plot_batch: Visualize batch using umap dimensional reduction

In prybakowska/CytoQP: Cytometry data quality control and cleaninig

Visualize batch using umap dimensional reduction

Description

Plots batch effect using UMAP and clustering markers

Usage

plot_batch(
  files_before_norm,
  files_after_norm,
  batch_labels = NULL,
  batch_pattern = NULL,
  cores = 1,
  out_dir = NULL,
  clustering_markers = "CD|HLA|IgD|PD|BAFF|TCR",
  arcsine_transform = TRUE,
  manual_colors = NULL,
  cells_total = 1000,
  transform_list = NULL,
  n_neighbors = length(files_before_norm)
)

Arguments

files_before_norm	Character, full path to the unnormalized fcs_files.
files_after_norm	Character, full path to the normalized fcs_files.
batch_pattern	Character, batch pattern to be match in the fcs file name.
cores	Number of cores to be used
out_dir	Character, pathway to where the files should be saved, if NULL (default) files will be saved to file.path(getwd(), CytoNormed).
clustering_markers	Character vector, marker names to be used for clustering, can be full marker name e.g. "CD45" or "CD" if all CD-markers needs to be plotted. These markers are used for building and plotting UMAP.
arcsine_transform	Logical, if the data should be transformed with arcsine transformation and cofactor 5, default is set to TRUE.
manual_colors	Character, vector of the colors to be used, the number of colors needs to be equal to the length of batch_pattern.
cells_total	Number of cells to plot per each file.
transform_list	Transformation list to pass to the flowCore transform function, see flowCore::transformList(), if different transformation than arcsine is needed. Only if arcsine_transform is FALSE. If NULL and arcsine_transform = FALSE no transformation will be applied.
n_neighbors	The size of local neighborhood in UMAP analysis, default set to 15, as in uwot::umap(). It is recommended to set it to the number of files in each batch.

Value

save plots for batch effect in the out_dir

Examples

# Define files before normalization
gate_dir <- file.path(dir, "Gated")
files_before_norm <- list.files(gate_dir,
                                pattern = ".fcs",
                                full.names = T)

# Define files after normalization
norm_dir <- file.path(dir, "CytoNormed")
files_after_norm <- list.files(norm_dir,
                               pattern = ".fcs",
                               full.names = T)

# files needs to be in the same order, check and order if needed
test_match_order(x = basename(gsub("Norm_","",files_after_norm)),
                 basename(files_before_norm))

batch_labels <- stringr::str_match(basename(files_before_norm), "day[0-9]*")[,1]

# Plot batch effect
set.seed(789)
plot_batch(files_before_norm = files_before_norm,
           files_after_norm = files_after_norm,
           batch_labels = batch_labels,
           cores = 1,
           out_dir = norm_dir,
           clustering_markers = c("CD", "IgD", "HLA"),
           manual_colors = c("darkorchid4", "darkorange", "chartreuse4"))

prybakowska/CytoQP documentation built on June 28, 2022, 12:36 a.m.