callDML: Function to detect differntially methylated loci (DML) from...

In DSS: Dispersion shrinkage for sequencing 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 <[e-mail]>

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=0.1)
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)

DSS documentation built on Nov. 8, 2020, 7:44 p.m.