semseeker

Semseeker aims to identify all variants that are enriched and localized in methylation.

Installation

To install semseeker, you can use devtools; upcoming releases will be accessible via CRAN.

Install the latest release:

install.packages("devtools")
library("devtools")
install_github("drake69/semseeker")

Quick Example

This basic example demonstrates the process of creating a methylation matrix for beta values that can be utilized in calculations using ChAMP:

library(ChAMP)
idat_folder <- "~/source_idat/"
result_folder = "~/result/"
myLoadN <- champ.load(directory = idat_folder,
                      method = "minfi",
                      methValue="B",
                      autoimpute=TRUE,
                      filterDetP=TRUE,
                      ProbeCutoff=0,
                      SampleCutoff=0.1,
                      detPcut=0.01,
                      filterBeads=TRUE,
                      beadCutoff=0.05,
                      filterNoCG=TRUE,
                      filterSNPs=TRUE,
                      population=NULL,
                      filterMultiHit=TRUE,
                      filterXY=TRUE,
                      force=FALSE,
                      arraytype="450K")

# normalize with ChAMP
myNormN<-champ.norm(beta=myLoadN$beta,
                    rgSet=myLoadN$rgSet,
                    mset=myLoadN$mset,
                    resultsDir= result_folder,
                    method="SWAN",
                    plotBMIQ=FALSE,
                    arraytype="450K",
                    cores= detectCores(all.tests = FALSE, logical = TRUE) - 1
                    )

saveRDS(myNormN,"~/normalizedData.rds")

Here’s how you can obtain the analyzed data:

library(semseeker)

normalizedData <- readRDS("~/normalizedData.rds")

sample_sheet <- read.csv2("~/sample_sheet.csv")

semseeker (sample_sheet = sample_sheet, 
        methylation_data = normalizedData,
        result_folder = "~/semseeker_result/")

Complete Example

You can find a complete and functional example, which includes data from Gene Expression Omnibus (GEO), by examining the repository’s “example” folder.

Input requirements

• The “samplesheet” dataframe must include a column named “Sample_Group” with the following accepted values: “Case”, “Control”, and “Reference”. If you do not have a Reference population, you may duplicate the Control population rows and use “Reference” in the “Sample_Group” column.