get.diff.meth: get.diff.meth to identify hypo/hyper-methylated CpG sites on...

In lijingya/ELMER: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes

Description

get.diff.meth applys one-way t-test to identify the CpG sites that are significantly hypo/hyper-methyalated using proportional samples (defined by percentage option) from control and experimental groups. The P values will be adjusted by Benjamini-Hochberg method. Option pvalue and sig.dif will be the criteria (cutoff) for selecting significant differentially methylated CpG sites. If save is TURE, two getMethdiff.XX.csv files will be generated (see detail).

Usage

get.diff.meth(mee, diff.dir = "hypo", cores = NULL, percentage = 0.2, pvalue = 0.01, sig.dif = 0.3, dir.out = "./", save = TRUE)

Arguments

mee	A MEE.data class object contains at least methy and probeInfo slots.
diff.dir	A character can be "hypo" or "hyper", showing dirction DNA methylation changes. If it is "hypo", get.diff.meth function will identify all significantly hypomethylated CpG sites; If "hyper", get.diff.meth function will identify all significantly hypoermethylated CpG sites
cores	A interger which defines the number of cores to be used in parallel process. Default is NULL: no parallel process.
percentage	A number ranges from 0 to 1 specifying the percentage of samples from control and experimental groups that are used to identify the differential methylation. Default is 0.2.
pvalue	A number specifies the significant P value (adjusted P value by BH) cutoff for selecting significant hypo/hyper-methylated probes. Default is 0.01.
sig.dif	A number specifies the smallest DNA methylation difference as a cutoff for selecting significant hypo/hyper-methylated probes. Default is 0.3.
dir.out	A path specifies the directory for outputs. Default is is current directory.
save	A logic. When TRUE, two getMethdiff.XX.csv files will be generated (see detail)

Details

save: When save is TRUE, function will generate two XX.csv files.The first one is named getMethdiff.hypo.probes.csv (or getMethdiff.hyper.probes.csv depends on diff.dir). The first file contains all statistic results for each probe. Based on this file, user can change different P value or sig.dir cutoff to select the significant results without redo the analysis. The second file is named getMethdiff.hypo.probes.significant.csv (or getMethdiff.hyper.probes.significant.csv depends on diff.dir). This file contains statistic results for the probes that pass the significant criteria (P value and sig.dir). When save is FALSE, a data frame R object will be generate which contains the same information with the second file.

Value

A data frame contains statistics from differential analysis for each probes.

Author(s)

Lijing Yao (maintainer: [e-mail])

References

Yao L, Shen H, Laird PW, Farnham PJ,Berman BP: Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes. in revision of Genome Biology

Examples

load(system.file("extdata","mee.example.rda",package = "ELMER"))
Hypo.probe <- get.diff.meth(mee, diff.dir="hypo") # get hypomethylated probes

lijingya/ELMER documentation built on May 21, 2019, 6:14 a.m.