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
transformList	In case the data from the fcs files still needs to be transformed. Default NULL, in which case no transformation happens.
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 = 101),
                        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 March 19, 2024, 6:43 p.m.