plotDensities: Plot densities per batch

In saeyslab/CytoNorm: Normalisation of cytometry data measured across multiple batches

Plot densities per batch

Description

Plot densities per batch

Usage

plotDensities(
  input,
  channels,
  colors = NULL,
  model = NULL,
  transformList = NULL,
  show_goal = FALSE,
  suffix = c(original = "", normalized = "_norm")
)

Arguments

input	Named list containing paths to fcs files, for both batch and batch_norm. For a batch, the input can be either one or more paths to fcs files or a flowFrame.
channels	Channels to plot
colors	Optional vector with a color for each batch
model	CytoNorm model to split out the normalized density plots per FlowSOM meta-cluster. Optional.
transformList	In case the data from the fcs files still needs to be transformed. Default NULL, in which case no transformation happens.
show_goal	If a 'model' is provided, it is possible to show the model's goal distribution.
suffix	Suffixes used to distinguish the original from the normalized files. Default is c("original" = "", "normalized" = "_norm")

Value

List with 2 plots per channel

Examples

dir <- system.file("extdata", package = "CytoNorm")
files <- list.files(dir, pattern = "fcs$")
data <- data.frame(File = files,
                   Path = file.path(dir, files),
                   Type = stringr::str_match(files, "_([12]).fcs")[,2],
                   Batch = stringr::str_match(files, "PTLG[0-9]*")[,1],
                   stringsAsFactors = FALSE)
data$Type <- c("1" = "Train", "2" = "Validation")[data$Type]
train_data <- dplyr::filter(data, Type == "Train")
validation_data <- dplyr::filter(data, Type == "Validation")

ff <- flowCore::read.FCS(data$Path[1])
channels <- grep("Di$", flowCore::colnames(ff), value = TRUE)
transformList <- flowCore::transformList(channels,
                                         cytofTransform)
transformList.reverse <- flowCore::transformList(channels,
                                                 cytofTransform.reverse)

model <- CytoNorm.train(files = train_data$Path,
                        labels = train_data$Batch,
                        channels = channels,
                        transformList = transformList,
                        FlowSOM.params = list(nCells = 10000, #1000000
                                              xdim = 15,
                                              ydim = 15,
                                              nClus = 10,
                                              scale = FALSE),
                        normParams = list(nQ = 99),
                        seed = 1,
                        verbose = TRUE)

CytoNorm.normalize(model = model,
                   files = validation_data$Path,
                   labels = validation_data$Batch,
                   transformList = transformList,
                   transformList.reverse = transformList.reverse,
                   outputDir = "Normalized",
                   verbose = TRUE)

original <- list("PTLG021" = validation_data$Path[1],
                 "PTLG028" = validation_data$Path[2],
                 "PTLG034" = validation_data$Path[3])
normalized <- list("PTLG021_norm" = paste0("Normalized/Norm_",validation_data$File[1]),
                   "PTLG028_norm" = paste0("Normalized/Norm_",validation_data$File[2]),
                   "PTLG034_norm" = paste0("Normalized/Norm_",validation_data$File[3]))

channels_to_plot <- c("Er170Di", "La139Di")
plots <- plotDensities(input = c(original, normalized),
                       model = model,
                       channels = channels_to_plot,
                       colors = c("blue", "red", "green"),
                       transformList = transformList)

p <- ggpubr::ggarrange(ggpubr::ggarrange(plotlist = plots[1:(length(plots)-1)],
                                         ncol = 2,
                                         nrow = 2*length(channels_to_plot)),
                       ggpubr::ggarrange(ggpubr::as_ggplot(plots[[length(plots)]])),
                       ncol = 1, nrow = 2, heights = c(10,1))

saeyslab/CytoNorm documentation built on Nov. 2, 2024, 12:39 p.m.