annotate FlowSOMMApIndividualFCS.R @ 2:f611ded6c6df draft default tip

planemo upload for repository https://github.com/ImmPortDB/immport-galaxy-tools/tree/master/flowtools/flowsom_cross_comp commit bbff20e20dc2b9dbb40b613a0d5f16ee8132446d

#!/usr/bin/Rscript
# Module for Galaxy
# Generates FlowSOM reference tree
# with FlowSOM AggregateFlowFrames
######################################################################
#                  Copyright (c) 2017 Northrop Grumman.
#                          All rights reserved.
######################################################################
#
# Version 1
# Cristel Thomas
#
#
library(FlowSOM)
library(flowCore)

## geometric mean from
# https://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in
gm_mean <- function(x, na_rm = TRUE) {
  exp(sum(log(x[x > 0]), na.rm = na_rm) / length(x))
}

pretty_marker_names <- function(flow_frame) {
  n <- flow_frame@parameters@data[, "name"]
  d <- flow_frame@parameters@data[, "desc"]
  d[is.na(d)] <- n[is.na(d)]
  pretty_names <- list()
  if (any(grepl("#", d))) {
    # Support for hashtag notation:
    pretty_names <- gsub("#(.*)$", " (\\1)", d)
  } else {
    pretty_names <- paste(d, " <", n, ">", sep = "")
  }
  return(pretty_names)
}

check_markers <- function(fcsfiles, flag_ff = FALSE) {
  marker_check <- TRUE
  for (i in seq_along(fcsfiles)) {
    if (flag_ff) {
      fcs <- readRDS(fcsfiles[i])
    } else {
      fcs <- read.FCS(fcsfiles[i], transformation = FALSE)
    }
    if (i == 1) {
      m1 <- as.vector(pData(parameters(fcs))$desc)
    } else {
      m2 <- as.vector(pData(parameters(fcs))$desc)
      if (is.na(all(m1 == m2))) {
        mm1 <- is.na(m1)
        mm2 <- is.na(m2)
        if (all(mm1 == mm2)) {
          if (!all(m1 == m2, na.rm = TRUE)) {
            marker_check <- FALSE
          }
        } else {
          marker_check <- FALSE
        }
      } else if (!all(m1 == m2)) {
        marker_check <- FALSE
      }
    }
  }
  if (!marker_check) {
    quit(save = "no", status = 13, runLast = FALSE)
  }
}

map_to_tree <- function(filenames, filepaths,
                        flag_ff = FALSE, reftree, cluster = 10,
                        outdir = "", flag_meta = FALSE,
                        mfi = "mfi", stat1 = "", stat2 = "",
                        stat3 = "", plot = "", mplot = "") {

  check_markers(filepaths, flag_ff)
  # get tree
  fst <- readRDS(reftree)
  plots <- FALSE
  mplots <- FALSE
  dir.create(outdir)
  if (!plot == "") {
    dir.create(plot)
    plots <- TRUE
  }
  if (!mplot == "") {
    dir.create(mplot)
    mplots <- TRUE
  }
  meta_c <- metaClustering_consensus(fst$map$codes, k = cluster, seed = 33)
  nb_pop <- if (flag_meta) cluster else max(fst$map$mapping[, 1])
  nb_samples <- length(filepaths)
  nb_marker <- length(fst$prettyColnames)
  print_markers <- gsub(" <.*>", "", fst$prettyColnames)
  print_markers_ff <- append(print_markers, "Population")

  m_stat1 <- matrix(0, nrow = nb_samples, ncol = (nb_pop + 2))
  colnames(m_stat1) <- c("FileID", "SampleName", seq_len(nb_pop))

  sink(stat2)
  cat(print_markers, sep = "\t")
  cat("\tPercentage\tPopulation\tSampleName\n")
  sink()

  col_m3 <- c("Population")
  for (m in print_markers){
    m1 <- paste(m, "mean", sep = "_")
    m2 <- paste(m, "median", sep = "_")
    m3 <- paste(m, "stdev", sep = "_")
    col_m3 <- append(col_m3, c(m1, m2, m3))
  }
  col_stat3 <- c(col_m3,
                 "Percentage_mean",
                 "Percentage_median",
                 "Percentage_stdev")

  for (i in seq_along(filepaths)) {
    if (flag_ff) {
      ff <- readRDS(filepaths[i])
    } else {
      ff <- read.FCS(filepaths[i], transformation = FALSE)
    }
    if (i == 1) {
      # compare to tree markers
      pm <- pretty_marker_names(ff)
      if (!all(fst$prettyColnames %in% pm)) {
        quit(save = "no", status = 14, runLast = FALSE)
      }
    }

    fsom <- NewData(fst, ff)

    if (mplots) {
      markers <- colnames(ff)
      tmpmplot <- paste(filenames[i], "marker_plots.pdf", sep = "_")
      pdf(file.path(mplot, tmpmplot), useDingbats = FALSE, onefile = TRUE)
      for (marker in markers){
        PlotMarker(fsom, marker)
      }
      dev.off()
    }

    if (!plot == "") {
      plotpath <- paste(filenames[i], "tree.png", sep = "_")
      png(file.path(plot, plotpath), type = "cairo", height = 800, width = 800)
      PlotStars(fsom, backgroundValues = as.factor(meta_c))
      dev.off()
    }

    m <- matrix(0, nrow = nrow(ff), ncol = 1)
    s <- seq_len(nrow(ff))
    if (flag_meta) {
      m[s, ] <- meta_c[fsom$map$mapping[, 1]]
    } else {
      m[s, ] <- fsom$map$mapping[, 1]
    }
    colnames(m) <- "FlowSOM"
    ff <- cbind2(ff, m)
    out <- exprs(ff)
    colnames(out) <- print_markers_ff

    clr_table <- paste(filenames[i], "clustered.flowclr", sep = "_")
    write.table(out,
                file = file.path(outdir, clr_table),
                quote = FALSE,
                row.names = FALSE,
                col.names = TRUE,
                sep = "\t",
                append = FALSE)

    cluster_count <- table(out[, "Population"])
    cluster_prop <- prop.table(cluster_count) * 100
    m1_tmp <- numeric(nb_pop)
    for (j in 1:nb_pop){
      if (as.character(j) %in% names(cluster_count)) {
        m1_tmp[[j]] <- format(
                              round(cluster_prop[[as.character(j)]], 2),
                              nsmall = 2)
      }
    }
    samplename <- paste("Sample", i, sep = "")
    m_stat1[i, ] <- c(filenames[i], samplename, m1_tmp)
    # flowstat2
    # Marker1 Marker2 Marker3 ... Population Percentage SampleName
    # MFIs for each marker
    # dimension ==> col = nb of markers + 3; row = nb of files * nb of clusters
    if (mfi == "mfi") {
      m2 <- aggregate(out[, 1:nb_marker], list(out[, nb_marker + 1]), mean)
    } else if (mfi == "mdfi") {
      m2 <- aggregate(out[, 1:nb_marker], list(out[, nb_marker + 1]), median)
    } else {
      m2 <- aggregate(out[, 1:nb_marker], list(out[, nb_marker + 1]), gm_mean)
    }

    m2["Percentage"] <- as.character(cluster_prop)
    m2["Population"] <- as.character(m2$Group.1)
    m2["SampleName"] <- samplename
    m2t <- as.matrix(m2[2:length(m2)])
    write.table(m2t,
                file = stat2,
                quote = FALSE,
                row.names = FALSE,
                col.names = FALSE,
                sep = "\t",
                append = TRUE)

  }
  write.table(m_stat1,
              file = stat1,
              quote = FALSE,
              row.names = FALSE,
              col.names = TRUE,
              sep = "\t",
              append = FALSE)

  m2df <- read.table(stat2, sep = "\t", header = TRUE)
  ag <- aggregate(m2df[, 0:nb_marker + 1],
                  list(m2df[, nb_marker + 2]),
                  function(x) c(mn = mean(x), med = median(x), stdv = sd(x)))
  m3t <- as.matrix(ag)
  colnames(m3t) <- col_stat3
  write.table(m3t, file = stat3,
              quote = FALSE,
              row.names = FALSE,
              col.names = TRUE, sep = "\t",
              append = FALSE)
}

flow_frame_or_fcs <- function(filenames,
                              filepaths,
                              reftree,
                              cluster = 10, outdir = "",
                              flag_meta = FALSE,
                              mfi = "mfi", stat1 = "", stat2 = "",
                              stat3 = "", plot = "", mplot = "") {
  is_fcs <- FALSE
  is_ff <- FALSE
  flag_ff <- FALSE
  i <- 0
  for (f in filepaths){
    tryCatch({
      is_fcs <- isFCSfile(f)
    }, error = function(ex) {
      print(paste(ex))
    })
    if (!is_fcs) {
      tryCatch({
        ff <- readRDS(f)
        is_ff <- TRUE
      }, error = function(ex) {
        print(paste(ex))
      })
    } else {
      i <- i + 1
    }
    if (!is_ff && !is_fcs) {
      quit(save = "no", status = 10, runLast = FALSE)
    }
  }
  if (i == 0) {
    flag_ff <- TRUE
  } else if (!i == length(filenames)) {
    quit(save = "no", status = 12, runLast = FALSE)
  }
  map_to_tree(filenames,
              filepaths,
              flag_ff, reftree, cluster, outdir, flag_meta,
              mfi, stat1, stat2, stat3, plot, mplot)
}

args <- commandArgs(trailingOnly = TRUE)
plot <- ""
mplot <- ""
m <- 8
flag_meta <- FALSE
if (args[4] == "meta") {
  flag_meta <- TRUE
}
if (args[9] == "newDataTrees") {
  plot <- "newDataTrees"
  m <- m + 1
  if (args[10] == "newDataMarkers") {
    mplot <- "newDataMarkers"
    m <- m + 1
  }
} else if (args[9] == "newDataMarkers") {
  mplot <- "newDataMarkers"
  m <- m + 1
}

n <- m + 1
nb_files <- (length(args) - m) / 2
files1 <- character(nb_files)
files2 <- character(nb_files)
j <- 1
file_list <- args[n:length(args)]
for (i in seq_along(file_list)) {
  if (i %% 2) {
    files1[[j]] <- file_list[i]
    files2[[j]] <- file_list[i + 1]
    j <- j + 1
  }
}

flow_frame_or_fcs(files2,
                  files1, args[1], as.numeric(args[3]), args[2], flag_meta,
                  args[5], args[6], args[7], args[8], plot, mplot)