Find the differential methylation elements of lincRNA, protein coding gene ,processed transcript and pseudogene

Description

Find the differential methylation elements or the elements that related with the phenotype. The elements are belong to lincRNA, protein coding gene, processed transcript and pseudogene.

Usage

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dme(data,classes=c("lincRNA","gene","processed_transcript","pseudogene"),contin=c(
"ON","OFF"),testmethod = c("wilcox","limma", "t.test", "satterthwaite"), Padj = c(
"holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"), gcase = 
"case", gcontrol = "control", paired = FALSE,rawpcut = 0.05, adjustpcut = 0.05,
 betadiffcut = 0.14,num)

Arguments

data

The objects of class LincMethy450 which return from loaddata. The beta matrixs of sites. A site per row and A sample per column.

classes

Whose element will be calculated.

contin

If phenotype is continuous,contin is 'ON',use linear regression to find the elements that related with the phenotype.

testmethod

The method to do the test to find dme while contin is 'OFF' which means phenotype is discontinuous.

Padj

The method of multiple testing adjustment to adjust P value.

gcase

The name of case group while contin is 'OFF'.

gcontrol

The name of case group while contin is 'OFF'.

paired

Whether compare in pairs while do t.test.

rawpcut

It is the threshold for cutting raw P value.

adjustpcut

It is the threshold for cutting adjust P value.

betadiffcut

The minimum differential between two groups' means while contin is 'OFF'.

num

The number which is the parameter of of elements to plot.

Details

dme is designed to find differential methylation elements or the transcripts' elements that related with the continuous phenotype. If contin is 'ON', it means the phenotype is continuous, and linear regression will be used. If the phenotype isn't continuous, test such as t test will be used.

Value

dme will return two excel files that one contains the transcripts' elements whose P value less than rawpcut, adjust P less than adjustpcut and the differ of the means of two groups more than betadiffer, while another is the beta matrix of these significant elements. There are box plot for most significative elements and heat map all significative elements.

Author(s)

Hui Zhizhihui013201@gmail.com,Yanxun Suhmu_yanxunsu@163.com,Xin Lilixin920126@163.com

See Also

See Also dms and dmr

Examples

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  Dir <- system.file("extdata/localdata",package="LncDM")
  dir.create(paste(Dir,"/dme",sep=""))
  ###user can set the dir of their own
  setwd(paste(Dir,"/dme",sep=""))
  ###load the result of loaddata()
  load(paste(Dir,"/loadData.Rdata",sep=""))
  ###dme is based on the result of the regionLevel()
  Region <- regionLevel(data=loadData,indexmethod = "mean",classes="lincRNA")
  dme(data=Region,classes="lincRNA",contin="OFF",testmethod = "t.test", Padj = "fdr", 
  gcase = "case", gcontrol = "control", paired = FALSE,rawpcut = 0.05, adjustpcut = 0.05, 
  betadiffcut = 0.3,num=1)