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R/timescape.R
In timescape: Patient Clonal Timescapes
Defines functions
replaceSpaces
getMutationsData
checkPerts
checkCloneColours
checkGtypePositioning
checkTreeEdges
checkClonalPrev
checkAlpha
checkRequiredInputs
checkMinDims
processUserData
renderTimescape
timescapeOutput
timescape
Documented in
checkAlpha
checkClonalPrev
checkCloneColours
checkGtypePositioning
checkMinDims
checkPerts
checkRequiredInputs
checkTreeEdges
getMutationsData
processUserData
renderTimescape
replaceSpaces
timescape
timescapeOutput
#' TimeScape
#'
#' \code{timescape} is a tool for visualizing temporal clonal evolution data.
#'
#' Interactive components:
#' \enumerate{
#'
#' \item Mouseover any clone to view its (i) clone ID and (ii) clonal
#' prevalence at each time point.
#'
#' \item Click the view switch button to switch from the traditional
#' timescape view to the clonal trajectory view, where each clone
#' changes prevalence on its own track.
#'
#' \item Click the download buttons to download a PNG or SVG of the
#' view.
#'
#' }
#'
#' @param clonal_prev \code{data.frame} Clonal prevalence.
#' Required columns are:
#' \describe{
#'
#' \item{timepoint:}{\code{character()} time point. Time
#' points will be alphanumerically sorted in the view.}
#'
#' \item{clone_id:}{\code{character()} clone id.}
#'
#' \item{clonal_prev:}{\code{numeric()} clonal prevalence.}
#'
#' }
#' @param tree_edges \code{data.frame} Tree edges of a rooted tree.
#' Required columns are:
#' \describe{
#'
#' \item{source:}{\code{character()} source node id.}
#'
#' \item{target:}{\code{character()} target node id.}
#'
#' }
#' @param mutations \code{data.frame} (Optional) Mutations
#' occurring at each clone. Required columns are:
#' \describe{
#'
#' \item{chrom:}{\code{character()} chromosome number.}
#'
#' \item{coord:}{\code{numeric()} coordinate of mutation
#' on chromosome.}
#'
#' \item{clone_id:}{\code{character()} clone id.}
#'
#' \item{timepoint:}{\code{character()} time point.}
#'
#' \item{VAF:}{\code{numeric()} variant allele frequency
#' of the mutation in the corresponding timepoint.}
#'
#' }
#' Any additional field will be shown in the mutation table.
#' @param clone_colours \code{data.frame} Clone ids and their
#' corresponding colours. Required columns are:
#' \describe{
#'
#' \item{clone_id:}{\code{character()} clone id.}
#'
#' \item{colour:}{\code{character()} the corresponding Hex
#' colour for each clone id.}
#'
#' }
#' @param xaxis_title \code{character()} (Optional) x-axis title.
#' Default is "Time Point".
#' @param yaxis_title \code{character()} (Optional) y-axis title.
#' Default is "Clonal Prevalence".
#' @param phylogeny_title \code{character()} (Optional) Legend
#' phylogeny title. Default is "Clonal Phylogeny".
#' @param alpha \code{numeric()} (Optional) Alpha value for clonal
#' sweeps, range [0, 100].
#' @param genotype_position \code{character()} (Optional) How to
#' position the genotypes from ["centre", "stack", "space"].
#' \enumerate{
#'
#' \item centre: genotypes are centred with
#' respect to their ancestors.
#'
#' \item stack: genotypes are stacked such
#' that nogenotype is split at any time point.
#'
#' \item space: genotypes are stacked but
#' with a bit of spacing at the bottom.
#'
#' }
#' @param perturbations \code{data.frame} (Optional) Any
#' perturbations that occurred between two time points.
#' Required columns are:
#' \describe{
#'
#' \item{pert_name:}{\code{character()} the perturbation name.}
#'
#' \item{prev_tp:}{\code{character()} the time point (as labelled
#' in clonal prevalence data) BEFORE perturbation.}
#'
#' }
#' @param sort \code{logical()} (Optional) Whether (TRUE) or not (FALSE)
#' to vertically sort the genotypes by their emergence values
#' (descending). Default is FALSE. Note that genotype sorting will
#' always retain the phylogenetic hierarchy, and this parameter will
#' only affect the ordering of siblings.
#' @param show_warnings \code{logical()} (Optional) Whether or not to show
#' any warnings. Default is TRUE.
#' @param width \code{numeric()} (Optional) Width of the plot. Minimum
#' width is 450.
#' @param height \code{numeric()} (Optional) Height of the plot. Minimum
#' height with and without mutations is 500 and 260, respectively.
#' @export
#' @examples
#'
#' # EXAMPLE 1 - Acute myeloid leukemia patient, Ding et al., 2012
#'
#' # genotype tree edges
#' tree_edges <- read.csv(system.file("extdata", "AML_tree_edges.csv",
#' package = "timescape"))
#'
#' # clonal prevalences
#' clonal_prev <- read.csv(system.file("extdata", "AML_clonal_prev.csv",
#' package = "timescape"))
#'
#' # targeted mutations
#' mutations <- read.csv(system.file("extdata", "AML_mutations.csv",
#' package = "timescape"))
#'
#' # perturbations
#' perturbations <- data.frame( pert_name = c("Chemotherapy"),
#' prev_tp = c("Diagnosis"))
#'
#' # run timescape
#' timescape(clonal_prev = clonal_prev, tree_edges = tree_edges,
#' perturbations = perturbations, mutations = mutations)
#'
#' # EXAMPLE 2 - Patient 7, McPherson and Roth et al., 2016
#'
#' # genotype tree edges
#' tree_edges <- read.csv(system.file("extdata", "px7_tree_edges.csv",
#' package = "timescape"))
#'
#' # clonal prevalences
#' clonal_prev <- read.csv(system.file("extdata", "px7_clonal_prev.csv",
#' package = "timescape"))
#'
#' # clone colours
#' clone_colours <- data.frame(clone_id = c("A","B","C","D","E"),
#' colour = c("d0ced0", "2CD0AB", "FFD94B",
#' "FD8EE5", "F8766D"))
#'
#' # run timescape
#' timescape(clonal_prev = clonal_prev, tree_edges = tree_edges,
#' clone_colours = clone_colours, height=260, alpha=15)
#' @return None
timescape <- function(clonal_prev,
tree_edges,
mutations = "NA",
clone_colours = "NA",
xaxis_title = "Time Point",
yaxis_title = "Clonal Prevalence",
phylogeny_title = "Clonal Phylogeny",
alpha = 50,
genotype_position = "stack",
perturbations = "NA",
sort = FALSE,
show_warnings = TRUE,
width = 900,
height = NULL) {
# forward options using x
x = processUserData(clonal_prev,
tree_edges,
mutations,
clone_colours,
xaxis_title,
yaxis_title,
phylogeny_title,
alpha,
genotype_position,
perturbations,
sort,
show_warnings,
width,
height)
# create widget
htmlwidgets::createWidget(
name = "timescape",
x,
width = width,
height = height,
package = "timescape"
)
}
#' Widget output function for use in Shiny
#'
#' @param outputId -- id of output
#' @param width -- width of output
#' @param height -- height of output
#' @examples
#' timescapeOutput(1, '100%', '300px')
#' timescapeOutput(1, '80%', '300px')
#' @rdname helpers
#' @export
#' @return None
timescapeOutput <- function(outputId, width = "100%", height = "400px"){
htmlwidgets::shinyWidgetOutput(outputId, "timescape", width, height,
package = "timescape")
}
#' Widget render function for use in Shiny
#'
#' @param expr -- expression for Shiny
#' @param env -- environment for Shiny
#' @param quoted -- default is FALSE
#' @export
#' @rdname helpers
#' @return None
renderTimescape <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) } # force quoted
htmlwidgets::shinyRenderWidget(expr, timescapeOutput, env, quoted = TRUE)
}
#' Function to process the user data
#' @param clonal_prev -- data frame of Clonal prevalence. Note: timepoints will be alphanumerically sorted in the view.
#' Format: columns are (1) character() "timepoint" - time point
#' (2) character() "clone_id" - clone id
#' (3) numeric() "clonal_prev" - clonal prevalence.
#' @param tree_edges -- data frame of Tree edges of a rooted tree.
#' Format: columns are (1) character() "source" - source node id
#' (2) character() "target" - target node id.
#' @param mutations -- data frame (Optional) of Mutations occurring at each clone. Any additional field will be shown in the mutation table.
#' Format: columns are (1) character() "chrom" - chromosome number
#' (2) numeric() "coord" - coordinate of mutation on chromosome
#' (3) character() "clone_id" - clone id
#' (4) character() "timepoint" - time point
#' (5) numeric() "VAF" - variant allele frequency of the mutation in the corresponding timepoint.
#' @param clone_colours -- data frame (Optional) of Clone ids and their corresponding colours
#' Format: columns are (1) character() "clone_id" - the clone ids
#' (2) character() "colour" - the corresponding Hex colour for each clone id.
#' @param xaxis_title -- String (Optional) of x-axis title. Default is "Time Point".
#' @param yaxis_title -- String (Optional) of y-axis title. Default is "Clonal Prevalence".
#' @param phylogeny_title -- String (Optional) of Legend phylogeny title. Default is "Clonal Phylogeny".
#' @param alpha -- Number (Optional) of Alpha value for sweeps, range [0, 100].
#' @param genotype_position -- String (Optional) of How to position the genotypes from ["centre", "stack", "space"]
#' "centre" -- genotypes are centred with respect to their ancestors
#' "stack" -- genotypes are stacked such that no genotype is split at any time point
#' "space" -- genotypes are stacked but with a bit of spacing at the bottom
#' @param perturbations -- data frame (Optional) of any perturbations that occurred between two time points.
#' Format: columns are (1) character() "pert_name" - the perturbation name
#' (2) character() "prev_tp" - the time point (as labelled in clonal prevalence data)
#' BEFORE perturbation.
#' @param sort -- Boolean (Optional) of whether (TRUE) or not (FALSE) to vertically sort the genotypes by their emergence values (descending).
#' Default is FALSE.
#' Note that genotype sorting will always retain the phylogenetic hierarchy, and this parameter will only affect the ordering of siblings.
#' @param show_warnings -- Boolean (Optional) of Whether or not to show any warnings. Default is TRUE.
#' @param width -- Number (Optional) of width of the plot. Minimum width is 450.
#' @param height -- Number (Optional) of height of the plot. Minimum height with and without mutations is 500 and 260, respectively.
#' @export
#' @rdname helpers
#' @return Returns the ready list of user input data for htmlwidget
processUserData <- function(clonal_prev,
tree_edges,
mutations,
clone_colours,
xaxis_title,
yaxis_title,
phylogeny_title,
alpha,
genotype_position,
perturbations,
sort,
show_warnings,
width,
height) {
# ENSURE MINIMUM DIMENSIONS SATISFIED
checkMinDims(mutations, height, width)
# CHECK REQUIRED INPUTS ARE PRESENT
checkRequiredInputs(clonal_prev, tree_edges)
# ALPHA VALUE
checkAlpha(alpha)
# SORTED GENOTYPES
if (!is.logical(sort)) {
stop("Sort parameter must be a boolean.")
}
# CLONAL PREVALENCE DATA
clonal_prev <- checkClonalPrev(clonal_prev)
# TREE EDGES DATA
tree_edges <- checkTreeEdges(tree_edges)
# GENOTYPE POSITIONING
checkGtypePositioning(genotype_position)
# CHECK CLONE COLOURS
checkCloneColours(clone_colours)
# CHECK PERTURBATIONS
perturbations <- checkPerts(perturbations)
# MUTATIONS DATA
mut_data <- getMutationsData(mutations, tree_edges, clonal_prev)
mutation_info <- mut_data$mutation_info
mutation_prevalences <- mut_data$mutation_prevalences
if (is.data.frame(mutations)) {
mutations_provided <- TRUE
}
else {
mutations_provided <- FALSE
}
# REPLACE SPACES WITH UNDERSCORES
spaces_replaced <- replaceSpaces(clonal_prev, tree_edges, clone_colours, mutation_info, mutations, mutation_prevalences)
timepoint_map <- spaces_replaced$timepoint_map
clone_id_map <- spaces_replaced$clone_id_map
clonal_prev <- spaces_replaced$clonal_prev
tree_edges <- spaces_replaced$tree_edges
mutation_info <- spaces_replaced$mutation_info
clone_colours <- spaces_replaced$clone_colours
mutation_prevalences <- spaces_replaced$mutation_prevalences
# forward options using x
return(list(
clonal_prev = jsonlite::toJSON(clonal_prev),
gtype_tree_edges = jsonlite::toJSON(tree_edges),
clone_cols = jsonlite::toJSON(clone_colours),
mutations = jsonlite::toJSON(mutation_info),
mutation_prevalences = jsonlite::toJSON(mutation_prevalences),
mutations_provided=mutations_provided, # whether or not mutations are provided
xaxis_title = as.character(xaxis_title),
yaxis_title = as.character(yaxis_title),
phylogeny_title = as.character(phylogeny_title),
alpha = alpha,
genotype_position = genotype_position,
perturbations = jsonlite::toJSON(perturbations),
sort_gtypes = sort,
timepoint_map = jsonlite::toJSON(timepoint_map),
clone_id_map = jsonlite::toJSON(clone_id_map)
))
}
#' Function to check minimum dimensions
#'
#' @param mutations -- mutations provided by user
#' @param height -- height provided by user
#' @param width -- width provided by user
#' @examples
#' checkMinDims(data.frame(chr = c("11"), coord = c(104043), VAF = c(0.1)), "700px", "700px")
#' @export
#' @rdname helpers
#' @return None
checkMinDims <- function(mutations, height, width) {
# set height if not set by user
if (is.null(height)) {
if (!is.data.frame(mutations)) { # no mutations
height = 260
}
else { # mutations
height = 500
}
}
# check height is big enough
min_width = 450
if (!is.data.frame(mutations)) { # no mutations
min_height = 260
}
else { # mutations
min_height = 500
}
if (height < min_height) {
stop("Height must be greater than or equal to ", min_height, "px.")
}
if (width < min_width) {
stop("Width must be greater than or equal to ", min_width, "px.")
}
}
#' Function to check required inputs are present
#'
#' @param clonal_prev -- clonal_prev provided by user
#' @param tree_edges -- tree_edges provided by user
#' @examples
#' checkRequiredInputs(data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.1","0.22","0.08","0.53","0.009","0.061","1")),
#' data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")))
#' checkRequiredInputs(data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.12","0.12","0.18","0.13","0.009","0.061","1")),
#' data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")))
#' @export
#' @rdname helpers
#' @return None
checkRequiredInputs <- function(clonal_prev, tree_edges) {
if (missing(clonal_prev)) {
stop("Clonal prevalence data frame must be provided.")
}
if (missing(tree_edges)) {
stop("Tree edge data frame must be provided.")
}
}
#' check alpha value input is correct
#'
#' @param alpha -- alpha provided by user
#' @examples
#' checkAlpha(4)
#' checkAlpha(100)
#' @export
#' @rdname helpers
#' @return None
checkAlpha <- function(alpha) {
if (!is.numeric(alpha)) {
stop("Alpha value must be numeric.")
}
if (alpha < 0 || alpha > 100) {
stop("Alpha value must be between 0 and 100.")
}
}
#' check clonal_prev parameter data
#'
#' @param clonal_prev -- clonal prevalence provided by user
#' @examples
#' checkClonalPrev(data.frame(timepoint=c(1), clone_id=c(2), clonal_prev=c(0.1)))
#' @export
#' @rdname helpers
#' @return Clonal prevalence data after checkint it for column names and content types
checkClonalPrev <- function(clonal_prev) {
# ensure column names are correct
if (!("timepoint" %in% colnames(clonal_prev)) ||
!("clone_id" %in% colnames(clonal_prev)) ||
!("clonal_prev" %in% colnames(clonal_prev))) {
stop("Clonal prevalence data frame must have the following column names: ",
"\"timepoint\", \"clone_id\", \"clonal_prev\"")
}
# ensure data is of the correct type
clonal_prev$timepoint <- as.character(clonal_prev$timepoint)
clonal_prev$clone_id <- as.character(clonal_prev$clone_id)
clonal_prev$clonal_prev <- as.numeric(as.character(clonal_prev$clonal_prev))
return(clonal_prev)
}
#' check tree_edges parameter data
#'
#' @param tree_edges -- tree edges provided by user
#' @examples
#' checkTreeEdges(data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")))
#' @export
#' @rdname helpers
#' @return Tree edges data after checkint it for column names and content types
checkTreeEdges <- function(tree_edges) {
# ensure column names are correct
if (!("source" %in% colnames(tree_edges)) ||
!("target" %in% colnames(tree_edges))) {
stop("Tree edges data frame must have the following column names: ",
"\"source\", \"target\"")
}
# ensure data is of the correct type
tree_edges$source <- as.character(tree_edges$source)
tree_edges$target <- as.character(tree_edges$target)
# check for tree rootedness
sources <- unique(tree_edges$source)
targets <- unique(tree_edges$target)
sources_for_iteration <- sources # because we will be changing the sources array over time
for (i in 1:length(sources_for_iteration)) {
cur_source <- sources_for_iteration[i]
# if the source is a target, remove it from the sources list
if (cur_source %in% targets) {
sources <- sources[sources != cur_source]
}
}
# if multiple roots are detected, throw error
if (length(sources) > 1) {
stop("Multiple roots detected in tree (",paste(sources,collapse=", "),
") - tree must have only one root.")
}
# if an edge is found whose source and target are equal, throw an error
if (length(which(as.character(tree_edges$source) == as.character(tree_edges$target))) > 0) {
stop("One of the tree edges has a source as its own target. Remove this edge.")
}
return(tree_edges)
}
#' check genotype_position parameter
#'
#' @param genotype_position -- genotype_position provided by user
#' @examples
#' checkGtypePositioning("centre")
#' @export
#' @rdname helpers
#' @return None
checkGtypePositioning <- function(genotype_position) {
if (!(genotype_position %in% c("stack", "centre", "space"))) {
stop("Genotype position must be one of c(\"stack\", \"centre\", \"space\")")
}
}
#' check clone_colours parameter
#'
#' @param clone_colours -- clone_colours provided by user
#' @examples
#' checkCloneColours(data.frame(clone_id = c("1","2","3", "4"), colour = c("#beaed4", "#fdc086", "#beaed4", "#beaed4")))
#' @export
#' @rdname helpers
#' @return None
checkCloneColours <- function(clone_colours) {
if (is.data.frame(clone_colours)) {
# ensure column names are correct
if (!("clone_id" %in% colnames(clone_colours)) ||
!("colour" %in% colnames(clone_colours))) {
stop("Node colour data frame must have the following column names: ",
"\"clone_id\", \"colour\"")
}
}
}
#' check perturbations parameter
#'
#' @param perturbations -- perturbations provided by user
#' @examples
#' checkPerts(data.frame(pert_name = c("New Drug"), prev_tp = c("Diagnosis")))
#' @export
#' @rdname helpers
#' @return Perturbations after checking them for content types and column names
checkPerts <- function(perturbations) {
if (is.data.frame(perturbations)) {
# ensure column names are correct
if (!("pert_name" %in% colnames(perturbations)) ||
!("prev_tp" %in% colnames(perturbations))) {
stop("Perturbations data frame must have the following column names: ",
"\"pert_name\", \"prev_tp\"")
}
# check that columns are of the correct type
perturbations$pert_name <- as.character(perturbations$pert_name)
perturbations$prev_tp <- as.character(perturbations$prev_tp)
}
return(perturbations)
}
#' get mutation data
#'
#' @param mutations -- mutations data from user
#' @param tree_edges -- tree edges data from user
#' @param clonal_prev -- clonal prevalence data from user
#' @examples
#' getMutationsData(data.frame(chrom = c("11"), coord = c(104043), VAF = c(0.1), clone_id=c(1), timepoint=c("Relapse")),
#' data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")),
#' data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.12","0.12","0.18","0.13","0.009","0.061","1")))
#' @export
#' @rdname helpers
#' @return List of mutation information and mutation prevalences
getMutationsData <- function(mutations, tree_edges, clonal_prev) {
if (is.data.frame(mutations)) {
# ensure column names are correct
if (!("chrom" %in% colnames(mutations)) ||
!("coord" %in% colnames(mutations)) ||
!("clone_id" %in% colnames(mutations)) ||
!("timepoint" %in% colnames(mutations)) ||
!("VAF" %in% colnames(mutations))) {
stop("Mutations data frame must have the following column names: ",
"\"chrom\", \"coord\", \"clone_id\", \"timepoint\", \"VAF\".")
}
# ensure data is of the correct type
mutations$chrom <- toupper(as.character(mutations$chrom)) # upper case X & Y
mutations$coord <- as.character(mutations$coord)
mutations$timepoint <- as.character(mutations$timepoint)
mutations$clone_id <- as.character(mutations$clone_id)
mutations$VAF <- as.numeric(as.character(mutations$VAF))
# check for optional info, and ensure data of correct type
extra_columns <- colnames(mutations)[which(!(colnames(mutations) %in% c("chrom", "coord", "clone_id", "timepoint", "VAF")))]
mutations <- data.frame(lapply(mutations, as.character), stringsAsFactors=FALSE)
# check that all CLONE IDS in the mutations data are present in the tree data
mutations_clone_ids <- unique(mutations$clone_id)
tree_edges_clone_ids <- c(unique(tree_edges$source), unique(tree_edges$target))
clone_ids_missing_from_tree_edges_data <- setdiff(mutations_clone_ids, tree_edges_clone_ids)
if (length(clone_ids_missing_from_tree_edges_data) > 0) {
stop("The following clone ID(s) are present in the mutations data but ",
"are missing from the tree edges data: ",
paste(clone_ids_missing_from_tree_edges_data, collapse=", "), ".")
}
# check that all TIMEPOINTS in the mutations data are present in the clonal prev data
mutations_tps <- unique(mutations$timepoint)
clonal_prev_tps <- unique(clonal_prev$timepoint)
tps_missing_from_clonal_prev_data <- setdiff(mutations_tps, clonal_prev_tps)
if (length(tps_missing_from_clonal_prev_data) > 0) {
stop("The following timepoint(s) are present in the mutations data but ",
"are missing from the clonal prevalence data: ",
paste(tps_missing_from_clonal_prev_data, collapse=", "), ".")
}
# create a location column, combining the chromosome and the coordinate
mutations$location <- apply(mutations[, c("chrom","coord")], 1 , paste, collapse = ":")
# coordinate is now a number
mutations$coord <- as.numeric(as.character(mutations$coord))
# check X & Y chromosomes are labelled "X" and "Y", not "23", "24"
num_23 <- mutations[which(mutations$chrom == "23"),]
if (nrow(num_23) > 0) {
stop("Chromosome numbered \"23\" was detected in mutations data frame - X and Y chromosomes ",
"must be labelled \"X\" and \"Y\".")
}
# get list of clones in the phylogeny
clones_in_phylo <- unique(c(tree_edges$source, tree_edges$target))
# keep only those mutations whose clone ids are present in the phylogeny
mutations <- mutations[which(mutations$clone_id %in% clones_in_phylo),]
# MUTATION PREVALENCES DATA
mutation_prevalences <- mutations
# keep only those mutations whose clone ids are present in the phylogeny
mutation_prevalences <- mutation_prevalences[which(mutation_prevalences$clone_id %in% clones_in_phylo),]
# warn if more than 10,000 rows in data that the visualization may be slow
if (nrow(mutation_prevalences) > 10000 && show_warnings) {
print(paste("[WARNING] Number of rows in mutations data exceeds 10,000. ",
"Resultantly, visualization may be slow. ",
"It is recommended to filter the data to a smaller set of mutations.", sep=""))
}
# compress results
prevs_split <- split(mutation_prevalences, f = mutation_prevalences$location)
# reduce the size of the data frame in each list
prevs_split_small <- lapply(prevs_split, function(prevs) {
return(prevs[,c("timepoint", "VAF")])
})
# MUTATION INFO
mutation_info <- unique(mutations[,c("chrom","coord","clone_id",extra_columns)])
}
else {
prevs_split_small <- "NA"
mutation_info <- "NA"
}
return(list("mutation_info"=mutation_info, "mutation_prevalences"=prevs_split_small))
}
#' function to replace spaces with underscores in all data frames & keep maps of original names to space-replaced names
#' @param clonal_prev -- clonal_prev data from user
#' @param tree_edges -- tree edges data from user
#' @param clone_colours -- clone_colours data from user
#' @param mutation_info -- processed mutation_info
#' @param mutations -- mutations data from user
#' @param mutation_prevalences -- mutation_prevalences data from user
#' @export
#' @rdname helpers
#' @examples
#' replaceSpaces(mutations = data.frame(chrom = c("11"), coord = c(104043), VAF = c(0.1), clone_id=c(1), timepoint=c("Relapse")),
#' tree_edges = data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")),
#' clonal_prev = data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.12","0.12","0.18","0.13","0.009","0.061","1")),
#' mutation_prevalences = list("X:6154028" = data.frame(timepoint = c("Diagnosis"), VAF = c(0.5557))), mutation_info=data.frame(clone_id=c(1)),
#' clone_colours = data.frame(clone_id = c("1","2","3", "4"), colour = c("#beaed4", "#fdc086", "#beaed4", "#beaed4")))
#' @return List of data frames with spaces replaced
replaceSpaces <- function(clonal_prev, tree_edges, clone_colours, mutation_info, mutations, mutation_prevalences) {
# create map of original sample ids to space-replaced sample ids
timepoint_map <- data.frame(original_timepoint = unique(clonal_prev$timepoint), stringsAsFactors=FALSE)
timepoint_map$space_replaced_timepoint <- stringr::str_replace_all(timepoint_map$original_timepoint,"\\s+","_")
# create map of original clone ids to space-replaced clone ids
clone_id_map <- data.frame(original_clone_id = unique(c(tree_edges$source, tree_edges$target)), stringsAsFactors=FALSE)
clone_id_map$space_replaced_clone_id <- stringr::str_replace_all(clone_id_map$original_clone_id,"\\s+","_")
# replace spaces with underscores
# --> timepoints
clonal_prev$timepoint <- stringr::str_replace_all(clonal_prev$timepoint,"\\s+","_")
if (is.data.frame(mutations)) {
mutation_prevalences <- lapply(mutation_prevalences, function(prevs) {
prevs$timepoint <- stringr::str_replace_all(prevs$timepoint,"\\s+","_")
return(prevs)
})
}
# --> clone ids
clonal_prev$clone_id <- stringr::str_replace_all(clonal_prev$clone_id,"\\s+","_")
tree_edges$source <- stringr::str_replace_all(tree_edges$source,"\\s+","_")
tree_edges$target <- stringr::str_replace_all(tree_edges$target,"\\s+","_")
if (is.data.frame(clone_colours)) {
clone_colours$clone_id <- stringr::str_replace_all(clone_colours$clone_id,"\\s+","_")
}
if (is.data.frame(mutations)) {
mutation_info$clone_id <- stringr::str_replace_all(mutation_info$clone_id,"\\s+","_")
}
return(list("timepoint_map"=timepoint_map,
"clone_id_map"=clone_id_map,
"clonal_prev"=clonal_prev,
"tree_edges"=tree_edges,
"mutation_info"=mutation_info,
"clone_colours"=clone_colours,
"mutation_prevalences"=mutation_prevalences))
}
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Defines functions replaceSpaces getMutationsData checkPerts checkCloneColours checkGtypePositioning checkTreeEdges checkClonalPrev checkAlpha checkRequiredInputs checkMinDims processUserData renderTimescape timescapeOutput timescape
Documented in checkAlpha checkClonalPrev checkCloneColours checkGtypePositioning checkMinDims checkPerts checkRequiredInputs checkTreeEdges getMutationsData processUserData renderTimescape replaceSpaces timescape timescapeOutput
#' TimeScape
#'
#' \code{timescape} is a tool for visualizing temporal clonal evolution data.
#'
#' Interactive components:
#' \enumerate{
#'
#' \item Mouseover any clone to view its (i) clone ID and (ii) clonal
#' prevalence at each time point.
#'
#' \item Click the view switch button to switch from the traditional
#' timescape view to the clonal trajectory view, where each clone
#' changes prevalence on its own track.
#'
#' \item Click the download buttons to download a PNG or SVG of the
#' view.
#'
#' }
#'
#' @param clonal_prev \code{data.frame} Clonal prevalence.
#' Required columns are:
#' \describe{
#'
#' \item{timepoint:}{\code{character()} time point. Time
#' points will be alphanumerically sorted in the view.}
#'
#' \item{clone_id:}{\code{character()} clone id.}
#'
#' \item{clonal_prev:}{\code{numeric()} clonal prevalence.}
#'
#' }
#' @param tree_edges \code{data.frame} Tree edges of a rooted tree.
#' Required columns are:
#' \describe{
#'
#' \item{source:}{\code{character()} source node id.}
#'
#' \item{target:}{\code{character()} target node id.}
#'
#' }
#' @param mutations \code{data.frame} (Optional) Mutations
#' occurring at each clone. Required columns are:
#' \describe{
#'
#' \item{chrom:}{\code{character()} chromosome number.}
#'
#' \item{coord:}{\code{numeric()} coordinate of mutation
#' on chromosome.}
#'
#' \item{clone_id:}{\code{character()} clone id.}
#'
#' \item{timepoint:}{\code{character()} time point.}
#'
#' \item{VAF:}{\code{numeric()} variant allele frequency
#' of the mutation in the corresponding timepoint.}
#'
#' }
#' Any additional field will be shown in the mutation table.
#' @param clone_colours \code{data.frame} Clone ids and their
#' corresponding colours. Required columns are:
#' \describe{
#'
#' \item{clone_id:}{\code{character()} clone id.}
#'
#' \item{colour:}{\code{character()} the corresponding Hex
#' colour for each clone id.}
#'
#' }
#' @param xaxis_title \code{character()} (Optional) x-axis title.
#' Default is "Time Point".
#' @param yaxis_title \code{character()} (Optional) y-axis title.
#' Default is "Clonal Prevalence".
#' @param phylogeny_title \code{character()} (Optional) Legend
#' phylogeny title. Default is "Clonal Phylogeny".
#' @param alpha \code{numeric()} (Optional) Alpha value for clonal
#' sweeps, range [0, 100].
#' @param genotype_position \code{character()} (Optional) How to
#' position the genotypes from ["centre", "stack", "space"].
#' \enumerate{
#'
#' \item centre: genotypes are centred with
#' respect to their ancestors.
#'
#' \item stack: genotypes are stacked such
#' that nogenotype is split at any time point.
#'
#' \item space: genotypes are stacked but
#' with a bit of spacing at the bottom.
#'
#' }
#' @param perturbations \code{data.frame} (Optional) Any
#' perturbations that occurred between two time points.
#' Required columns are:
#' \describe{
#'
#' \item{pert_name:}{\code{character()} the perturbation name.}
#'
#' \item{prev_tp:}{\code{character()} the time point (as labelled
#' in clonal prevalence data) BEFORE perturbation.}
#'
#' }
#' @param sort \code{logical()} (Optional) Whether (TRUE) or not (FALSE)
#' to vertically sort the genotypes by their emergence values
#' (descending). Default is FALSE. Note that genotype sorting will
#' always retain the phylogenetic hierarchy, and this parameter will
#' only affect the ordering of siblings.
#' @param show_warnings \code{logical()} (Optional) Whether or not to show
#' any warnings. Default is TRUE.
#' @param width \code{numeric()} (Optional) Width of the plot. Minimum
#' width is 450.
#' @param height \code{numeric()} (Optional) Height of the plot. Minimum
#' height with and without mutations is 500 and 260, respectively.
#' @export
#' @examples
#'
#' # EXAMPLE 1 - Acute myeloid leukemia patient, Ding et al., 2012
#'
#' # genotype tree edges
#' tree_edges <- read.csv(system.file("extdata", "AML_tree_edges.csv",
#' package = "timescape"))
#'
#' # clonal prevalences
#' clonal_prev <- read.csv(system.file("extdata", "AML_clonal_prev.csv",
#' package = "timescape"))
#'
#' # targeted mutations
#' mutations <- read.csv(system.file("extdata", "AML_mutations.csv",
#' package = "timescape"))
#'
#' # perturbations
#' perturbations <- data.frame( pert_name = c("Chemotherapy"),
#' prev_tp = c("Diagnosis"))
#'
#' # run timescape
#' timescape(clonal_prev = clonal_prev, tree_edges = tree_edges,
#' perturbations = perturbations, mutations = mutations)
#'
#' # EXAMPLE 2 - Patient 7, McPherson and Roth et al., 2016
#'
#' # genotype tree edges
#' tree_edges <- read.csv(system.file("extdata", "px7_tree_edges.csv",
#' package = "timescape"))
#'
#' # clonal prevalences
#' clonal_prev <- read.csv(system.file("extdata", "px7_clonal_prev.csv",
#' package = "timescape"))
#'
#' # clone colours
#' clone_colours <- data.frame(clone_id = c("A","B","C","D","E"),
#' colour = c("d0ced0", "2CD0AB", "FFD94B",
#' "FD8EE5", "F8766D"))
#'
#' # run timescape
#' timescape(clonal_prev = clonal_prev, tree_edges = tree_edges,
#' clone_colours = clone_colours, height=260, alpha=15)
#' @return None
timescape <- function(clonal_prev,
tree_edges,
mutations = "NA",
clone_colours = "NA",
xaxis_title = "Time Point",
yaxis_title = "Clonal Prevalence",
phylogeny_title = "Clonal Phylogeny",
alpha = 50,
genotype_position = "stack",
perturbations = "NA",
sort = FALSE,
show_warnings = TRUE,
width = 900,
height = NULL) {
# forward options using x
x = processUserData(clonal_prev,
tree_edges,
mutations,
clone_colours,
xaxis_title,
yaxis_title,
phylogeny_title,
alpha,
genotype_position,
perturbations,
sort,
show_warnings,
width,
height)
# create widget
htmlwidgets::createWidget(
name = "timescape",
x,
width = width,
height = height,
package = "timescape"
)
}
#' Widget output function for use in Shiny
#'
#' @param outputId -- id of output
#' @param width -- width of output
#' @param height -- height of output
#' @examples
#' timescapeOutput(1, '100%', '300px')
#' timescapeOutput(1, '80%', '300px')
#' @rdname helpers
#' @export
#' @return None
timescapeOutput <- function(outputId, width = "100%", height = "400px"){
htmlwidgets::shinyWidgetOutput(outputId, "timescape", width, height,
package = "timescape")
}
#' Widget render function for use in Shiny
#'
#' @param expr -- expression for Shiny
#' @param env -- environment for Shiny
#' @param quoted -- default is FALSE
#' @export
#' @rdname helpers
#' @return None
renderTimescape <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) } # force quoted
htmlwidgets::shinyRenderWidget(expr, timescapeOutput, env, quoted = TRUE)
}
#' Function to process the user data
#' @param clonal_prev -- data frame of Clonal prevalence. Note: timepoints will be alphanumerically sorted in the view.
#' Format: columns are (1) character() "timepoint" - time point
#' (2) character() "clone_id" - clone id
#' (3) numeric() "clonal_prev" - clonal prevalence.
#' @param tree_edges -- data frame of Tree edges of a rooted tree.
#' Format: columns are (1) character() "source" - source node id
#' (2) character() "target" - target node id.
#' @param mutations -- data frame (Optional) of Mutations occurring at each clone. Any additional field will be shown in the mutation table.
#' Format: columns are (1) character() "chrom" - chromosome number
#' (2) numeric() "coord" - coordinate of mutation on chromosome
#' (3) character() "clone_id" - clone id
#' (4) character() "timepoint" - time point
#' (5) numeric() "VAF" - variant allele frequency of the mutation in the corresponding timepoint.
#' @param clone_colours -- data frame (Optional) of Clone ids and their corresponding colours
#' Format: columns are (1) character() "clone_id" - the clone ids
#' (2) character() "colour" - the corresponding Hex colour for each clone id.
#' @param xaxis_title -- String (Optional) of x-axis title. Default is "Time Point".
#' @param yaxis_title -- String (Optional) of y-axis title. Default is "Clonal Prevalence".
#' @param phylogeny_title -- String (Optional) of Legend phylogeny title. Default is "Clonal Phylogeny".
#' @param alpha -- Number (Optional) of Alpha value for sweeps, range [0, 100].
#' @param genotype_position -- String (Optional) of How to position the genotypes from ["centre", "stack", "space"]
#' "centre" -- genotypes are centred with respect to their ancestors
#' "stack" -- genotypes are stacked such that no genotype is split at any time point
#' "space" -- genotypes are stacked but with a bit of spacing at the bottom
#' @param perturbations -- data frame (Optional) of any perturbations that occurred between two time points.
#' Format: columns are (1) character() "pert_name" - the perturbation name
#' (2) character() "prev_tp" - the time point (as labelled in clonal prevalence data)
#' BEFORE perturbation.
#' @param sort -- Boolean (Optional) of whether (TRUE) or not (FALSE) to vertically sort the genotypes by their emergence values (descending).
#' Default is FALSE.
#' Note that genotype sorting will always retain the phylogenetic hierarchy, and this parameter will only affect the ordering of siblings.
#' @param show_warnings -- Boolean (Optional) of Whether or not to show any warnings. Default is TRUE.
#' @param width -- Number (Optional) of width of the plot. Minimum width is 450.
#' @param height -- Number (Optional) of height of the plot. Minimum height with and without mutations is 500 and 260, respectively.
#' @export
#' @rdname helpers
#' @return Returns the ready list of user input data for htmlwidget
processUserData <- function(clonal_prev,
tree_edges,
mutations,
clone_colours,
xaxis_title,
yaxis_title,
phylogeny_title,
alpha,
genotype_position,
perturbations,
sort,
show_warnings,
width,
height) {
# ENSURE MINIMUM DIMENSIONS SATISFIED
checkMinDims(mutations, height, width)
# CHECK REQUIRED INPUTS ARE PRESENT
checkRequiredInputs(clonal_prev, tree_edges)
# ALPHA VALUE
checkAlpha(alpha)
# SORTED GENOTYPES
if (!is.logical(sort)) {
stop("Sort parameter must be a boolean.")
}
# CLONAL PREVALENCE DATA
clonal_prev <- checkClonalPrev(clonal_prev)
# TREE EDGES DATA
tree_edges <- checkTreeEdges(tree_edges)
# GENOTYPE POSITIONING
checkGtypePositioning(genotype_position)
# CHECK CLONE COLOURS
checkCloneColours(clone_colours)
# CHECK PERTURBATIONS
perturbations <- checkPerts(perturbations)
# MUTATIONS DATA
mut_data <- getMutationsData(mutations, tree_edges, clonal_prev)
mutation_info <- mut_data$mutation_info
mutation_prevalences <- mut_data$mutation_prevalences
if (is.data.frame(mutations)) {
mutations_provided <- TRUE
}
else {
mutations_provided <- FALSE
}
# REPLACE SPACES WITH UNDERSCORES
spaces_replaced <- replaceSpaces(clonal_prev, tree_edges, clone_colours, mutation_info, mutations, mutation_prevalences)
timepoint_map <- spaces_replaced$timepoint_map
clone_id_map <- spaces_replaced$clone_id_map
clonal_prev <- spaces_replaced$clonal_prev
tree_edges <- spaces_replaced$tree_edges
mutation_info <- spaces_replaced$mutation_info
clone_colours <- spaces_replaced$clone_colours
mutation_prevalences <- spaces_replaced$mutation_prevalences
# forward options using x
return(list(
clonal_prev = jsonlite::toJSON(clonal_prev),
gtype_tree_edges = jsonlite::toJSON(tree_edges),
clone_cols = jsonlite::toJSON(clone_colours),
mutations = jsonlite::toJSON(mutation_info),
mutation_prevalences = jsonlite::toJSON(mutation_prevalences),
mutations_provided=mutations_provided, # whether or not mutations are provided
xaxis_title = as.character(xaxis_title),
yaxis_title = as.character(yaxis_title),
phylogeny_title = as.character(phylogeny_title),
alpha = alpha,
genotype_position = genotype_position,
perturbations = jsonlite::toJSON(perturbations),
sort_gtypes = sort,
timepoint_map = jsonlite::toJSON(timepoint_map),
clone_id_map = jsonlite::toJSON(clone_id_map)
))
}
#' Function to check minimum dimensions
#'
#' @param mutations -- mutations provided by user
#' @param height -- height provided by user
#' @param width -- width provided by user
#' @examples
#' checkMinDims(data.frame(chr = c("11"), coord = c(104043), VAF = c(0.1)), "700px", "700px")
#' @export
#' @rdname helpers
#' @return None
checkMinDims <- function(mutations, height, width) {
# set height if not set by user
if (is.null(height)) {
if (!is.data.frame(mutations)) { # no mutations
height = 260
}
else { # mutations
height = 500
}
}
# check height is big enough
min_width = 450
if (!is.data.frame(mutations)) { # no mutations
min_height = 260
}
else { # mutations
min_height = 500
}
if (height < min_height) {
stop("Height must be greater than or equal to ", min_height, "px.")
}
if (width < min_width) {
stop("Width must be greater than or equal to ", min_width, "px.")
}
}
#' Function to check required inputs are present
#'
#' @param clonal_prev -- clonal_prev provided by user
#' @param tree_edges -- tree_edges provided by user
#' @examples
#' checkRequiredInputs(data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.1","0.22","0.08","0.53","0.009","0.061","1")),
#' data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")))
#' checkRequiredInputs(data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.12","0.12","0.18","0.13","0.009","0.061","1")),
#' data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")))
#' @export
#' @rdname helpers
#' @return None
checkRequiredInputs <- function(clonal_prev, tree_edges) {
if (missing(clonal_prev)) {
stop("Clonal prevalence data frame must be provided.")
}
if (missing(tree_edges)) {
stop("Tree edge data frame must be provided.")
}
}
#' check alpha value input is correct
#'
#' @param alpha -- alpha provided by user
#' @examples
#' checkAlpha(4)
#' checkAlpha(100)
#' @export
#' @rdname helpers
#' @return None
checkAlpha <- function(alpha) {
if (!is.numeric(alpha)) {
stop("Alpha value must be numeric.")
}
if (alpha < 0 || alpha > 100) {
stop("Alpha value must be between 0 and 100.")
}
}
#' check clonal_prev parameter data
#'
#' @param clonal_prev -- clonal prevalence provided by user
#' @examples
#' checkClonalPrev(data.frame(timepoint=c(1), clone_id=c(2), clonal_prev=c(0.1)))
#' @export
#' @rdname helpers
#' @return Clonal prevalence data after checkint it for column names and content types
checkClonalPrev <- function(clonal_prev) {
# ensure column names are correct
if (!("timepoint" %in% colnames(clonal_prev)) ||
!("clone_id" %in% colnames(clonal_prev)) ||
!("clonal_prev" %in% colnames(clonal_prev))) {
stop("Clonal prevalence data frame must have the following column names: ",
"\"timepoint\", \"clone_id\", \"clonal_prev\"")
}
# ensure data is of the correct type
clonal_prev$timepoint <- as.character(clonal_prev$timepoint)
clonal_prev$clone_id <- as.character(clonal_prev$clone_id)
clonal_prev$clonal_prev <- as.numeric(as.character(clonal_prev$clonal_prev))
return(clonal_prev)
}
#' check tree_edges parameter data
#'
#' @param tree_edges -- tree edges provided by user
#' @examples
#' checkTreeEdges(data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")))
#' @export
#' @rdname helpers
#' @return Tree edges data after checkint it for column names and content types
checkTreeEdges <- function(tree_edges) {
# ensure column names are correct
if (!("source" %in% colnames(tree_edges)) ||
!("target" %in% colnames(tree_edges))) {
stop("Tree edges data frame must have the following column names: ",
"\"source\", \"target\"")
}
# ensure data is of the correct type
tree_edges$source <- as.character(tree_edges$source)
tree_edges$target <- as.character(tree_edges$target)
# check for tree rootedness
sources <- unique(tree_edges$source)
targets <- unique(tree_edges$target)
sources_for_iteration <- sources # because we will be changing the sources array over time
for (i in 1:length(sources_for_iteration)) {
cur_source <- sources_for_iteration[i]
# if the source is a target, remove it from the sources list
if (cur_source %in% targets) {
sources <- sources[sources != cur_source]
}
}
# if multiple roots are detected, throw error
if (length(sources) > 1) {
stop("Multiple roots detected in tree (",paste(sources,collapse=", "),
") - tree must have only one root.")
}
# if an edge is found whose source and target are equal, throw an error
if (length(which(as.character(tree_edges$source) == as.character(tree_edges$target))) > 0) {
stop("One of the tree edges has a source as its own target. Remove this edge.")
}
return(tree_edges)
}
#' check genotype_position parameter
#'
#' @param genotype_position -- genotype_position provided by user
#' @examples
#' checkGtypePositioning("centre")
#' @export
#' @rdname helpers
#' @return None
checkGtypePositioning <- function(genotype_position) {
if (!(genotype_position %in% c("stack", "centre", "space"))) {
stop("Genotype position must be one of c(\"stack\", \"centre\", \"space\")")
}
}
#' check clone_colours parameter
#'
#' @param clone_colours -- clone_colours provided by user
#' @examples
#' checkCloneColours(data.frame(clone_id = c("1","2","3", "4"), colour = c("#beaed4", "#fdc086", "#beaed4", "#beaed4")))
#' @export
#' @rdname helpers
#' @return None
checkCloneColours <- function(clone_colours) {
if (is.data.frame(clone_colours)) {
# ensure column names are correct
if (!("clone_id" %in% colnames(clone_colours)) ||
!("colour" %in% colnames(clone_colours))) {
stop("Node colour data frame must have the following column names: ",
"\"clone_id\", \"colour\"")
}
}
}
#' check perturbations parameter
#'
#' @param perturbations -- perturbations provided by user
#' @examples
#' checkPerts(data.frame(pert_name = c("New Drug"), prev_tp = c("Diagnosis")))
#' @export
#' @rdname helpers
#' @return Perturbations after checking them for content types and column names
checkPerts <- function(perturbations) {
if (is.data.frame(perturbations)) {
# ensure column names are correct
if (!("pert_name" %in% colnames(perturbations)) ||
!("prev_tp" %in% colnames(perturbations))) {
stop("Perturbations data frame must have the following column names: ",
"\"pert_name\", \"prev_tp\"")
}
# check that columns are of the correct type
perturbations$pert_name <- as.character(perturbations$pert_name)
perturbations$prev_tp <- as.character(perturbations$prev_tp)
}
return(perturbations)
}
#' get mutation data
#'
#' @param mutations -- mutations data from user
#' @param tree_edges -- tree edges data from user
#' @param clonal_prev -- clonal prevalence data from user
#' @examples
#' getMutationsData(data.frame(chrom = c("11"), coord = c(104043), VAF = c(0.1), clone_id=c(1), timepoint=c("Relapse")),
#' data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")),
#' data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.12","0.12","0.18","0.13","0.009","0.061","1")))
#' @export
#' @rdname helpers
#' @return List of mutation information and mutation prevalences
getMutationsData <- function(mutations, tree_edges, clonal_prev) {
if (is.data.frame(mutations)) {
# ensure column names are correct
if (!("chrom" %in% colnames(mutations)) ||
!("coord" %in% colnames(mutations)) ||
!("clone_id" %in% colnames(mutations)) ||
!("timepoint" %in% colnames(mutations)) ||
!("VAF" %in% colnames(mutations))) {
stop("Mutations data frame must have the following column names: ",
"\"chrom\", \"coord\", \"clone_id\", \"timepoint\", \"VAF\".")
}
# ensure data is of the correct type
mutations$chrom <- toupper(as.character(mutations$chrom)) # upper case X & Y
mutations$coord <- as.character(mutations$coord)
mutations$timepoint <- as.character(mutations$timepoint)
mutations$clone_id <- as.character(mutations$clone_id)
mutations$VAF <- as.numeric(as.character(mutations$VAF))
# check for optional info, and ensure data of correct type
extra_columns <- colnames(mutations)[which(!(colnames(mutations) %in% c("chrom", "coord", "clone_id", "timepoint", "VAF")))]
mutations <- data.frame(lapply(mutations, as.character), stringsAsFactors=FALSE)
# check that all CLONE IDS in the mutations data are present in the tree data
mutations_clone_ids <- unique(mutations$clone_id)
tree_edges_clone_ids <- c(unique(tree_edges$source), unique(tree_edges$target))
clone_ids_missing_from_tree_edges_data <- setdiff(mutations_clone_ids, tree_edges_clone_ids)
if (length(clone_ids_missing_from_tree_edges_data) > 0) {
stop("The following clone ID(s) are present in the mutations data but ",
"are missing from the tree edges data: ",
paste(clone_ids_missing_from_tree_edges_data, collapse=", "), ".")
}
# check that all TIMEPOINTS in the mutations data are present in the clonal prev data
mutations_tps <- unique(mutations$timepoint)
clonal_prev_tps <- unique(clonal_prev$timepoint)
tps_missing_from_clonal_prev_data <- setdiff(mutations_tps, clonal_prev_tps)
if (length(tps_missing_from_clonal_prev_data) > 0) {
stop("The following timepoint(s) are present in the mutations data but ",
"are missing from the clonal prevalence data: ",
paste(tps_missing_from_clonal_prev_data, collapse=", "), ".")
}
# create a location column, combining the chromosome and the coordinate
mutations$location <- apply(mutations[, c("chrom","coord")], 1 , paste, collapse = ":")
# coordinate is now a number
mutations$coord <- as.numeric(as.character(mutations$coord))
# check X & Y chromosomes are labelled "X" and "Y", not "23", "24"
num_23 <- mutations[which(mutations$chrom == "23"),]
if (nrow(num_23) > 0) {
stop("Chromosome numbered \"23\" was detected in mutations data frame - X and Y chromosomes ",
"must be labelled \"X\" and \"Y\".")
}
# get list of clones in the phylogeny
clones_in_phylo <- unique(c(tree_edges$source, tree_edges$target))
# keep only those mutations whose clone ids are present in the phylogeny
mutations <- mutations[which(mutations$clone_id %in% clones_in_phylo),]
# MUTATION PREVALENCES DATA
mutation_prevalences <- mutations
# keep only those mutations whose clone ids are present in the phylogeny
mutation_prevalences <- mutation_prevalences[which(mutation_prevalences$clone_id %in% clones_in_phylo),]
# warn if more than 10,000 rows in data that the visualization may be slow
if (nrow(mutation_prevalences) > 10000 && show_warnings) {
print(paste("[WARNING] Number of rows in mutations data exceeds 10,000. ",
"Resultantly, visualization may be slow. ",
"It is recommended to filter the data to a smaller set of mutations.", sep=""))
}
# compress results
prevs_split <- split(mutation_prevalences, f = mutation_prevalences$location)
# reduce the size of the data frame in each list
prevs_split_small <- lapply(prevs_split, function(prevs) {
return(prevs[,c("timepoint", "VAF")])
})
# MUTATION INFO
mutation_info <- unique(mutations[,c("chrom","coord","clone_id",extra_columns)])
}
else {
prevs_split_small <- "NA"
mutation_info <- "NA"
}
return(list("mutation_info"=mutation_info, "mutation_prevalences"=prevs_split_small))
}
#' function to replace spaces with underscores in all data frames & keep maps of original names to space-replaced names
#' @param clonal_prev -- clonal_prev data from user
#' @param tree_edges -- tree edges data from user
#' @param clone_colours -- clone_colours data from user
#' @param mutation_info -- processed mutation_info
#' @param mutations -- mutations data from user
#' @param mutation_prevalences -- mutation_prevalences data from user
#' @export
#' @rdname helpers
#' @examples
#' replaceSpaces(mutations = data.frame(chrom = c("11"), coord = c(104043), VAF = c(0.1), clone_id=c(1), timepoint=c("Relapse")),
#' tree_edges = data.frame(source = c("1","1","2","2","5","6"), target=c("2","5","3","4","6","7")),
#' clonal_prev = data.frame(timepoint = c(rep("Diagnosis", 6), rep("Relapse", 1)), clone_id = c("1","2","3","4","5","6","7"), clonal_prev = c("0.12","0.12","0.18","0.13","0.009","0.061","1")),
#' mutation_prevalences = list("X:6154028" = data.frame(timepoint = c("Diagnosis"), VAF = c(0.5557))), mutation_info=data.frame(clone_id=c(1)),
#' clone_colours = data.frame(clone_id = c("1","2","3", "4"), colour = c("#beaed4", "#fdc086", "#beaed4", "#beaed4")))
#' @return List of data frames with spaces replaced
replaceSpaces <- function(clonal_prev, tree_edges, clone_colours, mutation_info, mutations, mutation_prevalences) {
# create map of original sample ids to space-replaced sample ids
timepoint_map <- data.frame(original_timepoint = unique(clonal_prev$timepoint), stringsAsFactors=FALSE)
timepoint_map$space_replaced_timepoint <- stringr::str_replace_all(timepoint_map$original_timepoint,"\\s+","_")
# create map of original clone ids to space-replaced clone ids
clone_id_map <- data.frame(original_clone_id = unique(c(tree_edges$source, tree_edges$target)), stringsAsFactors=FALSE)
clone_id_map$space_replaced_clone_id <- stringr::str_replace_all(clone_id_map$original_clone_id,"\\s+","_")
# replace spaces with underscores
# --> timepoints
clonal_prev$timepoint <- stringr::str_replace_all(clonal_prev$timepoint,"\\s+","_")
if (is.data.frame(mutations)) {
mutation_prevalences <- lapply(mutation_prevalences, function(prevs) {
prevs$timepoint <- stringr::str_replace_all(prevs$timepoint,"\\s+","_")
return(prevs)
})
}
# --> clone ids
clonal_prev$clone_id <- stringr::str_replace_all(clonal_prev$clone_id,"\\s+","_")
tree_edges$source <- stringr::str_replace_all(tree_edges$source,"\\s+","_")
tree_edges$target <- stringr::str_replace_all(tree_edges$target,"\\s+","_")
if (is.data.frame(clone_colours)) {
clone_colours$clone_id <- stringr::str_replace_all(clone_colours$clone_id,"\\s+","_")
}
if (is.data.frame(mutations)) {
mutation_info$clone_id <- stringr::str_replace_all(mutation_info$clone_id,"\\s+","_")
}
return(list("timepoint_map"=timepoint_map,
"clone_id_map"=clone_id_map,
"clonal_prev"=clonal_prev,
"tree_edges"=tree_edges,
"mutation_info"=mutation_info,
"clone_colours"=clone_colours,
"mutation_prevalences"=mutation_prevalences))
}
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