R/cytofkit.R

In haoeric/cytofkit_devel: cytofkit: an integrated mass cytometry data analysis pipeline

#' cytofkit: an integrated mass cytometry data analysis pipeline
#' 
#' This package is designed to facilitate the analysis workflow of mass cytometry data with 
#' automatic subset identification and mapping of cellular progression. Both command line and 
#' a GUI client are provided for executing the workflow easily.
#' 
#' This package integrates merging methods of multiple FCS files, dimension reduction methods (PCA, t-SNE and ISOMAP) 
#' and clustering methods (DensVM, ClusterX, and Rphenograph) for rapid subset detection. Analysis reuslts can be visualized 
#' and explored interactively using a specially designed shiny web APP, see \code{\link{cytofkitShinyAPP}}. Moreover, the method isomap is provided to map the cellular progression. 
#' This workflow can be easily executed with the main function \code{\link{cytofkit}} or through the GUI client \code{\link{cytofkit_GUI}}.
#' 
#' Pre-processing
#' 
#' Using function \code{\link{cytof_exprsMerge}}, one or multiple FCS files will be loaded via the *read.FCS* 
#' function in the *flowCore* package. Then transformation was applied to the expression value 
#' of selected markers of each FCS file. Transformation methods include \code{autoLgcl}, \code{cytofAsinh}, 
#' \code{logicle} and \code{arcsinh}, where \code{cytofAsinh} is the default.Then mutilple FCS files are 
#' merged using one of the merging methods \code{all}, \code{min}, \code{fixed} or \code{ceil}.
#' 
#' Dimensionality reduction
#' 
#' Using function \code{\link{cytof_dimReduction}}, t-Distributed Stochastic Neighbor Embedding (\code{tsne}) 
#' is suggested for dimensionality reduction although we also provide methods like \code{isomap} and \code{pca}.
#' 
#' Cluster 
#' 
#' Using function \code{\link{cytof_cluster}}, three cluster method are provided, \code{DensVM}, \code{ClusterX} 
#' \code{Rphenograph} and \code{FlowSOM}. \code{DensVM}, \code{densityClustX} are performend on the dimension reduced data, while \code{Rphenograph}
#' works directed on the high dimensional expression data. Method \code{FlowSOM} is integrated from FlowSOM package 
#' (https://bioconductor.org/packages/release/bioc/html/FlowSOM.html). 
#' 
#' Post-processing
#' 
#'  - Using function \code{\link{cytof_clusterPlot}} to visualize the cluster results in a catter plot, in which dots represent cells, colours 
#' indicate their assigned clusters and point shapes represent their belonging samples.
#' 
#'  - Using function \code{\link{cytof_heatmap}} to generate heat map to visualize the mean expression of every marker in every cluster. 
#'  This heat maps is useful to interrogate marker expression to identify each cluster's defining markers. 
#' 
#'  - Using function \code{\link{cytof_progressionPlot}} to visualize the expression patter of selected markers against the estimated 
#'  cellular progression order.
#'  
#'  - Using function \code{\link{cytof_addToFCS}} to add any dimension reduced data, cluster results, progression data into the original FCS files,
#'  new FCS files will be saved for easy checking with other softwares like FlowJo. 
#' 
#' All the above post processing can be automatically implemented and saved using one function \code{\link{cytof_writeResults}}.
#' 
#' @author Hao Chen, Jinmiao Chen
#' @examples
#' 
#' ## Run on GUI
#' #cytofkit_GUI()  # remove the hash symbol to launch the GUI
#' 
#' ## Run on command
#' dir <- system.file('extdata',package='cytofkit')
#' file <- list.files(dir, pattern='.fcs$', full=TRUE)
#' parameters <- list.files(dir, pattern='.txt$', full=TRUE)
#' ## remove the hash symbol to run the following command
#' #cytofkit(fcsFile = file, markers = parameters)   
#' 
#' ## Checking the vignettes for more details 
#' if(interactive()) browseVignettes(package = 'cytofkit')
#' 
#' @seealso \code{\link{cytofkit}}, \code{\link{cytofkit_GUI}}
#' @references \url{http://signbioinfo.github.io/cytofkit/}
#' @docType package
#' @name cytofkit-package
#' 
NULL



#' cytofkit: an integrated mass cytometry data analysis pipeline
#' 
#' The main function to drive the cytofkit workflow.
#' 
#' \code{cytofkit} works as the main funciton to perform the analysis of one or multiple FCS files. 
#' The workflow contains data merging from multiple FCS file, expression data transformation, 
#' dimensionality reduction with \code{PCA}, \code{isomap} or \code{tsne} (default), clustering 
#' analysis with methods includes \code{DensVM}, \code{ClusterX}, \code{Rphenograph)} and \code{FlowSOM} for 
#' subpopulation detection, and estimation of cellular progression using \code{isomap}. The analysis 
#' results can be visualized using scatter plot, heatmap plot or progression plot. Dimension reduced 
#' data and cluster labels will be saved back to new copies of FCS files. By default the analysis 
#' results will be automatically saved under \code{resultDir} for further annotation. Moreover An 
#' interactive web application is provided for interactive exploration of the analysis results, 
#' see \code{cytofkitShinyAPP}.
#' 
#' 
#' @param fcsFiles It can be either the path where stores your FCS files or a vector of FCS file names. 
#' @param markers It can be either a text file where contains the makers to be used for analysis or a vector of the marker names.
#' @param projectName A prefix that will be added to the names of all result files.
#' @param ifCompensation Either boolean value tells if do compensation (compensation matrix contained in FCS), or a compensation matrix to be applied.
#' @param transformMethod Data Transformation method, including \code{autoLgcl}, \code{cytofAsinh}, \code{logicle} and \code{arcsinh}, or \code{none} to avoid transformation.
#' @param mergeMethod When multiple fcs files are selected, cells can be combined using 
#' one of the four different methods including \code{ceil}, \code{all}, \code{min}, \code{fixed}. 
#' The default option is \code{ceil}, up to a fixed number (specified by \code{fixedNum}) of cells are sampled 
#' without replacement from each fcs file and combined for analysis.
#' \code{all}: all cells from each fcs file are combined for analysis. 
#' \code{min}: The minimum number of cells among all the selected fcs files are sampled from each fcs file and combined for analysis. 
#' \code{fixed}: a fixed num (specified by fixedNum) of cells are sampled (with replacement when the total number of cell is less than 
#' fixedNum) from each fcs file and combined for analysis.
#' @param fixedNum The fixed number of cells to be extracted from each FCS file.
#' @param dimReductionMethod The method used for dimensionality reduction, including \code{tsne}, \code{pca} and \code{isomap}.
#' @param clusterMethods The clustering method(s) used for subpopulation detection, including \code{DensVM}, \code{ClusterX}, \code{Rphenograph} and \code{FlowSOM}. Multiple selection are accepted.
#' @param visualizationMethods The method(s) used for visualize the cluster data, including \code{tsne}, \code{pca} and \code{isomap}. Multiple selection are accepted.
#' @param progressionMethod Use the first ordination score of \code{isomap} to estimated the preogression order of cells, choose \code{NULL} to ignore.
#' @param FlowSOM_k Number of clusters for meta clustering in FlowSOM.
#' @param clusterSampleSize The uniform size of each cluster.
#' @param resultDir The directory where result files will be generated.
#' @param saveResults If save the results, and the post-processing results including scatter plot, heatmap, and statistical results.
#' @param saveObject Save the resutls into RData objects for loading back to R for further analysis
#' @param ... Other arguments passed to \code{cytof_exprsExtract}
#' 
#' @return a list containing \code{expressionData}, \code{dimReductionMethod}, \code{visualizationMethods}, \code{dimReducedRes}, \code{clusterRes}, \code{progressionRes}, \code{projectName}, \code{rawFCSdir} and \code{resultDir}. If choose 'saveResults = TRUE', results will be saved into files under \code{resultDir}.
#' @author Hao Chen, Jinmiao Chen
#' @references \url{http://signbioinfo.github.io/cytofkit/}
#' @seealso \code{\link{cytofkit}}, \code{\link{cytofkit_GUI}}, \code{\link{cytofkitShinyAPP}}
#' @useDynLib cytofkit
#' @export
#' @examples
#' dir <- system.file('extdata',package='cytofkit')
#' file <- list.files(dir, pattern='.fcs$', full=TRUE)
#' parameters <- list.files(dir, pattern='.txt$', full=TRUE)
#' ## remove the hash symbol to run the following command
#' #cytofkit(fcsFile = file, markers = parameters)   
cytofkit <- function(fcsFiles = getwd(), 
                     markers = "parameter.txt", 
                     projectName = "cytofkit", 
                     ifCompensation = FALSE, 
                     transformMethod = c("autoLgcl", "cytofAsinh", "logicle", "arcsinh", "none"), 
                     mergeMethod = c("ceil", "all", "min", "fixed"), 
                     fixedNum = 10000, 
                     dimReductionMethod = c("tsne", "pca", "isomap"), 
                     clusterMethods = c("Rphenograph", "ClusterX", "DensVM", "FlowSOM", "NULL"), 
                     visualizationMethods = c("tsne", "pca", "isomap", "NULL"), 
                     progressionMethod = c("NULL", "diffusionmap", "isomap"), 
                     FlowSOM_k = 40,
                     clusterSampleSize = 500,
                     resultDir = getwd(), 
                     saveResults = TRUE, 
                     saveObject = TRUE, ...) {
    
    ## arguments checking
    if (is.null(fcsFiles) || is.na(fcsFiles) || is.nan(fcsFiles)){
        stop("Wrong input fcsFiles!")
    }else if (length(fcsFiles) == 1 && file.info(fcsFiles)$isdir) {
        fcsFiles <- list.files(path = fcsFiles, pattern = ".fcs$", 
            full.names = TRUE)
        rawFCSdir <- fcsFiles
    }else{
        if(dirname(fcsFiles[1]) == "."){
            rawFCSdir <- getwd()
        }else{
            rawFCSdir <- dirname(fcsFiles[1])  
        }
    }
    setwd(rawFCSdir)
    if(length(fcsFiles) < 1)
        stop("No FCS file found, please select your fcsFiles!")
    if(!all(file.exists(fcsFiles)))
        stop("Can not find file(s):", fcsFiles[which(!file.exists(fcsFiles))])
    
    if (length(markers) == 1 && file.exists(markers)) {
        markers <- as.character(read.table(markers, sep = "\t", 
                                           header = TRUE)[, 1])
    }
    if (is.null(markers) || length(markers) < 1) 
        stop("no marker selected!")
  
    mergeMethod <- match.arg(mergeMethod)
    
    if (!is.null(fixedNum) && !(is.numeric(fixedNum))) 
        stop("clusterSampleSize must be a numeric number!")
    
    transformMethod <- match.arg(transformMethod)
    dimReductionMethod <- match.arg(dimReductionMethod) 
    
    if(missing(clusterMethods)){
        clusterMethods <- "Rphenograph"
    }else{
        clusterMethods <- match.arg(clusterMethods, several.ok = TRUE)
    }
    
    if(missing(visualizationMethods)){
        visualizationMethods <- "tsne"
    }else{
        visualizationMethods <- match.arg(visualizationMethods, several.ok = TRUE)
    }
    
    progressionMethod <- match.arg(progressionMethod)
    
    if (!(is.numeric(clusterSampleSize))) 
        stop("clusterSampleSize must be a numeric number!")
    
    
    ## print arguments for user info
    message("Input arguments:")
    cat("* Project Name: ")
    cat(projectName, "\n")
    cat("* Input FCS files for analysis:\n ")
    cat(paste0("  -", basename(fcsFiles), "\n"))
    cat("* Makrers:\n ")
    cat(paste0("  -", markers, "\n"))
    cat("* Data merging method: ")
    cat(mergeMethod, "\n")
    cat("* Data transformation method: ")
    cat(transformMethod, "\n")
    cat("* Dimensionality reduction method: ")
    cat(dimReductionMethod, "\n")
    cat("* Data clustering method(s): ")
    cat(clusterMethods, "\n")
    cat("* Data visualization method(s): ")
    cat(visualizationMethods, "\n")
    cat("* Subset progression analysis method: ")
    cat(progressionMethod, "\n\n")
    
    
    ## get marker-filtered, transformed, combined exprs data
    message("Extract expression data...")
    exprs_data <- cytof_exprsMerge(fcsFiles, comp = ifCompensation, verbose = FALSE, 
                                   markers = markers, transformMethod = transformMethod, 
                                   mergeMethod = mergeMethod, fixedNum = fixedNum, ...)
    cat("  ", nrow(exprs_data), " x ", ncol(exprs_data), " data was extracted!\n")
    
    
    ## dimension reduced data, a list
    message("Dimension reduction...")
    alldimReductionMethods <- unique(c(visualizationMethods, dimReductionMethod))
    allDimReducedList <- lapply(alldimReductionMethods, 
                                cytof_dimReduction, data = exprs_data)
    names(allDimReducedList) <- alldimReductionMethods
    
    
    ## cluster results, a list
    message("Run clustering...")
    cluster_res <- lapply(clusterMethods, cytof_cluster, 
                          ydata = allDimReducedList[[dimReductionMethod]], 
                          xdata = exprs_data,
                          FlowSOM_k = FlowSOM_k)
    names(cluster_res) <- clusterMethods
    
    
    ## progression analysis results, a list  
    ## NOTE, currently only the first cluster method resutls 
    ## are used for preogression visualization(by default: cluster_res[[1]])
    message("Progression analysis...")   
    progression_res <- cytof_progression(data = exprs_data, 
                                         cluster = cluster_res[[1]], 
                                         method = progressionMethod,
                                         out_dim = 4,
                                         clusterSampleSize = clusterSampleSize)
    
    ## wrap the results
    analysis_results <- list(expressionData = exprs_data,
                             dimReductionMethod = dimReductionMethod,
                             visualizationMethods = alldimReductionMethods, #visualizationMethods,
                             dimReducedRes = allDimReducedList,
                             clusterRes = cluster_res, 
                             progressionRes = progression_res,
                             projectName = projectName,
                             rawFCSdir = rawFCSdir,
                             resultDir = resultDir)
     
    
    ## save the results
    message("Analysis DONE, saving the reuslts...") 
    cytof_writeResults(analysis_results = analysis_results,
                       saveToRData = saveObject,
                       saveToFCS = saveResults,
                       saveToFiles = saveResults)
    
    invisible(analysis_results)
}



#' A Shiny app to interactively visualize the analysis results 
#' 
#' Load the RData object saved by cytofkit, explore the analysis results with interactive control
#'
#' @import shiny
#' @author Hao Chen
#' @export
#' @examples 
#' if (interactive()) cytofkit::cytofkitShinyAPP()
cytofkitShinyAPP = function() {
    shiny::runApp(system.file('shiny', package = 'cytofkit'))
}



#' check the package update news
#' 
#' @export
cytofkitNews <- function() 
{
    newsfile <- file.path(system.file(package = "cytofkit"),
                          "NEWS.Rd")
    file.show(newsfile)
}