Semseeker aims to identify all variants that are enriched and localized in methylation.
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")
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/")
You can find a complete and functional example, which includes data from Gene Expression Omnibus (GEO), by examining the repository’s “example” folder.
- 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.