Package 'DMRScan' reference manual

Title:	Detection of Differentially Methylated Regions
Description:	This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG.
Authors:	Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut]
Maintainer:	Christian M Page <[email protected]>
License:	GPL-3
Version:	1.29.0
Built:	2024-11-29 05:10:26 UTC
Source:	https://github.com/bioc/DMRScan

DMR Scan function

Description

DMR Scan function

Usage

dmrscan(observations, windowSize, windowThreshold = NULL, chr = NULL,
  pos = NULL, maxGap = 500, ...)
dmrscan(observations, windowSize, windowThreshold = NULL, chr = NULL,
  pos = NULL, maxGap = 500, ...)

Arguments

`observations`	An object of either; `GRangesList` made by `makeCpGregions`, a vector of the test statistic, a `GRanges` object, or a "minfi" object (soon to be supported).
`windowSize`	A sequence of windowSizes for the slidingWindow. Must be an integer vector, with equal length as the number of windows.
`windowThreshold`	Optional argument with corresponding cut-off for each window. Will be estimated if not supplied.
`chr`	A vector of chromosomal position. Only used when the observations vector is a matrix of test statistic.
`pos`	A vector of genomic coordinates for the CpGs to match the chr argument
`maxGap`	The maximum allowed gap between two CpGs within the same region.
`...`	Optional arguments to be passed to `estimateThreshold`, if no grid is specified.

Value

An object of type GRanges with significantly differentially

Examples

## methylation data from chromosome 22 
data(DMRScan.methylationData)
## phenotype (end-point for methylation data)

data(DMRScan.phenotypes) 

## Test for an association between phenotype and methylation
test.statistics <- apply(DMRScan.methylationData,1,function(x,y)
  summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],
                                                   y = DMRScan.phenotypes)
## Set chromosomal position to each test-statistic
positions <- data.frame(matrix(as.integer(unlist(strsplit(names(test.statistics), 
                               split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1]
## Set clustering features 
min.cpg <- 4  ## Minimum number of CpGs in a tested cluster
## Maximum distance (in base-pairs) within a cluster 
## before it is broken up into two separate cluster 
max.gap <- 750  

## Identify all clusters, and generate a list for each cluster
regions <- makeCpGregions(observations = test.statistics, 
                          chr = positions[,1], pos = positions[,2], 
                          maxGap = max.gap, minCpG = min.cpg)
## Number of CpGs in the slidingWindows, can be either a single number 
## or a sequence of windowSizes
windowSizes <- 3:7 
nCpG        <- sum(sapply(regions,length)) ## Number of CpGs to be tested

# Estimate the windowThreshold, based on the number of CpGs and windowSizes
windowThresholds <- estimateWindowThreshold(nProbe = nCpG, 
               windowSize = windowSizes, method = "sampling", mcmc = 10000)
## Run the slidingWindow
DMRScanResults   <- dmrscan(observations = regions, 
                            windowSize = windowSizes, 
                            windowThreshold = windowThresholds)
## Print the result
print(DMRScanResults)

## methylation data from chromosome 22 
data(DMRScan.methylationData)
## phenotype (end-point for methylation data)

data(DMRScan.phenotypes) 

## Test for an association between phenotype and methylation
test.statistics <- apply(DMRScan.methylationData,1,function(x,y)
  summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],
                                                   y = DMRScan.phenotypes)
## Set chromosomal position to each test-statistic
positions <- data.frame(matrix(as.integer(unlist(strsplit(names(test.statistics), 
                               split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1]
## Set clustering features 
min.cpg <- 4  ## Minimum number of CpGs in a tested cluster
## Maximum distance (in base-pairs) within a cluster 
## before it is broken up into two separate cluster 
max.gap <- 750  

## Identify all clusters, and generate a list for each cluster
regions <- makeCpGregions(observations = test.statistics, 
                          chr = positions[,1], pos = positions[,2], 
                          maxGap = max.gap, minCpG = min.cpg)
## Number of CpGs in the slidingWindows, can be either a single number 
## or a sequence of windowSizes
windowSizes <- 3:7 
nCpG        <- sum(sapply(regions,length)) ## Number of CpGs to be tested

# Estimate the windowThreshold, based on the number of CpGs and windowSizes
windowThresholds <- estimateWindowThreshold(nProbe = nCpG, 
               windowSize = windowSizes, method = "sampling", mcmc = 10000)
## Run the slidingWindow
DMRScanResults   <- dmrscan(observations = regions, 
                            windowSize = windowSizes, 
                            windowThreshold = windowThresholds)
## Print the result
print(DMRScanResults)

DMRScan: An R-package for identification of Differentially Metylated Regions

Description

DMRScan: An R-package for identification of Differentially Metylated Regions

Arguments

`observations`	An object of type GRangesList from makeCpGregions
`windowSize`	A sequence of windowSizes for the slidingWindow, must be an integer
`windowThreshold`	Optional argument with corresponding cut-off for each window. Will be estimated if not supplied.
`...`	Optional arguments to be pased to `estimateThreshold`, if no grid is specified.

Value

An object of type GRanges with signficantly differentially

Author(s)

Christian Page, [email protected]

References

Not Published yet (Under revision)

Examples

                                                             
## nProbeoad methylation data from chromosome 22                             
data(DMRScan.methylationData)                                                
## nProbeoad phenotype (end-point for methylation data)                      
data(DMRScan.phenotypes)                                                     
                                                                             
## Test for an association between phenotype and Methylation                 
test.statistics <- apply(DMRScan.methylationData,1,function(x,y)             
  summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],  
                                                   y = DMRScan.phenotypes)   
## Set chromosomal position to each test-statistic                           
positions <- data.frame(matrix(as.integer(unlist(strsplit(names(test.statistics), split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1]
## Set clustering features                                                   
min.cpg <- 4  ## Minimum number of CpGs in a tested cluster                  
## Maxium distance (in base-pairs) within a cluster                          
## before it is broken up into two seperate cluster                          
max.gap <- 750                                                               
                                                                             
## Identify all clusters, and generate a list for each cluster               
regions <- makeCpGregions(observations = test.statistics,                    
                          chr = positions[,1], pos = positions[,2],          
                          maxGap = max.gap, minCpG = min.cpg)                
## Number of CpGs in the slidingWindows, can be either a single number       
## or a sequence of windowSizes                                              
windowSizes <- 3:7                                                           
nCpG        <- sum(sapply(regions, length)) ## Number of CpGs to be tested                  
                                                                             
# Estimate the windowThreshold, based on the number of CpGs and windowSizes  
windowThresholds <- estimateWindowThreshold(nProbe = nCpG,                   
               windowSize = windowSizes, method = "sampling", mcmc = 10000)  
## Run the slidingWindow                                                     
DMRScanResults   <- dmrscan(observations = regions,                          
                            windowSize = windowSizes,                        
                            windowThreshold = windowThresholds)              
## Print the result                                                          
print(DMRScanResults)                                                        
## nProbeoad methylation data from chromosome 22                             
data(DMRScan.methylationData)                                                
## nProbeoad phenotype (end-point for methylation data)                      
data(DMRScan.phenotypes)                                                     
                                                                             
## Test for an association between phenotype and Methylation                 
test.statistics <- apply(DMRScan.methylationData,1,function(x,y)             
  summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],  
                                                   y = DMRScan.phenotypes)   
## Set chromosomal position to each test-statistic                           
positions <- data.frame(matrix(as.integer(unlist(strsplit(names(test.statistics), split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1]
## Set clustering features                                                   
min.cpg <- 4  ## Minimum number of CpGs in a tested cluster                  
## Maxium distance (in base-pairs) within a cluster                          
## before it is broken up into two seperate cluster                          
max.gap <- 750                                                               
                                                                             
## Identify all clusters, and generate a list for each cluster               
regions <- makeCpGregions(observations = test.statistics,                    
                          chr = positions[,1], pos = positions[,2],          
                          maxGap = max.gap, minCpG = min.cpg)                
## Number of CpGs in the slidingWindows, can be either a single number       
## or a sequence of windowSizes                                              
windowSizes <- 3:7                                                           
nCpG        <- sum(sapply(regions, length)) ## Number of CpGs to be tested                  
                                                                             
# Estimate the windowThreshold, based on the number of CpGs and windowSizes  
windowThresholds <- estimateWindowThreshold(nProbe = nCpG,                   
               windowSize = windowSizes, method = "sampling", mcmc = 10000)  
## Run the slidingWindow                                                     
DMRScanResults   <- dmrscan(observations = regions,                          
                            windowSize = windowSizes,                        
                            windowThreshold = windowThresholds)              
## Print the result                                                          
print(DMRScanResults)

Bi-sulfite sequencing data from all known CpG islands at chromosome 22 from 100 the Finish teens study, sampled from extreme BMI quantiles. The data set is reduced to 25139 sites on chromosome 22. See "Genome-wide DNA methylation in saliva and body size of adolescent girls", TB Rounge, CM Page, M Lepisto, E Pekka, and BK Andreassen and E Weiderpass, _Epigenomics_ 8.11 (2016): 1495-1505 for a full overview of the data set.

Examples

data(DMRScan.methylationData)
head(DMRScan.methylationData)
data(DMRScan.methylationData)
head(DMRScan.methylationData)

DMRScan

Description

Accompanying phenotypes for the methylation data, indicating case- control status for the BMI quantiles. See "Genome-wide DNA methylation in saliva and body size of adolescent girls", TB Rounge, CM Page, M Lepisto, E Pekka, and BK Andreassen and E Weiderpass, _Epigenomics_ 8.11 (2016): 1495-1505 for a full description of the phenotypes.

Examples

data(DMRScan.phenotypes)
table(DMRScan.phenotypes)
data(DMRScan.phenotypes)
table(DMRScan.phenotypes)

EstimateWindowThresholds

Description

Estimate window thresholds for sliding window, one unique value for each window size

Usage

estimateWindowThreshold(nProbe, windowSize, method = "siegmund",
  mcmc = 1000, nCPU = 1, submethod = "ar", ...)
estimateWindowThreshold(nProbe, windowSize, method = "siegmund",
  mcmc = 1000, nCPU = 1, submethod = "ar", ...)

Arguments

`nProbe`	The number of probes (CpGs) in the study.
`windowSize`	The different window sizes to be tested. Must be either one, or an ordered sequence of integers.
`method`	Gives the method by which the threshold is calculated. Can be either an analytical solution "siegmund", provided by Siegnumd et.al (2012), or an iterative process; either importance sampling "sampling", as suggested by Zhang (2012) or a full MCMC model "mcmc" which can account for any dependency structure, wich is pass to arima.sim, with ...
`mcmc`	The number of MCMC iterations to be used, when using either Important Sampling ("zhang") or MCMC estimation of the threshold.
`nCPU`	When calculating the thresholds on a cluster, how many CPUs should be used. This option is only compatible with the 'mcmc' method.
`submethod`	A character string indicating if an AR(5) or ARIMA model should be used. In the AR(5), the index runs from -2 to 2. A regular AR(p) model can be obtaine using ARIMA(p,0,0) instead.
`...`	Optinal parameters pased on to `arima`, when simulating data using the mcmc option, see arima.sim()

Value

Returns a vector of the threshold for each window size

Examples

thresholdGrid <- estimateWindowThreshold(nProbe = 1000, 
                                    windowSize = 3:8, method = "siegmund")
thresholdGrid <- estimateWindowThreshold(nProbe = 1000, 
                                    windowSize = 3:8, method = "siegmund")

Cluster

Description

Cluster CpGs together in genes based on annotation

Usage

makeCpGgenes(observations, chr, pos, gene, minCpG = 2)
makeCpGgenes(observations, chr, pos, gene, minCpG = 2)

Arguments

`observations`	Vector of corresponding observed T-value for each CpG, must be ordered in the same way as chr and pos
`chr`	Vector of chromosome location for each CpG
`pos`	Vector giving base pair position for each CpG If unsorted, use order(chr,pos) to sort the genomic positions within each chromosome.
`gene`	A vector asigning each probe to a gene.
`minCpG`	Minimum number of CpGs allowed in each region to be considered. Default is set to at least 2 CpGs within each region.

Value

The suplied observations ordered into into a list, with one entry for each CpG region.

Examples

data(DMRScan.methylationData) ## Load methylation data from chromosome 22
data(DMRScan.phenotypes) ## Load phenotype (end-point for methylation data)

## Test for an association between phenotype and Methylation
testStatistics <- apply(DMRScan.methylationData,1,function(x,y)
 summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],
  y = DMRScan.phenotypes)

## Set chromosomal position to each test-statistic
pos <- data.frame(matrix(as.integer(unlist(strsplit(names(testStatistics),
 split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1]

## Set clustering features 
minCpG   <- 3  ## Minimum number of CpGs in a tested cluster
gene     <- sample(paste("Gene",1:100,sep=""), 
                           length(testStatistics),replace=TRUE)
regions  <- makeCpGgenes(observations = testStatistics, 
                         chr = pos[,1], pos = pos[,2], 
                         gene = gene, minCpG = minCpG)

data(DMRScan.methylationData) ## Load methylation data from chromosome 22
data(DMRScan.phenotypes) ## Load phenotype (end-point for methylation data)

## Test for an association between phenotype and Methylation
testStatistics <- apply(DMRScan.methylationData,1,function(x,y)
 summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],
  y = DMRScan.phenotypes)

## Set chromosomal position to each test-statistic
pos <- data.frame(matrix(as.integer(unlist(strsplit(names(testStatistics),
 split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1]

## Set clustering features 
minCpG   <- 3  ## Minimum number of CpGs in a tested cluster
gene     <- sample(paste("Gene",1:100,sep=""), 
                           length(testStatistics),replace=TRUE)
regions  <- makeCpGgenes(observations = testStatistics, 
                         chr = pos[,1], pos = pos[,2], 
                         gene = gene, minCpG = minCpG)

Cluster

Description

Cluster CpGs together in regions based on proximity

Usage

makeCpGregions(observations, chr, pos, maxGap = 500, minCpG = 2)
makeCpGregions(observations, chr, pos, maxGap = 500, minCpG = 2)

Arguments

`observations`	Vector of corresponding observed T-value for each CpG, must be ordered in the same way as chr and pos
`chr`	Vector of chromosome location for each CpG
`pos`	Vector giving base pair position for each CpG If unsorted, use order(chr,pos) to sort the genomic positions within each chromosome.
`maxGap`	Maximum allowed base pair gap within a cluster. Default is set to 500.
`minCpG`	Minimum number of CpGs allowed in each region to be considered. Default is set to at least 2 CpGs within each region.

Value

The suplied observations ordered into into a GRangesList object. To be parsed further into dmrscan

Examples

data(DMRScan.methylationData) ## Load methylation data from chromosome 22
data(DMRScan.phenotypes) ## Load phenotype (end-point for methylation data)

## Test for an association between phenotype and Methylation
testStatistics <- apply(DMRScan.methylationData,1,function(x,y)
 summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],
 y = DMRScan.phenotypes)

## Set chromosomal position to each test-statistic
pos<- data.frame(matrix(as.integer(unlist(strsplit(names(testStatistics),
split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1] 

## Set clustering features 
minCpG <- 3  ## Minimum number of CpGs in a tested cluster 
## Maxium distance (in base-pairs) within a cluster before it is 
## broken up into two seperate cluster
maxGap <- 750  
regions <- makeCpGregions(observations = testStatistics, chr = pos[,1], 
                            pos = pos[,2], maxGap = maxGap, minCpG = minCpG)
data(DMRScan.methylationData) ## Load methylation data from chromosome 22
data(DMRScan.phenotypes) ## Load phenotype (end-point for methylation data)

## Test for an association between phenotype and Methylation
testStatistics <- apply(DMRScan.methylationData,1,function(x,y)
 summary(glm(y ~ x, family = binomial(link = "logit")))$coefficients[2,3],
 y = DMRScan.phenotypes)

## Set chromosomal position to each test-statistic
pos<- data.frame(matrix(as.integer(unlist(strsplit(names(testStatistics),
split="chr|[.]"))), ncol = 3, byrow = TRUE))[,-1] 

## Set clustering features 
minCpG <- 3  ## Minimum number of CpGs in a tested cluster 
## Maxium distance (in base-pairs) within a cluster before it is 
## broken up into two seperate cluster
maxGap <- 750  
regions <- makeCpGregions(observations = testStatistics, chr = pos[,1], 
                            pos = pos[,2], maxGap = maxGap, minCpG = minCpG)

Method Fixed window size scan for a sequence of window sizes

Description

Method Fixed window size scan for a sequence of window sizes

Usage

manyWindowSizeScanner(region, windowThreshold, windowSize)

## S4 method for signature 'GRangesList'
manyWindowSizeScanner(region, windowThreshold,
  windowSize)

## S4 method for signature 'GRanges'
manyWindowSizeScanner(region, windowThreshold,
  windowSize)
manyWindowSizeScanner(region, windowThreshold, windowSize)

## S4 method for signature 'GRangesList'
manyWindowSizeScanner(region, windowThreshold,
  windowSize)

## S4 method for signature 'GRanges'
manyWindowSizeScanner(region, windowThreshold,
  windowSize)

Arguments

`region`	Object of type GRanges
`windowThreshold`	Vector of window thresholds
`windowSize`	Vector of window sizes to be tested on regions

Value

A list of the windows that are significant

Examples

## Not run

## Not run

Method Fixed window size scan for one window size

Description

Method Fixed window size scan for one window size

Usage

oneWindowSizeScanner(region, windowThreshold, windowSize)

## S4 method for signature 'GRangesList'
oneWindowSizeScanner(region, windowThreshold,
  windowSize)

## S4 method for signature 'GRanges'
oneWindowSizeScanner(region, windowThreshold,
  windowSize)
oneWindowSizeScanner(region, windowThreshold, windowSize)

## S4 method for signature 'GRangesList'
oneWindowSizeScanner(region, windowThreshold,
  windowSize)

## S4 method for signature 'GRanges'
oneWindowSizeScanner(region, windowThreshold,
  windowSize)

Arguments

`region`	Object of type GRanges
`windowThreshold`	Vector of window thresholds
`windowSize`	Vector of window sizes to be tested on regions

Value

A list of the windows that are significant

Examples

## Not run

## Not run

Package 'DMRScan'

Help Index

DMR Scan function

Description

Usage

Arguments

Value

Examples

DMRScan: An R-package for identification of Differentially Metylated Regions

Description

Arguments

Value

Author(s)

References

Examples

DMRScan

Description

Examples

DMRScan

Description

Examples

EstimateWindowThresholds

Description

Usage

Arguments

Value

Examples

Cluster

Description

Usage

Arguments

Value

Examples

Cluster

Description

Usage

Arguments

Value

Examples

Method Fixed window size scan for a sequence of window sizes

Description

Usage

Arguments

Value

Examples

Method Fixed window size scan for one window size

Description

Usage

Arguments

Value

Examples