R/MethPed_dataset.R

#####################################################################################
## MethPed sample Data set
#####################################################################################
"MethPed_sample"
#' Sample dataset to MethPed package.
#' @description Methylation beta-values generated with the Infinium HumanMethylation450 BeadChips (Illumina).
#' @usage data(MethPed_sample)
#' @name MethPed_sample
#' @docType data
#' @references
#' [1] Anna Danielsson, Szilárd Nemes, Magnus Tisell, Birgitta Lannering, Claes Nordborg, Magnus Sabel, and Helena Carén. "MethPed: A DNA Methylation Classifier Tool for the Identification of Pediatric Brain Tumor Subtypes". Clinical Epigenetics 2015, 7:62, 2015
#' @seealso  See \url{http://www.clinicalepigeneticsjournal.com/content/7/1/62} for more details.
#' @keywords data
#' @format A data frame with 468821 probes and 2 tumor samples
#' @return  DNA Methylation beta-values
#' @examples
#' #################### Loading and view sample data
#' data(MethPed_sample)
#' head(MethPed_sample)
#'
#' #################### Check dimention of sample data
#' dim(MethPed_sample)  # Check number of probes and samples in data
#'
#' #################### Checking missing value in the data
#' missingIndex <- checkNA(MethPed_sample)
#'
MethPed_sample<-data(MethPed_sample,envir = environment())


#####################################################################################
## List of 900 probe used to build predictor
#####################################################################################
"MethPed_900probes"
#' List of 900 probes in the predictor data
#' @description List of 900 probes in the predictor (Training data for Random Forest model)
#' @usage data(MethPed_900probes)
#' @name MethPed_900probes
#' @details For the construction of the training probe pool (predictors), methylation data generated by
#' the Illumina Infinium HumanMethylation 450 BeadChip arrays were downloaded from the Gene Expression
#' Omnibus (GEO). Four hundred seventy-two cases were available, representing several brain tumor diagnoses
#' (DIPG, glioblastoma, ETMR, medulloblastoma, ependymoma, pilocytic astrocytoma) and their further subgroups.
#'
#' The data sets were merged and probes that did not appear in all data sets were filtered away. In addition,
#' about 190,000 CpGs were removed due to SNPs, repeats and multiple mapping sites. The final data set contained
#' 206,823 unique probes and nine tumor classes including the medulloblastoma subgroups. K–neighbor imputation
#' was used for missing probe data.
#'
#' After that, a large number of regression analyses were performed to select the 100 probes per tumor class that
#' had the highest predictive power (AUC values). Based on the identified 900 methylation sites, the nine pediatric
#' brain tumor types could be accurately classified using the multiclass classification algorithm MethPed.
#' @docType data
#' @references
#' [1] Anna Danielsson, Szilárd Nemes, Magnus Tisell, Birgitta Lannering, Claes Nordborg, Magnus Sabel, and Helena Carén. "MethPed: A DNA Methylation Classifier Tool for the Identification of Pediatric Brain Tumor Subtypes". Clinical Epigenetics 2015, 7:62, 2015
#' @seealso  See \url{http://www.clinicalepigeneticsjournal.com/content/7/1/62} for more details.
#' @keywords data
#' @format Data frame of 900 probes.
#' @return  450k methylation array probe name
#' @examples
#' #################### Loading and view sample data
#' data(MethPed_900probes)
#' head(MethPed_900probes)
#'
MethPed_900probes<-data(MethPed_900probes,envir = environment())

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MethPed documentation built on Nov. 1, 2018, 4:21 a.m.