R/FlowSorted.Blood.EPIC.R

In immunomethylomics/FlowSorted.Blood.EPIC: Illumina EPIC data on immunomagnetic sorted peripheral adult blood cells

#' FlowSorted.Blood.EPIC
#' @description
#' Illumina Human Methylation data from EPIC on immunomagnetic sorted adult
#' blood cell populations. The FlowSorted.Blood.EPIC package contains Illumina
#' HumanMethylationEPIC (\dQuote{EPIC})) DNA methylation microarray data
#' from the immunomethylomics group
#' \href{https://dx.doi.org/10.1186/s13059-018-1448-7}{(Salas et al. 2018)},
#' consisting of 37 magnetic sorted blood cell references and 12 samples,
#' formatted as an RGChannelSet object for  integration and normalization using
#' most of the existing Bioconductor packages.
#'
#' This package contains data similar to the FlowSorted.Blood.450k
#' package consisting of data from peripheral blood samples generated from
#' adult men and women. However, when using the newer EPIC microarray minfi
#' estimates of cell type composition using the FlowSorted.Blood.450k package
#' are less precise compared to actual cell counts. Hence, this package
#' consists of appropriate data for deconvolution of adult blood samples
#' used in for example EWAS relying in the newer EPIC technology.
#'
#' Researchers may find this package useful as these samples represent
#' different cellular populations ( T lymphocytes (CD4+ and CD8+), B cells
#' (CD19+), monocytes (CD14+), NK cells (CD56+) and Neutrophils of cell
#' sorted blood generated with high purity estimates. As a test of accuracy
#' 12 experimental mixtures were reconstructed using fixed amounts of DNA from
#' purified cells. We offer the function estimateCellCounts2 a modification of
#' the popular estimatesCellCounts function in minfi.
#' This function allows estimating cellular composition in
#' users' whole blood Illumina EPIC samples using a modified version of
#' the algorithm constrained projection/quadratic programming described
#' in Houseman et al. 2012. For a slightly more accurate estimations we also
#' offered an IDOL optimized CpG selection for cell deconvolution as the object
#' IDOLOptimizedCpGs, and the IDOLOptimizedCpGs450klegacy object for legacy
#' 450K datasets. See the objects help for details.
#'
#'
#' @format A class: RGChannelSet, dimensions: 1051815 49
#' @source The FlowSorted.Blood.EPIC object is based in samples assayed
#' by Brock Christensen and colleagues; Salas et al. 2018.
#' \href{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110554}{GSE110554}
#' @seealso
#' References \enumerate{
#' \item LA Salas et al. (2018). \emph{An optimized library for
#' reference-based deconvolution of whole-blood biospecimens assayed using the
#' Illumina HumanMethylationEPIC BeadArray}. Genome Biology 19, 64. doi:
#' \href{https://dx.doi.org/10.1186/s13059-018-1448-7}{10.1186/s13059-018-1448-7}.
#' \item DC Koestler et al. (2016). \emph{Improving cell mixture deconvolution
#' by identifying optimal DNA methylation libraries (IDOL)}. BMC bioinformatics.
#' 17, 120. doi:\href{https://dx.doi.org/10.1186/s12859-016-0943-7}{10.1186/s12859-016-0943-7}.
#' \item EA Houseman et al. (2012) \emph{DNA methylation arrays as surrogate
#' measures of cell mixture distribution}. BMC Bioinformatics 13, 86.
#' doi:\href{https://dx.doi.org/10.1186/s12859-016-0943-7}{10.1186/1471-2105-13-86}.
#' \item \pkg{minfi} package, tools for analyzing DNA methylation microarrays
#' }
#'
#' @examples
#' # FlowSorted.Blood.EPIC<-
#' # libraryDataGet('FlowSorted.Blood.EPIC')
#' # FlowSorted.Blood.EPIC
#' # table(FlowSorted.Blood.EPIC$CellType)
#' @return RGChannelSet 49 samples
#' @usage
#' FlowSorted.Blood.EPIC
#' #See ?estimateCellCounts2 for cell deconvolution guidelines
"FlowSorted.Blood.EPIC"