level (Qu et al., 2013).
The function MLML provides maximum likelihood estimates (MLE) for 5-hmC and 5-mC levels using data from any combination of two of the methods: BS-seq, TAB-seq or oxBS-seq. The function also provides MLE when combining these three methods.
The algorithm implemented in the MLML function is based on the Expectation-Maximization (EM) algorithm proposed by Qu et al. (2013). In addition, when only two methods are combined, our implementation is optimized, since we derived the exact MLE for 5-mC or 5-hmC levels, and the iterative EM algorithm is not needed. Our improved formulation can, thus, decrease analytic processing time and computational burden, common bottlenecks when processing single-base profiling data from thousands of samples.
Furthermore, our routine is flexible and can be used with both next generation sequencing and Infinium Methylation microarray data in the R-statistical language.
Preparing dataset
We will use the dataset from Field et al. (2015), which consists of eight DNA samples from the same DNA source treated with oxBS-BS and hybridized to the Infinium 450K array.
The steps shown in this section follows the vignette from minfi package.
Getting publicly available data
We start with the steps to get the raw data from the GEO repository.
The dataset from Field et al. (2015) is available at GEO accession GSE63179.
The sample was divided into four BS and four oxBS replicates.
Platform used: GPL16304 Illumina HumanMethylation450 BeadChip [UBC enhanced annotation v1.0]
Samples:
- GSM1543269 brain-BS-1
- GSM1543270 brain-oxBS-3
- GSM1543271 brain-BS-2
- GSM1543272 brain-oxBS-4
- GSM1543273 brain-BS-3
- GSM1543274 brain-BS-4
- GSM1543275 brain-oxBS-1
- GSM1543276 brain-oxBS-2
To start this example we will need the following packages:
library(minfi)
library(GEOquery)
which can be installed in R using the commands:
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("minfi", "GEOquery"))
It is usually best practice to start the analysis from the raw data, which in the case of the 450K array is a .IDAT file.
The raw files from \cite{10.1371/journal.pone.0118202} are deposited in GEO and can be downloaded by doing:
getGEOSuppFiles("GSE63179")
untar("GSE63179/GSE63179_RAW.tar", exdir = "GSE63179/idat")
head(list.files("GSE63179/idat", pattern = "idat"))
Decompress the compressed IDAT files:
idatFiles <- list.files("GSE63179/idat", pattern = "idat.gz$", full = TRUE)
sapply(idatFiles, gunzip, overwrite = TRUE)
## named list()
Now we read the IDAT files in the directory:
rgSet <- read.metharray.exp("GSE63179/idat")
rgSet
## class: RGChannelSet
## dim: 622399 8
## metadata(0):
## assays(2): Green Red
## rownames(622399): 10600313 10600322 ... 74810490 74810492
## rowData names(0):
## colnames(8): GSM1543269_9373551079_R01C01
## GSM1543270_9373551079_R01C02 ... GSM1543275_9373551079_R05C01
## GSM1543276_9373551079_R06C01
## colData names(0):
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
pData(rgSet)
## DataFrame with 8 rows and 0 columns
sampleNames(rgSet)
## [1] "GSM1543269_9373551079_R01C01" "GSM1543270_9373551079_R01C02"
## [3] "GSM1543271_9373551079_R02C01" "GSM1543272_9373551079_R02C02"
## [5] "GSM1543273_9373551079_R03C01" "GSM1543274_9373551079_R04C01"
## [7] "GSM1543275_9373551079_R05C01" "GSM1543276_9373551079_R06C01"
The file names consists of a GEO identifier (the GSM part) followed by a standard IDAT naming convention with a 10 digit number which is an array identifier followed by an identifier of the form R01C01. This is because each array actually allows for the hybridization of 12 samples in a 6x2 arrangement. The 9373551079_R01C01 means row 1 and column 1 on chip 9373551079.
We need to identify the samples from different methods: BS-conversion, oxBS-conversion.
geoMat <- getGEO("GSE63179")
pD.all <- pData(geoMat[[1]])
pD <- pD.all[, c("title", "geo_accession", "characteristics_ch1.1", "characteristics_ch1.2","characteristics_ch1.3")]
pD
## title geo_accession characteristics_ch1.1
## GSM1543269 brain-BS-1 GSM1543269 gender: Male
## GSM1543270 brain-oxBS-3 GSM1543270 gender: Male
## GSM1543271 brain-BS-2 GSM1543271 gender: Male
## GSM1543272 brain-oxBS-4 GSM1543272 gender: Male
## GSM1543273 brain-BS-3 GSM1543273 gender: Male
## GSM1543274 brain-BS-4 GSM1543274 gender: Male
## GSM1543275 brain-oxBS-1 GSM1543275 gender: Male
## GSM1543276 brain-oxBS-2 GSM1543276 gender: Male
## characteristics_ch1.2 characteristics_ch1.3
## GSM1543269 age: 25 bisulfite_proc: BS
## GSM1543270 age: 25 bisulfite_proc: oxBS
## GSM1543271 age: 25 bisulfite_proc: BS
## GSM1543272 age: 25 bisulfite_proc: oxBS
## GSM1543273 age: 25 bisulfite_proc: BS
## GSM1543274 age: 25 bisulfite_proc: BS
## GSM1543275 age: 25 bisulfite_proc: oxBS
## GSM1543276 age: 25 bisulfite_proc: oxBS
names(pD)[c(3,4,5)] <- c("gender", "age","method")
pD$gender <- sub("^gender: ", "", pD$gender)
pD$age <- sub("^age: ", "", pD$age)
pD$method <- sub("^bisulfite_proc: ","",pD$method)
We now need to merge this pheno data into the methylation data. The following are commands to make sure we have the same row identifier in both datasets before merging.
sampleNames(rgSet) <- sapply(sampleNames(rgSet),function(x) strsplit(x,"_")[[1]][1])
rownames(pD) <- pD$geo_accession
pD <- pD[sampleNames(rgSet),]
pData(rgSet) <- as(pD,"DataFrame")
rgSet
## class: RGChannelSet
## dim: 622399 8
## metadata(0):
## assays(2): Green Red
## rownames(622399): 10600313 10600322 ... 74810490 74810492
## rowData names(0):
## colnames(8): GSM1543269 GSM1543270 ... GSM1543275 GSM1543276
## colData names(5): title geo_accession gender age method
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
Preprocessing
We refer the reader to the minfi package tutorials for more preprocessing options.
We need to install the required package bellow:
source("https://bioconductor.org/biocLite.R")
biocLite("IlluminaHumanMethylation450kmanifest")
First, we removed probes with detection p-value <0.01 in any of the 8 arrays. The function detectionP identifies failed positions defined as both the methylated and unmethylated channel reporting background signal levels.
detP <- detectionP(rgSet)
failed <- detP >0.01
## Keep probes which failed in at most maxFail arrays (0 = the probe passed in all arrays)
maxFail<- 0
keep_probes <- rowSums(failed) <= maxFail
We kept $83\%$ of the probes according to this criterion.
The rgSet object is a class called RGChannelSet which represents two color data with a green and a red channel. We will use, as input in the MLML funcion, a MethylSet, which contains the methylated and unmethylated signals. The most basic way to construct a MethylSet is to using the function preprocessRaw which uses the array design to match up the different probes and color channels to construct the methylated and unmethylated signals. Here we will use the preprocessNoob function, which does the preprocessing and returns a MethylSet.
Arrays were then normalized using the Noob/ssNoob preprocessing method for Infinium methylation microarrays.
From a MethylSet it is easy to compute Beta values, defined as:
Beta = Meth / (Meth + Unmeth + c)
The c constant is chosen to avoid dividing with small values. Illumina uses a default of c=100. The function getBeta from minfi package can be used to obtain the Beta values.
MSet.noob<- preprocessNoob(rgSet[keep_probes,])
## [preprocessNoob] Applying R/G ratio flip to fix dye bias...
densityPlot(MSet.noob, sampGroups= pData(rgSet)$method,
main= sprintf('Beta values for filtered probes (n= %s)', nrow(MSet.noob)))
Using the MLML2R package
After all the preprocessing procedures, we now can use the MLML2R package to obtain the maximum likelihood estimates for the 5-hmC and 5-mC levels.
Install the R package using the following commands on the R console:
install.packages("devtools")
devtools::install_github("samarafk/MLML2R")
Prepare de input data:
MethylatedBS <- getMeth(MSet.noob)[,c(1,3,5,6)]
UnMethylatedBS <- getUnmeth(MSet.noob)[,c(1,3,5,6)]
MethylatedOxBS <- getMeth(MSet.noob)[,c(7,8,2,4)]
UnMethylatedOxBS <- getUnmeth(MSet.noob)[,c(7,8,2,4)]
Getting the MLE estimates using EM-algorithm:
library(MLML2R)
results_em <- MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS, Mc = MethylatedOxBS,tol=0.0001,iterative=TRUE)
Plot of the results (we have 4 replicates)
Getting the constrained exact MLE estimates:
library(MLML2R)
results_exact <- MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS, Mc = MethylatedOxBS)
Plot of the results (we have 4 replicates)
Other methods to obtain the estimates
Naive estimates
The naive approach to obtain 5-hmC levels is $\beta_{BS} - \beta_{OxBS}$. This approach results in negative values for the 5-hmC levels.
beta_BS <- getBeta(MSet.noob)[,c(1,3,5,6)]
beta_OxBS <- getBeta(MSet.noob)[,c(7,8,2,4)]
hmC_naive <- beta_BS-beta_OxBS
C_naive <- 1-beta_BS
mC_naive <- beta_OxBS
OxyBS estimates
For the specific case where only ox-BS and BS data are available, OxyBS package from Houseman et al. (2016) can be use to obtain estimates.
library(OxyBS)
# Methylated signals from the BS and oxBS arrays
methBS <- MethylatedBS
methOxBS <- MethylatedOxBS
# Unmethylated signals from the BS and oxBS arrays
unmethBS <- UnMethylatedBS
unmethOxBS <- UnMethylatedOxBS
# Calculate Total Signals
signalBS <- methBS+unmethBS
signalOxBS <- methOxBS+unmethOxBS
# Calculate Beta Values
betaBS <- methBS/signalBS
betaOxBS <- methOxBS/signalOxBS
####################################################
# 4. Apply fitOxBS function to preprocessed values
####################################################
# Select the number of CpGs and Subjects to which the method will be applied
nCpGs <- dim(unmethOxBS)[1]
nSpecimens <- dim(unmethOxBS)[2]
# Create container for the OxyBS results
MethOxy <- array(NA,dim=c(nCpGs,nSpecimens,3))
dimnames(MethOxy) <- list(
rownames(methBS)[1:nCpGs],
colnames(methBS)[1:nSpecimens], c("C","5mC","5hmC"))
# Process results (one array at a time, slow)
for(i in 1:nSpecimens){
MethOxy[,i,] <-fitOxBS(betaBS[,i],betaOxBS[,i],signalBS[,i],signalOxBS[,i])
}
Plot of the results (we have 4 replicates)
par(mfrow =c(1,3))
plot(density(MethOxy[,1,3]),main= "5-hmC using OxyBS",xlab="")
lines(density(MethOxy[,2,3]),col=2)
lines(density(MethOxy[,3,3]),col=3)
lines(density(MethOxy[,4,3]),col=4)
plot(density(MethOxy[,1,2]),main= "5-mC using OxyBS",ylim=c(0,5),xlab="")
lines(density(MethOxy[,2,2]),col=2)
lines(density(MethOxy[,3,2]),col=3)
lines(density(MethOxy[,4,2]),col=4)
plot(density(MethOxy[,1,1]),main= "5-C using OxyBS",ylim=c(0,5),xlab="")
lines(density(MethOxy[,2,1]),col=2)
lines(density(MethOxy[,3,1]),col=3)
lines(density(MethOxy[,4,1]),col=4)
oxBS.MLE estimates
ENmix package had the function oxBS.MLE from Xu et al. (2016) that can be uses to obtain estimates for the specific case where ox-BS and BS data are available.
beta_BS <- getBeta(MSet.noob)[,c(1,3,5,6)]
beta_OxBS <- getBeta(MSet.noob)[,c(7,8,2,4)]
N_BS <- getCN(MSet.noob)[,c(1,3,5,6)]
N_OxBS <- getCN(MSet.noob)[,c(7,8,2,4)]
colnames(beta_BS) <- c("rep1","rep2","rep3","rep4")
colnames(beta_OxBS) <- c("rep1","rep2","rep3","rep4")
colnames(N_BS) <- c("rep1","rep2","rep3","rep4")
colnames(N_OxBS) <- c("rep1","rep2","rep3","rep4")
library(ENmix)
oxBSMLEresults <- oxBS.MLE(beta.BS=beta_BS,beta.oxBS=beta_OxBS,
N.BS=N_BS,N.oxBS=N_OxBS)
Comparison of 5-hmC estimates from different methods
Comparison of processing times from different methods
library(OxyBS)
library(microbenchmark)
library(MLML2R)
signalBS <- MethylatedBS+UnMethylatedBS
signalOxBS <- MethylatedOxBS+UnMethylatedOxBS
betaBS <- MethylatedBS/signalBS
betaOxBS <- MethylatedOxBS/signalOxBS
nCpGs <- dim(UnMethylatedOxBS)[1]
nSpecimens <- dim(UnMethylatedOxBS)[2]
MethOxy1 <- array(NA,dim=c(nCpGs,nSpecimens,3))
dimnames(MethOxy1) <- list(
rownames(MethylatedBS)[1:nCpGs],
colnames(MethylatedBS)[1:nSpecimens], c("C","5mC","5hmC"))
oxyBS <- function()
{
for(i in 1:nSpecimens){
MethOxy1[,i,] <-fitOxBS(betaBS[,i],betaOxBS[,i],signalBS[,i],signalOxBS[,i])
}
}
mbm = microbenchmark(
EXACT = MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS,
Mc = MethylatedOxBS),
EM = MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS,
Mc = MethylatedOxBS,tol=0.00001,iterative=TRUE),
oxBSMLE = oxBS.MLE(beta.BS=beta_BS,beta.oxBS=beta_OxBS,
N.BS=N_BS,N.oxBS=N_OxBS),
oxyBS_res = oxyBS(),
times=1)
| Method | Function | Package | Time (Seconds) |
|-----------|:-----------------------:|---------:|-------------------------------------------:|
| Iterative | MLML (tol=0.00001,iterative=TRUE) | MLML2R | 138.879 |
| Iterative | fitOxBS | OxyBS | 2850.424 |
| Closed-form analytical | MLML (iterative=FALSE) | MLML2R | 0.477 |
| Closed-form analytical | oxBS.MLE | ENmix | 0.699 |
samarafk/MLML2R documentation built on Oct. 19, 2019, 6:04 p.m.
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title: "MLML2R: an R package for maximum likelihood estimates of DNA methylation and hydroxymethylation"
author: Samara F. Kiihl, Maria Tellez-Plaza
output:
html_document:
keep_md: yes
toc: true
number_sections: true
Introduction
This document presents an example of the usage of the MLML2R package for R.
Install the R package using the following commands on the R console:
install.packages("devtools")
devtools::install_github("samarafk/MLML2R")
library(MLML2R)
Proposed analyses of single-base profiling of either 5-hmC or 5-mC require combining data obtained using bisulfite conversion, oxidative bisulfite conversion or Tet-Assisted bisulfite conversion methods, but doing so naively produces inconsistent estimates of 5-mC or 5-hmC level (Qu et al., 2013).
The function MLML provides maximum likelihood estimates (MLE) for 5-hmC and 5-mC levels using data from any combination of two of the methods: BS-seq, TAB-seq or oxBS-seq. The function also provides MLE when combining these three methods.
The algorithm implemented in the MLML function is based on the Expectation-Maximization (EM) algorithm proposed by Qu et al. (2013). In addition, when only two methods are combined, our implementation is optimized, since we derived the exact MLE for 5-mC or 5-hmC levels, and the iterative EM algorithm is not needed. Our improved formulation can, thus, decrease analytic processing time and computational burden, common bottlenecks when processing single-base profiling data from thousands of samples.
Furthermore, our routine is flexible and can be used with both next generation sequencing and Infinium Methylation microarray data in the R-statistical language.
Preparing dataset
We will use the dataset from Field et al. (2015), which consists of eight DNA samples from the same DNA source treated with oxBS-BS and hybridized to the Infinium 450K array.
The steps shown in this section follows the vignette from minfi package.
Getting publicly available data
We start with the steps to get the raw data from the GEO repository. The dataset from Field et al. (2015) is available at GEO accession GSE63179.
The sample was divided into four BS and four oxBS replicates.
Platform used: GPL16304 Illumina HumanMethylation450 BeadChip [UBC enhanced annotation v1.0]
Samples:
- GSM1543269 brain-BS-1
- GSM1543270 brain-oxBS-3
- GSM1543271 brain-BS-2
- GSM1543272 brain-oxBS-4
- GSM1543273 brain-BS-3
- GSM1543274 brain-BS-4
- GSM1543275 brain-oxBS-1
- GSM1543276 brain-oxBS-2
To start this example we will need the following packages:
library(minfi)
library(GEOquery)
which can be installed in R using the commands:
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("minfi", "GEOquery"))
It is usually best practice to start the analysis from the raw data, which in the case of the 450K array is a .IDAT file. The raw files from \cite{10.1371/journal.pone.0118202} are deposited in GEO and can be downloaded by doing:
getGEOSuppFiles("GSE63179")
untar("GSE63179/GSE63179_RAW.tar", exdir = "GSE63179/idat")
head(list.files("GSE63179/idat", pattern = "idat"))
Decompress the compressed IDAT files:
idatFiles <- list.files("GSE63179/idat", pattern = "idat.gz$", full = TRUE)
sapply(idatFiles, gunzip, overwrite = TRUE)
## named list()
Now we read the IDAT files in the directory:
rgSet <- read.metharray.exp("GSE63179/idat")
rgSet
## class: RGChannelSet
## dim: 622399 8
## metadata(0):
## assays(2): Green Red
## rownames(622399): 10600313 10600322 ... 74810490 74810492
## rowData names(0):
## colnames(8): GSM1543269_9373551079_R01C01
## GSM1543270_9373551079_R01C02 ... GSM1543275_9373551079_R05C01
## GSM1543276_9373551079_R06C01
## colData names(0):
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
pData(rgSet)
## DataFrame with 8 rows and 0 columns
sampleNames(rgSet)
## [1] "GSM1543269_9373551079_R01C01" "GSM1543270_9373551079_R01C02"
## [3] "GSM1543271_9373551079_R02C01" "GSM1543272_9373551079_R02C02"
## [5] "GSM1543273_9373551079_R03C01" "GSM1543274_9373551079_R04C01"
## [7] "GSM1543275_9373551079_R05C01" "GSM1543276_9373551079_R06C01"
The file names consists of a GEO identifier (the GSM part) followed by a standard IDAT naming convention with a 10 digit number which is an array identifier followed by an identifier of the form R01C01. This is because each array actually allows for the hybridization of 12 samples in a 6x2 arrangement. The 9373551079_R01C01 means row 1 and column 1 on chip 9373551079.
We need to identify the samples from different methods: BS-conversion, oxBS-conversion.
geoMat <- getGEO("GSE63179")
pD.all <- pData(geoMat[[1]])
pD <- pD.all[, c("title", "geo_accession", "characteristics_ch1.1", "characteristics_ch1.2","characteristics_ch1.3")]
pD
## title geo_accession characteristics_ch1.1
## GSM1543269 brain-BS-1 GSM1543269 gender: Male
## GSM1543270 brain-oxBS-3 GSM1543270 gender: Male
## GSM1543271 brain-BS-2 GSM1543271 gender: Male
## GSM1543272 brain-oxBS-4 GSM1543272 gender: Male
## GSM1543273 brain-BS-3 GSM1543273 gender: Male
## GSM1543274 brain-BS-4 GSM1543274 gender: Male
## GSM1543275 brain-oxBS-1 GSM1543275 gender: Male
## GSM1543276 brain-oxBS-2 GSM1543276 gender: Male
## characteristics_ch1.2 characteristics_ch1.3
## GSM1543269 age: 25 bisulfite_proc: BS
## GSM1543270 age: 25 bisulfite_proc: oxBS
## GSM1543271 age: 25 bisulfite_proc: BS
## GSM1543272 age: 25 bisulfite_proc: oxBS
## GSM1543273 age: 25 bisulfite_proc: BS
## GSM1543274 age: 25 bisulfite_proc: BS
## GSM1543275 age: 25 bisulfite_proc: oxBS
## GSM1543276 age: 25 bisulfite_proc: oxBS
names(pD)[c(3,4,5)] <- c("gender", "age","method")
pD$gender <- sub("^gender: ", "", pD$gender)
pD$age <- sub("^age: ", "", pD$age)
pD$method <- sub("^bisulfite_proc: ","",pD$method)
We now need to merge this pheno data into the methylation data. The following are commands to make sure we have the same row identifier in both datasets before merging.
sampleNames(rgSet) <- sapply(sampleNames(rgSet),function(x) strsplit(x,"_")[[1]][1])
rownames(pD) <- pD$geo_accession
pD <- pD[sampleNames(rgSet),]
pData(rgSet) <- as(pD,"DataFrame")
rgSet
## class: RGChannelSet
## dim: 622399 8
## metadata(0):
## assays(2): Green Red
## rownames(622399): 10600313 10600322 ... 74810490 74810492
## rowData names(0):
## colnames(8): GSM1543269 GSM1543270 ... GSM1543275 GSM1543276
## colData names(5): title geo_accession gender age method
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
Preprocessing
We refer the reader to the minfi package tutorials for more preprocessing options.
We need to install the required package bellow:
source("https://bioconductor.org/biocLite.R")
biocLite("IlluminaHumanMethylation450kmanifest")
First, we removed probes with detection p-value <0.01 in any of the 8 arrays. The function detectionP identifies failed positions defined as both the methylated and unmethylated channel reporting background signal levels.
detP <- detectionP(rgSet)
failed <- detP >0.01
## Keep probes which failed in at most maxFail arrays (0 = the probe passed in all arrays)
maxFail<- 0
keep_probes <- rowSums(failed) <= maxFail
We kept $83\%$ of the probes according to this criterion.
The rgSet object is a class called RGChannelSet which represents two color data with a green and a red channel. We will use, as input in the MLML funcion, a MethylSet, which contains the methylated and unmethylated signals. The most basic way to construct a MethylSet is to using the function preprocessRaw which uses the array design to match up the different probes and color channels to construct the methylated and unmethylated signals. Here we will use the preprocessNoob function, which does the preprocessing and returns a MethylSet.
Arrays were then normalized using the Noob/ssNoob preprocessing method for Infinium methylation microarrays.
From a MethylSet it is easy to compute Beta values, defined as:
Beta = Meth / (Meth + Unmeth + c)
The c constant is chosen to avoid dividing with small values. Illumina uses a default of c=100. The function getBeta from minfi package can be used to obtain the Beta values.
MSet.noob<- preprocessNoob(rgSet[keep_probes,])
## [preprocessNoob] Applying R/G ratio flip to fix dye bias...
densityPlot(MSet.noob, sampGroups= pData(rgSet)$method,
main= sprintf('Beta values for filtered probes (n= %s)', nrow(MSet.noob)))
Using the MLML2R package
After all the preprocessing procedures, we now can use the MLML2R package to obtain the maximum likelihood estimates for the 5-hmC and 5-mC levels.
Install the R package using the following commands on the R console:
install.packages("devtools")
devtools::install_github("samarafk/MLML2R")
Prepare de input data:
MethylatedBS <- getMeth(MSet.noob)[,c(1,3,5,6)]
UnMethylatedBS <- getUnmeth(MSet.noob)[,c(1,3,5,6)]
MethylatedOxBS <- getMeth(MSet.noob)[,c(7,8,2,4)]
UnMethylatedOxBS <- getUnmeth(MSet.noob)[,c(7,8,2,4)]
Getting the MLE estimates using EM-algorithm:
library(MLML2R)
results_em <- MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS, Mc = MethylatedOxBS,tol=0.0001,iterative=TRUE)
Plot of the results (we have 4 replicates)
Getting the constrained exact MLE estimates:
library(MLML2R)
results_exact <- MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS, Mc = MethylatedOxBS)
Plot of the results (we have 4 replicates)
Other methods to obtain the estimates
Naive estimates
The naive approach to obtain 5-hmC levels is $\beta_{BS} - \beta_{OxBS}$. This approach results in negative values for the 5-hmC levels.
beta_BS <- getBeta(MSet.noob)[,c(1,3,5,6)]
beta_OxBS <- getBeta(MSet.noob)[,c(7,8,2,4)]
hmC_naive <- beta_BS-beta_OxBS
C_naive <- 1-beta_BS
mC_naive <- beta_OxBS
OxyBS estimates
For the specific case where only ox-BS and BS data are available, OxyBS package from Houseman et al. (2016) can be use to obtain estimates.
library(OxyBS)
# Methylated signals from the BS and oxBS arrays
methBS <- MethylatedBS
methOxBS <- MethylatedOxBS
# Unmethylated signals from the BS and oxBS arrays
unmethBS <- UnMethylatedBS
unmethOxBS <- UnMethylatedOxBS
# Calculate Total Signals
signalBS <- methBS+unmethBS
signalOxBS <- methOxBS+unmethOxBS
# Calculate Beta Values
betaBS <- methBS/signalBS
betaOxBS <- methOxBS/signalOxBS
####################################################
# 4. Apply fitOxBS function to preprocessed values
####################################################
# Select the number of CpGs and Subjects to which the method will be applied
nCpGs <- dim(unmethOxBS)[1]
nSpecimens <- dim(unmethOxBS)[2]
# Create container for the OxyBS results
MethOxy <- array(NA,dim=c(nCpGs,nSpecimens,3))
dimnames(MethOxy) <- list(
rownames(methBS)[1:nCpGs],
colnames(methBS)[1:nSpecimens], c("C","5mC","5hmC"))
# Process results (one array at a time, slow)
for(i in 1:nSpecimens){
MethOxy[,i,] <-fitOxBS(betaBS[,i],betaOxBS[,i],signalBS[,i],signalOxBS[,i])
}
Plot of the results (we have 4 replicates)
par(mfrow =c(1,3))
plot(density(MethOxy[,1,3]),main= "5-hmC using OxyBS",xlab="")
lines(density(MethOxy[,2,3]),col=2)
lines(density(MethOxy[,3,3]),col=3)
lines(density(MethOxy[,4,3]),col=4)
plot(density(MethOxy[,1,2]),main= "5-mC using OxyBS",ylim=c(0,5),xlab="")
lines(density(MethOxy[,2,2]),col=2)
lines(density(MethOxy[,3,2]),col=3)
lines(density(MethOxy[,4,2]),col=4)
plot(density(MethOxy[,1,1]),main= "5-C using OxyBS",ylim=c(0,5),xlab="")
lines(density(MethOxy[,2,1]),col=2)
lines(density(MethOxy[,3,1]),col=3)
lines(density(MethOxy[,4,1]),col=4)
oxBS.MLE estimates
ENmix package had the function oxBS.MLE from Xu et al. (2016) that can be uses to obtain estimates for the specific case where ox-BS and BS data are available.
beta_BS <- getBeta(MSet.noob)[,c(1,3,5,6)]
beta_OxBS <- getBeta(MSet.noob)[,c(7,8,2,4)]
N_BS <- getCN(MSet.noob)[,c(1,3,5,6)]
N_OxBS <- getCN(MSet.noob)[,c(7,8,2,4)]
colnames(beta_BS) <- c("rep1","rep2","rep3","rep4")
colnames(beta_OxBS) <- c("rep1","rep2","rep3","rep4")
colnames(N_BS) <- c("rep1","rep2","rep3","rep4")
colnames(N_OxBS) <- c("rep1","rep2","rep3","rep4")
library(ENmix)
oxBSMLEresults <- oxBS.MLE(beta.BS=beta_BS,beta.oxBS=beta_OxBS,
N.BS=N_BS,N.oxBS=N_OxBS)
Comparison of 5-hmC estimates from different methods
Comparison of processing times from different methods
library(OxyBS)
library(microbenchmark)
library(MLML2R)
signalBS <- MethylatedBS+UnMethylatedBS
signalOxBS <- MethylatedOxBS+UnMethylatedOxBS
betaBS <- MethylatedBS/signalBS
betaOxBS <- MethylatedOxBS/signalOxBS
nCpGs <- dim(UnMethylatedOxBS)[1]
nSpecimens <- dim(UnMethylatedOxBS)[2]
MethOxy1 <- array(NA,dim=c(nCpGs,nSpecimens,3))
dimnames(MethOxy1) <- list(
rownames(MethylatedBS)[1:nCpGs],
colnames(MethylatedBS)[1:nSpecimens], c("C","5mC","5hmC"))
oxyBS <- function()
{
for(i in 1:nSpecimens){
MethOxy1[,i,] <-fitOxBS(betaBS[,i],betaOxBS[,i],signalBS[,i],signalOxBS[,i])
}
}
mbm = microbenchmark(
EXACT = MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS,
Mc = MethylatedOxBS),
EM = MLML(Tc = MethylatedBS , Uc = UnMethylatedBS, Lc = UnMethylatedOxBS,
Mc = MethylatedOxBS,tol=0.00001,iterative=TRUE),
oxBSMLE = oxBS.MLE(beta.BS=beta_BS,beta.oxBS=beta_OxBS,
N.BS=N_BS,N.oxBS=N_OxBS),
oxyBS_res = oxyBS(),
times=1)
| Method | Function | Package | Time (Seconds) |
|-----------|:-----------------------:|---------:|-------------------------------------------:|
| Iterative | MLML (tol=0.00001,iterative=TRUE) | MLML2R | 138.879 |
| Iterative | fitOxBS | OxyBS | 2850.424 |
| Closed-form analytical | MLML (iterative=FALSE) | MLML2R | 0.477 |
| Closed-form analytical | oxBS.MLE | ENmix | 0.699 |
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