R/06_converting_to_csv_and_FCS.R

In ebecht/infinityFlow: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions

#' Exporting results
#' @param paths Character vector of paths to store intput, intermediary results, outputs...
#' @param FCS_export if "none", no FCS export. if "concatenated", export one FCS file with all the data. if "split", export the data in the result folder under the subfolder FCS, with each file corresponding to a (subsampled) input file. If "csv", export raw concatenated data to CSV.
#' @param CSV_export Deprecated
#' @param chans vector of backbone channels' names
#' @param transforms_chan named list of logicle-transformations for backbone channels
#' @param transforms_pe named list of logicle-transformations for Infinity channels
#' @param xp Logicle-transformed backbone expression matrix
#' @param xp_scaled Per-well Z-scored Backbone expression matrix
#' @param umap UMAP dimensionality reduction matrix of the Backbone data
#' @param events.code vector of length nrow(xp) specifying from which well each event originates
#' @param preds matrix of imputed data
#' @param sampling boolean vector of length nrow(xp) specifying which events were selected for final imputation
#' @param a annotation table generated by infinityFlow:::intialize()
#' @param verbose Verbosity
#' @importFrom utils read.csv write.csv
#' @importFrom stats setNames
#' @importFrom flowCore inverseLogicleTransform flowFrame write.FCS
#' @noRd

export_data <- function(
                     paths,
                     FCS_export,
                     CSV_export = NULL,
                     chans=readRDS(file.path(paths["rds"],"chans.Rds")),
                     transforms_chan=readRDS(file.path(paths["rds"],"transforms_chan.Rds")),
                     transforms_pe=readRDS(file.path(paths["rds"],"transforms_pe.Rds")),
                     xp=readRDS(file.path(paths["rds"],"xp.Rds")),
                     xp_scaled=readRDS(file.path(paths["rds"],"xp_transformed_scaled.Rds")),
                     umap=readRDS(file.path(paths["rds"],"umap.Rds")),
                     events.code=readRDS(file.path(paths["rds"],"pe.Rds")),
                     preds=readRDS(file.path(paths["rds"],"predictions.Rds")),
                     sampling=readRDS(file.path(paths["rds"],"sampling_preds.Rds")),
                     a=read.csv(paths["annotation"],sep=",",header=TRUE,stringsAsFactors=FALSE),
                     verbose=TRUE
                     ){
    if(verbose){
        message("Exporting results")
    }
    
    a <- setNames(as.character(a[,"target",]),a[,"file"])
    
    if(verbose){
        message("\tTransforming predictions back to a linear scale")
    }

    preds_raw <- preds

    preds <- lapply(preds,function(x){
        for(pe in names(transforms_pe)){
            ilgcl <- inverseLogicleTransform(trans=transforms_pe[[pe]])
            x[,pe] <- ilgcl(x[,pe])
        }
        x
    })

    preds <- lapply(
        names(preds),
        function(x){
            w <- colnames(preds[[x]])[colnames(preds[[x]])%in%names(a)]
            preds[[x]] <- preds[[x]][,w]
            
            colnames(preds[[x]]) <- paste0(a[colnames(preds[[x]])],".",x)
            preds[[x]]
        }
    )
    preds_raw <- lapply(
        names(preds_raw),
        function(x){
            w <- colnames(preds_raw[[x]])[colnames(preds_raw[[x]])%in%names(a)]
            preds_raw[[x]] <- preds_raw[[x]][,w]
            
            colnames(preds_raw[[x]]) <- paste0(a[colnames(preds_raw[[x]])],".",x)
            preds_raw[[x]]
        }
    )
    
    prediction_colnames <- sort(do.call(c,lapply(preds,colnames)))
    preds <- do.call(cbind,preds)[,prediction_colnames]
    preds_raw <- do.call(cbind,preds_raw)[,prediction_colnames]
    
    unique_pes <- unique(events.code)
    PE_id <- vapply(events.code[sampling],match,table=unique_pes, FUN.VALUE = 1L)
    
    write.csv(file=file.path(paths["output"],"Exploratory_Ab_ID_table.csv"),data.frame(file=unique_pes,target=a[unique_pes],PE_id=seq_len(length(unique_pes))),row.names=FALSE)
    
    ## To make UMAP easier to plot for FlowJo users
    umap <- apply(umap,2,function(x){
        (x-min(x))/(max(x)-min(x))*10000
    })

    preds <- cbind(xp[sampling,],preds[,!colnames(preds)%in%colnames(xp)],Exploratory_Ab_ID=PE_id,umap)
    preds_raw <- cbind(xp_scaled[sampling,],preds_raw[,!colnames(preds_raw)%in%colnames(xp_scaled)],Exploratory_Ab_ID=PE_id,umap)
    
    colnames(preds) <- make.unique(colnames(preds))
    colnames(preds_raw) <- make.unique(colnames(preds_raw))

    if("csv" %in% FCS_export){
        if(verbose){
            message("\t","Exporting as CSV")
        }
        write.csv(preds,file=file.path(paths["output"],"results.csv"),row.names=FALSE)
    }
    
    if("split" %in% FCS_export){
        if(verbose){
            message("\t","Exporting FCS files (1 per well)")
        }
        preds_tmp <- lapply(split(as.data.frame(preds),events.code[sampling]),as.matrix)
        dir.create(file.path(paths["output"],"FCS","split"),showWarnings=FALSE,recursive=TRUE)
        lapply(
            names(preds_tmp),
            function(file){
                FCS <- flowFrame(preds_tmp[[file]])
                FCS@parameters$desc <- as.character(FCS@parameters$desc)
                FCS@parameters$name <- as.character(FCS@parameters$name)
                FCS <- generate_description(FCS)
                invisible(write.FCS(FCS,filename=file.path(paths["output"],"FCS","split",paste0(sub(".fcs","",file),"_target_",gsub("/","-",a[file]),".fcs"))))
            }
        )
    }
    if("concatenated" %in% FCS_export){
        if(verbose){
            message("\t","Exporting concatenated FCS file")
        }
        dir.create(file.path(paths["output"],"FCS","concatenated"),showWarnings=FALSE,recursive=TRUE)
        FCS <- flowFrame(preds)
        FCS@parameters$desc <- as.character(FCS@parameters$desc)
        FCS@parameters$name <- as.character(FCS@parameters$name)
        FCS <- generate_description(FCS)
        invisible(write.FCS(FCS,filename=file.path(paths["output"],"FCS","concatenated","concatenated_results.fcs")))
    }

    saveRDS(preds_raw,file.path(paths["rds"],"predictions_cbound.Rds"))
    saveRDS(prediction_colnames,file.path(paths["rds"],"prediction_colnames.Rds"))
    preds
}