comparison scripts/cluster.R @ 10:49776718ae90 draft default tip

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit 0ffa71ef9f8d020fe7ba94502db8cec26fd8741f

9:0bff0ee0683a

VERSION <- "0.5" # nolint

args <- commandArgs(trailingOnly = TRUE)

if (length(args) != 1) {
     message(paste("VERSION:", VERSION))
     stop("Please provide the config file")
}

suppressWarnings(suppressPackageStartupMessages(require(RaceID)))
## suppressWarnings(suppressPackageStartupMessages(require(scran)))  # nolint
source(args[1])

do.filter <- function(sc) { # nolint
    if (!is.null(filt.lbatch.regexes)) {
        lar <- filt.lbatch.regexes
        nn <- colnames(sc@expdata)
        filt$LBatch <- lapply(1:length(lar), function(m) {  # nolint
            return(nn[grep(lar[[m]], nn)])})
    }

    sc <- do.call(filterdata, c(sc, filt))

    ## Get histogram metrics for library size and number of features

    print(tmp)
    ## required, for extracting midpoint
    unq <- unique(filt_feat)
    if (length(unq) == 1) {
        abline(v = unq, col = "red", lw = 2)
        text(tmp$mids, table(filt_feat)[[1]] - 100, pos = 1,
             paste(10^unq, "\nFeatures\nin remaining\nCells",
                   sep = ""), cex = 0.8)
    }

    if (filt.use.ccorrect) {
        par(mfrow = c(2, 2))
        sc <- do.call(CCcorrect, c(sc, filt.ccc))

    print(plotsensitivity(sc))
    print(plotoutlierprobs(sc))
    ## Heatmaps
    test1 <- list()
    test1$side <- 3
    test1$line <- 0  #1 #3

    x <- clustheatmap(sc, final = FALSE)
    print(do.call(mtext, c(paste("(Initial)"), test1)))
    x <- clustheatmap(sc, final = TRUE)
    print(do.call(mtext, c(paste("(Final)"), test1)))

        buffer <- paste(rep("", 36), collapse = " ")
        print(do.call(mtext, c(paste(buffer, "Cluster ", n), test)))
        test$line <- -1
        print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test)))
        test$line <- 0
        print(do.call(mtext, c(paste(buffer, "(fc > ",
                                     genelist.foldchange, ")"), test)))
    })
    write.table(df, file = out.genelist, sep = "\t", quote = FALSE)
}


writecellassignments <- function(sc) {
    dat <- sc@cluster$kpart
    tab <- data.frame(row.names = NULL,
                      cells = names(dat),
                      cluster.initial = dat,
                      cluster.final = sc@cpart,
                      is.outlier = names(dat) %in% sc@out$out)

    write.table(tab, file = out.assignments, sep = "\t",
                quote = FALSE, row.names = FALSE)
}


pdf(out.pdf)

if (use.filtnormconf) {
    sc <- do.filter(sc)
    message(paste(" - Source:: genes:", nrow(sc@expdata),
                  ", cells:", ncol(sc@expdata)))
    message(paste(" - Filter:: genes:", nrow(as.matrix(getfdata(sc))),
                  ", cells:", ncol(as.matrix(getfdata(sc)))))
    message(paste("         :: ",
                  sprintf("%.1f", 100 * nrow(as.matrix(
                                            getfdata(sc))) / nrow(sc@expdata)),
                  "% of genes remain,",
                  sprintf("%.1f", 100 * ncol(as.matrix(
                                            getfdata(sc))) / ncol(sc@expdata)),
                  "% of cells remain"))
    write.table(as.matrix(sc@ndata), file = out.table, col.names = NA,
                row.names = TRUE, sep = "\t", quote = FALSE)
}

if (use.cluster) {
    par(mfrow = c(2, 2))
    sc <- do.cluster(sc)

10:49776718ae90

VERSION <- "0.5" # nolint

args <- commandArgs(trailingOnly = TRUE)

if (length(args) != 1) {
    message(paste("VERSION:", VERSION))
    stop("Please provide the config file")
}

suppressWarnings(suppressPackageStartupMessages(require(RaceID)))
## suppressWarnings(suppressPackageStartupMessages(require(scran)))  # nolint
source(args[1])

do.filter <- function(sc) { # nolint
    if (!is.null(filt.lbatch.regexes)) {
        lar <- filt.lbatch.regexes
        nn <- colnames(sc@expdata)
        filt$LBatch <- lapply(1:length(lar), function(m) { # nolint
            return(nn[grep(lar[[m]], nn)])
        })
    }

    sc <- do.call(filterdata, c(sc, filt))

    ## Get histogram metrics for library size and number of features

    print(tmp)
    ## required, for extracting midpoint
    unq <- unique(filt_feat)
    if (length(unq) == 1) {
        abline(v = unq, col = "red", lw = 2)
        text(tmp$mids, table(filt_feat)[[1]] - 100,
            pos = 1,
            paste(10^unq, "\nFeatures\nin remaining\nCells",
                sep = ""
            ), cex = 0.8
        )
    }

    if (filt.use.ccorrect) {
        par(mfrow = c(2, 2))
        sc <- do.call(CCcorrect, c(sc, filt.ccc))

    print(plotsensitivity(sc))
    print(plotoutlierprobs(sc))
    ## Heatmaps
    test1 <- list()
    test1$side <- 3
    test1$line <- 0 # 1 #3

    x <- clustheatmap(sc, final = FALSE)
    print(do.call(mtext, c(paste("(Initial)"), test1)))
    x <- clustheatmap(sc, final = TRUE)
    print(do.call(mtext, c(paste("(Final)"), test1)))

        buffer <- paste(rep("", 36), collapse = " ")
        print(do.call(mtext, c(paste(buffer, "Cluster ", n), test)))
        test$line <- -1
        print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test)))
        test$line <- 0
        print(do.call(mtext, c(paste(
            buffer, "(fc > ",
            genelist.foldchange, ")"
        ), test)))
    })
    write.table(df, file = out.genelist, sep = "\t", quote = FALSE)
}


writecellassignments <- function(sc) {
    dat <- sc@cluster$kpart
    tab <- data.frame(
        row.names = NULL,
        cells = names(dat),
        cluster.initial = dat,
        cluster.final = sc@cpart,
        is.outlier = names(dat) %in% sc@out$out
    )

    write.table(tab,
        file = out.assignments, sep = "\t",
        quote = FALSE, row.names = FALSE
    )
}


pdf(out.pdf)

if (use.filtnormconf) {
    sc <- do.filter(sc)
    message(paste(
        " - Source:: genes:", nrow(sc@expdata),
        ", cells:", ncol(sc@expdata)
    ))
    message(paste(
        " - Filter:: genes:", nrow(as.matrix(getfdata(sc))),
        ", cells:", ncol(as.matrix(getfdata(sc)))
    ))
    message(paste(
        "         :: ",
        sprintf("%.1f", 100 * nrow(as.matrix(
            getfdata(sc)
        )) / nrow(sc@expdata)),
        "% of genes remain,",
        sprintf("%.1f", 100 * ncol(as.matrix(
            getfdata(sc)
        )) / ncol(sc@expdata)),
        "% of cells remain"
    ))
    write.table(as.matrix(sc@ndata),
        file = out.table, col.names = NA,
        row.names = TRUE, sep = "\t", quote = FALSE
    )
}

if (use.cluster) {
    par(mfrow = c(2, 2))
    sc <- do.cluster(sc)