R/realData.R

In cloneRate: Estimate Growth Rates from Phylogenetic Trees

#' Real clone data from human blood
#'
#' 42 clones (39 distinct) from 32 individual donors, 13 of whom have a diagnosis
#' of Myeloproliferative Neoplasm
#'
#' @docType data
#'
#' @usage data(realCloneData)
#'
#' @format A \code{list} of containing one \code{list} with the full ultrametric
#' trees from 30 of the 32 individual donors (the two from Van Egeren are not included),
#' and one \code{list} containing the 42 clone trees.In three cases, there are two
#' timepoints from the same clone, and these are separate phylo objects.
#' Each \code{list} contains a tree as a class \code{phylo} object.
#' See ape package documentation for details on class \code{phylo} objects.
#' Names of each \code{phylo} object (tree) in the list matches the naming used
#' in the sources and also includes driver, age, and clone number.
#'
#'
#' @references These datasets were generated and annotated in:
#' [Williams et al. 2022](https://pubmed.ncbi.nlm.nih.gov/35058638/)
#' [Mitchell et al. 2022](https://pubmed.ncbi.nlm.nih.gov/35650442/)
#' [Fabre et al. 2022](https://pubmed.ncbi.nlm.nih.gov/35650444/)
#' [Van Egeren et al. 2021](https://pubmed.ncbi.nlm.nih.gov/33621486/)
#' @examples
#' # Plot full reconstructed tree from donor PD34493
#' ape::plot.phylo(cloneRate::realCloneData[["fullTrees"]][["PD34493"]],
#'   direction = "downwards", show.tip.label = FALSE
#' )
#'
"realCloneData"


#' Longitudinal validation data
#'
#' For three individuals with clonal expansions that can be estimated using our methods,
#' we have longitudinal data to orthogonally validate these estimates, which is included here.
#' Additionally, for 13 clones with a driver gene matching a driver gene in the single cell
#' data, but without a match to a specific clone, we include this longitudinal data as well.
#'
#' @format A \code{data.frame} containing all the information needed
#' \describe{
#'  \item{Sample.ID}{The individual's ID}
#'  \item{Age}{Individual's age at the various sampling times}
#'  \item{VAF}{The variant allele frequency at the various sampling times for the clone of interest}
#'  \item{Gene}{Gene or genes with mutation that identifies the clone}
#'  \item{Protein}{Protein affected by the mutation}
#'  \item{cellType}{The type of cells used for sequencing}
#'  \item{cloneName}{The name we use for the clone to match to single cell data, if applicable.}
#' }
#'
#' @references These datasets were generated and annotated in:
#' [Williams et al. 2022](https://pubmed.ncbi.nlm.nih.gov/35058638/)
#' [Fabre et al. 2022](https://pubmed.ncbi.nlm.nih.gov/35650444/)
#'
#' @keywords longitudinal
#'
#' @examples
#' # Plot longitudinal data from PD9478
#' library(ggplot2)
#' ggplot(longitudinalData[longitudinalData$Sample.ID == "PD9478", ]) +
#'   geom_point(aes(x = Age, y = VAF))
"longitudinalData"