extract_pctgs_msi_per_flowsom: #' Extracts percentages and MSI for cell populations #' #'...

In prybakowska/CytoQP: Cytometry data quality control and cleaninig

#' Extracts percentages and MSI for cell populations #' #' @description Performs FlowSOM clustering and extracts cluster and metacluster #' frequency and MSI. It is imputing 0 values when NAs are detected in MSI for #' clusters and metaclusters. #' #' @param file_list List, pathway to the files before and after normalization #' @param nCells Numeric, number of cells to be cluster per each file, #' default is set to 50 000. #' @param phenotyping_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. #' @param functional_markers Character vector, marker names to be used for #' functional markers, can be full marker name #' e.g. "IL-6" or "IL" if all IL-markers needs to be plotted. #' @param xdim Numeric, parameter to pass to FlowSOM, width of the SOM grid, #' default is set to 10. #' @param ydim Numeric, parameter to pass to FlowSOM, geight of the SOM grid, #' default is set to 10. #' @param n_metaclusters Numeric, exact number of clusters for metaclustering #' in FlowSOM, default is set to 35. #' @param out_dir Character, pathway to where the FlowSOM clustering plot should #' be saved, default is set to file.path(getwd(), "CytoNormed"). #' @param seed Numeric, set to obtain reproducible results, default is set to NULL. #' @param arcsine_transform arcsine_transform Logical, if the data should #' be transformed with arcsine transformation and cofactor 5, default is set to TRUE #' @param save_matrix Logical, if the results should be saved, if TRUE (default) #' list of matrices will be saved in out_dir. #' @param 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.Default set to NULL. #' @param save_flowsom_result Logical, if FlowSOM and FlowSOM plots should be #' saved. If TRUE (default) files will be saved in out_dir. #' @param impute_0_values Logical, if 0 values should be imputed for MSI values #' if NAs are present. #' #' @import ggplot2 #' #' @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), "day0-9*"),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) #' #' @return list of matrices that contain calculation for #' cl_pctgs (cluster percentages), mcl_pctgs (metaclusters percentages), #' cl_msi (cluster MSIs for selected markers), mcl_msi (metaclusters MSI #' for selected markers). If save_matrix = TRUE, saves this matrices in out_dir. #' FlowSOM objects for normalized and unnormalized data, #' if save_flowsom_result set to TRUE. #' #' @export extract_pctgs_msi_per_flowsom <- function(files_before_norm, files_after_norm, nCells = 50000, phenotyping_markers = c("CD", "HLA", "IgD"), functional_markers = NULL, xdim = 10, ydim = 10, n_metaclusters = 35, out_dir = NULL, seed = NULL, arcsine_transform = TRUE, save_matrix = TRUE, transform_list = NULL, save_flowsom_result = TRUE, impute_0_values = TRUE) extract_pctgs_msi_per_flowsom( files, nCells = 50000, phenotyping_markers = c("CD", "HLA", "IgD"), functional_markers = NULL, xdim = 10, ydim = 10, n_metaclusters = 35, out_dir = NULL, seed = NULL, arcsine_transform = TRUE, save_matrix = TRUE, file_name = NULL, transform_list = NULL, save_flowsom_result = TRUE, impute_0_values = TRUE ) \itemfilesPathway to the files \itemnCellsNumeric, number of cells to be cluster per each file, default is set to 50 000. \itemphenotyping_markersCharacter 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. \itemfunctional_markersCharacter vector, marker names to be used for functional markers, can be full marker name e.g. "IL-6" or "IL" if all IL-markers needs to be plotted. \itemxdimNumeric, parameter to pass to FlowSOM, width of the SOM grid, default is set to 10. \itemydimNumeric, parameter to pass to FlowSOM, geight of the SOM grid, default is set to 10. \itemn_metaclustersNumeric, exact number of clusters for metaclustering in FlowSOM, default is set to 35. \itemout_dirCharacter, pathway to where the FlowSOM clustering plot should be saved, default is set to file.path(getwd(), "CytoNormed"). \itemseedNumeric, set to obtain reproducible results, default is set to NULL. \itemarcsine_transformarcsine_transform Logical, if the data should be transformed with arcsine transformation and cofactor 5, default is set to TRUE \itemsave_matrixLogical, if the results should be saved, if TRUE (default) matrice will be saved in out_dir. \itemfile_nameThe prefix/name to use for saving the either flowsom result or frequency matrix, only if save_matrix or save_flowsom_result set to TRUE. deafult NULL. \itemtransform_listTransformation 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.Default set to NULL. \itemsave_flowsom_resultLogical, if FlowSOM and FlowSOM plots should be saved. If TRUE (default) files will be saved in out_dir. \itemimpute_0_valuesLogical, if 0 values should be imputed for MSI values if NAs are present. matrix that contain calculation for cl_pctgs (cluster percentages), mcl_pctgs (metaclusters percentages), cl_msi (cluster MSIs for selected markers), mcl_msi (metaclusters MSI for selected markers). If save_matrix = TRUE, saves matrix in out_dir. FlowSOM objects for normalized and unnormalized data, if save_flowsom_result set to TRUE. Performs FlowSOM clustering and extracts cluster and metacluster frequency and MSI. It is imputing 0 values when NAs are detected in MSI for clusters and metaclusters. # Define files before normalization gate_dir <- file.path(getwd(), "Gated") files_before_norm <- list.files(gate_dir,pattern = ".fcs", full.names = TRUE) # Define files after normalization norm_dir <- file.path(getwd(), "CytoNormed") files_after_norm <- list.files(norm_dir, pattern = ".fcs", full.names = TRUE) # 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] # Extract cell frequency and MSI files_list <- list("after" = files_after_norm, "before" = files_before_norm) results <- list() for (name in names(files_list)) files <- files_list[[name]] mx <- extract_pctgs_msi_per_flowsom(files = files, 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, file_name = name, seed = 343, impute_0_values = TRUE) results[[name]] <- mx

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