callDML {DSS} | R Documentation

## Function to detect differntially methylated loci (DML) from bisulfite sequencing (BS-seq) data.

### Description

This function takes the results from DML testing procedure ('DMLtest' function) and calls DMLs. Regions will CpG sites being statistically significant are deemed as DMLs.

### Usage

    callDML(DMLresult, delta=0.1, p.threshold=1e-5)

### Arguments

`DMLresult` | A data frame representing the results for DML detection. This should be the result returned from 'DMLtest' function.
`delta` | A threshold for defining DML. In DML testing procedure, hypothesis test that the two groups means are equalis is conducted at each CpG site. Here if 'delta' is specified, the function will compute the posterior probability that the difference of the means are greater than delta,and then call DML based on that. This only works when the test results are from 'DMLtest', which is for two-group comparison. For general design, this has to be set to 0.
`p.threshold` | When delta is not specified, this is the threshold of p-values for defining DML, e.g. Loci with p-values less than this threshold will be deemed DMLs. When delta is specified, CpG sites with posterior probability greater than 1-p.threshold are deemed DML.

### Value

A data frame for DMLs. Each row is for a DML. DMLs are sorted by statistical significance. The columns are

`chr` | Chromosome number.
`pos` | Genomic coordinates.
`mu1, mu2` | Mean methylations of two groups.
`diff` | Difference of mean methylations of two groups.
`diff.se` | Standard error of the methylation difference.
`stat` | Wald statistics.
`phi1, phi2` | Estimated dispersions in two groups.
`pval` | P-values. This is obtained from normal distribution.
`fdr` | False discovery rate.
`postprob.overThreshold` | The posterior probability of the difference in methylation greater than delta. This columns is only available when delta>0.

### Author(s)

Hao Wu <hao.wu@emory.edu>

### See Also

DMLtest, callDMR

### Examples

    ## Not run:
    require(bsseq)

    ## first read in methylation data.
    path <- file.path(system.file(package="DSS"), "extdata")
    dat1.1 <- read.table(file.path(path, "cond1_1.txt"), header=TRUE)
    dat1.2 <- read.table(file.path(path, "cond1_2.txt"), header=TRUE)
    dat2.1 <- read.table(file.path(path, "cond2_1.txt"), header=TRUE)
    dat2.2 <- read.table(file.path(path, "cond2_2.txt"), header=TRUE)

    ## make BSseq objects
    BSobj <- makeBSseqData( list(dat1.1, dat1.2, dat2.1, dat2.2),
      c("C1","C2", "N1", "N2") )

    ##  DML test
    dmlTest <- DMLtest(BSobj, group1=c("C1", "C2"), group2=c("N1","N2"))

    ## call DML
    dmls <- callDML(dmlTest)
    head(dmls)

    ## call DML with a threshold
    dmls2 <- callDML(dmlTest, delta=0.2)
    head(dmls2)

    ## For whole-genome BS-seq data, perform DML test with smoothing
    require(bsseqData)
    data(BS.cancer.ex)
    ## takea smallportionof data and test
    BSobj <- BS.cancer.ex[10000:15000,]
    dmlTest <- DMLtest(BSobj, group1=c("C1", "C2", "C3"), group2=c("N1","N2","N3"),
       smoothing=TRUE, smoothing.span=500)
    dmls <- callDML(dmlTest)
    head(dmls)

    ## from multifactor design
    data(RRBS)
    DMLfit = DMLfit.multiFactor(RRBS, design, ~case+cell+case:cell)
    DMLtest.cell = DMLtest.multiFactor(DMLfit, coef="cellrN")
    dml = callDML(DMLtest.cell, p.threshold=0.05) ## this produce a warning
    dml = callDML(DMLtest.cell, thresh=0, p.threshold=0.05) ## no warning

    head(dml)

    ## End(Not run)

[Package DSS version 2.34.0 Index]