plotRidgelines: Plot ridgeline plots

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

Plot ridgeline plots

Description

Plot ridgeline plots

Usage

plotRidgelines(
  input,
  batch = NULL,
  channels,
  colors = NULL,
  transformList = NULL,
  quantiles = c(seq(0, 1, 0.1))
)

Arguments

input	Paths to fcs files.
batch	Optional vector with a batch label for each file
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.
quantiles	Optional vector specifying the quantiles that need to be highlighted and connected.

Value

Named list with a plot per channel

Examples

dir <- system.file("extdata", package = "CytoNorm")
files <- list.files(dir, pattern = "fcs$")
ff <- flowCore::read.FCS(file.path(dir, files[1]))
channels <- grep("Di$", flowCore::colnames(ff), value = TRUE)
transformList <- flowCore::transformList(channels,
                                         cytofTransform)
beforeNorm <- plotRidgelines(input = file.path(dir, files), 
                             batch = stringr::str_match(files, "PTLG[0-9]*")[,1],
                             channels = c("Er170Di", "La139Di"),
                             colors = c("PTLG021" = "#d8e2dc", 
                                        "PTLG028" = "#ffe5d9",
                                        "PTLG034" = "#ffcad4"),
                             transformList = transformList,
                             quantiles = c(0.01, 0.25, 0.5, 0.75, 0.99))
                             
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.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)

files <- list.files("Normalized", pattern = "fcs$")
afterNorm <- plotRidgelines(input = file.path("Normalized", files), 
                            batch = stringr::str_match(files, "PTLG[0-9]*")[,1],
                            channels = c("Er170Di", "La139Di"),
                            colors = c("PTLG021" = "#d8e2dc", 
                                       "PTLG028" = "#ffe5d9",
                                       "PTLG034" = "#ffcad4"),
                            transformList = transformList,
                            quantiles = c(0.01, 0.25, 0.5, 0.75, 0.99))

p <- list()
for (i in 1:length(beforeNorm)){
  p[[length(p)+1]] <- ggpubr::ggarrange(beforeNorm[[i]], afterNorm[[i]], nrow =1)
}

saeyslab/CytoNorm documentation built on June 15, 2025, 11:40 a.m.