In MACSQuantifyR: Fast treatment of MACSQuantify FACS data

---

title: Report for MACSQuantifyR analysis subtitle: r params$exp date: "r Sys.Date()"

---

library(png)
library(grid)
library(gridExtra)
MACSQuant <- readRDS(paste(params$path, "/outputMQ/MACSQuant.RDS", sep = ""))
path <- [e-mail]$path

m <- max(lengths([e-mail]))

Experimental parameters

These are the parameters used for this experiment.

for (i in 1:length([e-mail])) {
    values <- [e-mail][[i]]
    name <- names([e-mail])[i]
    if (length(values) == 0) values <- NA
    cat(c(name, ": ", values))
    cat("\n")
}

This is the template for this experiment:

template_path <- paste([e-mail]$path, "/outputMQ/plate_template.png", sep = "")
template_image <- readPNG(template_path)
grid.raster(template_image)

• This template was used to order the data according to the replicates you selected.
  • You will find the file ./outputMQ/sorted_table.txt containing the results

Statistics

This is the table of statistics for each condition:

mat <- as.matrix(MACSQuant@statistics[, 3:7])
rownames(mat) <- MACSQuant@statistics[, 2]
cat(colnames(mat))
cat("\n")
write.table(format(round(mat, 3), justify = "right"),
    col.names = FALSE, quote = FALSE)

In this table and for each conditions:

• the mean of percentage over replicates of cells that incorporated the fluorochrome: Fluo.percent.plus and Fluo.percent.minus
• the standard variation for these percentages over replicates
• the mean of cell counts over replicates

• the standard deviation of cell count over replicates

  • You will find the file ./outputMQ/statistics.txt containing the results

Graphical representations

path_graph1 <- paste([e-mail]$path, "/outputMQ/barplot_counts_pipeline.png", sep = "")
path_graph2 <- paste([e-mail]$path, "/outputMQ/barplot_percent_pipeline.png", sep = "")
if (file.exists(path_graph1) & file.exists(path_graph2)) {
    img1 <-  rasterGrob(as.raster(readPNG(path_graph1)), interpolate = FALSE)
    img2 <-  rasterGrob(as.raster(readPNG(path_graph2)), interpolate = FALSE)
    grid.arrange(img1, img2, ncol = 2)
}

• Here are represented the informations contains in the statistics table\n
  • You will find the file ./outputMQ/barplot_percent.png or ...barplot_counts.png

c1 <- paste([e-mail]$path, "/outputMQ/barplot_counts.png", sep = "")
c2 <- paste([e-mail]$path, "/outputMQ/barplot_percent.png", sep = "")
if (file.exists(c1) & file.exists(c2)) {
    path_graph3 <- c1

    img3 <-  readPNG(path_graph3)
    grid.raster(img3)

}

c1 <- paste([e-mail]$path, "/outputMQ/barplot_counts.png", sep = "")
c2 <- paste([e-mail]$path, "/outputMQ/barplot_percent.png", sep = "")
if (file.exists(c1) & file.exists(c2)) {
    path_graph4 <- c2

    img4 <-  readPNG(path_graph4)
    grid.raster(img4)
}

if (sum(dim([e-mail])) > 0) {
    cat("\n##", "Combination index", "\n")
    cat("This is the result table of combination index for each combo condition:\n\n")
}

if (sum(dim([e-mail])) > 0) {
    mat <- as.matrix([e-mail][c(1, 2, 3, 4)])
    rownames(mat) <- [e-mail][, 9]
    cat(c("Decision", colnames(mat)))
    cat("\n")
    write.table(format(round(mat, 3), justify = "right"),
        col.names = FALSE, quote = FALSE)
}

ci1 <- paste([e-mail]$path, "/outputMQ/dose_response_curve.png", sep = "")
ci2 <- paste([e-mail]$path, "/outputMQ/median_effect.png", sep = "")
ci3 <- paste([e-mail]$path, "/outputMQ/isobologram.png", sep = "")
ci4 <- paste([e-mail]$path, "/outputMQ/barplot_combination_index.png", sep = "")


if  (file.exists(ci1) & file.exists(ci2) & file.exists(ci3) & file.exists(ci4)) {
    cat("* Here are the representations for combination indexes:\n    * You will find the files\n\t- ./outputMQ/dose_response_curve.png, \n\t- ./outputMQ/median_effect.png and \n\t- ./outputMQ/isobologram.png\n\t- ./outputMQ/barplot_combination_index.png\n\n")
}

if  (file.exists(ci1) & file.exists(ci2) & file.exists(ci3) & file.exists(ci4)) {
    plot1 <-  rasterGrob(as.raster(readPNG(ci1)), interpolate = FALSE)
    plot2 <-  rasterGrob(as.raster(readPNG(ci2)), interpolate = FALSE)
    grid.arrange(plot1, plot2, ncol = 2)
}

if  (file.exists(ci1) & file.exists(ci2) & file.exists(ci3) & file.exists(ci4)) {
    plot3 <-  rasterGrob(as.raster(readPNG(ci3)), interpolate = FALSE)
    plot4 <-  rasterGrob(as.raster(readPNG(ci4)), interpolate = FALSE)
    grid.arrange(plot3, plot4, ncol = 2)}

MACSQuantifyR documentation built on Nov. 8, 2020, 5:08 p.m.