prepare_data_for_plotting: Prepares data for plotting cell frequency and MSI

In prybakowska/CytoQP: Cytometry data quality control and cleaninig

Prepares data for plotting cell frequency and MSI

Description

Performs dimensional reduction and constructs data frame for plotting cell frequencies and MSI per clusters and metaclusters obtained from extract_pctgs_msi_per_flowsom function. For the MSI it is taking the MSI > 1.

Usage

prepare_data_for_plotting(
  frequency_msi_list,
  matrix_type,
  n_neighbours = 15,
  seed = NULL
)

Arguments

frequency_msi_list	List containing matrices with cell frequency and msi obtained in step extract_pctgs_msi_per_flowsom.
matrix_type	The name of the matrix to be plotted.
n_neighbours	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.
seed	Numeric set to obtain reproducible results, default NULL.

Value

data frame for plotting

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]

mx <- extract_pctgs_msi_per_flowsom(files_after_norm = files_after_norm,
                                    files_before_norm = files_before_norm,
                                    nCells = 50000,
                                    phenotyping_markers = c("CD", "HLA", "IgD"),
                                    functional_markers = c("MIP", "MCP", "IL",
                                                           "IFNa", "TNF", "TGF",
                                                           "Gr"),
                                    xdim = 10,
                                    ydim = 10,
                                    n_metaclusters = 35,
                                    out_dir = norm_dir,
                                    arcsine_transform = TRUE,
                                    save_matrix = TRUE,
                                    seed = 343)
# create the list to store the plots
 plots <- list()
 for (name in names(mx[[1]])){
 df_plot <- prepare_data_for_plotting(frequency_msi_list = mx,
                                       matrix_type = name,
                                      n_neighbours = 11, seed = 1)


 batch <- stringr::str_match(rownames(df_plot), "day[0-9]*")[,1]
 samples_id <- ifelse(grepl("p1", rownames(df_plot)),"p1",
                      ifelse(grepl("p2", rownames(df_plot)), "p2", "ref"))
 stimulation <- stringr::str_match(rownames(df_plot), "UNS|RSQ|IMQ|LPS|CPG")[,1]

 plots[[name]] <- plot_batch_using_freq_msi(df_plot = df_plot, fill = batch,
                                            shape = samples_id, color = batch,
                                            split_by_normalization = TRUE, title = name)

}

gg_a <- grid_arrange_common_legend(plot_lists = plots, nrow = 2, ncol = 2,
                                  position = "right")

ggplot2::ggsave(filename ="batch_effect_frequency_MSI.png",
               device = "png",
              path = norm_dir,
               plot = gg_a,
               units = "cm",
               width = 22,
               height = 14, dpi = 300)

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