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author | melpetera |
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date | Thu, 23 Nov 2017 08:50:14 -0500 |
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# author: Pauline Ribeyre ##################### # required packages # ##################### library("parallel") # provides cluster methods and "parLapplyLB" function library("grDevices") # provides jpeg handling methods ############ # data # ############ update_list_of_file_names <- function() { # Writes in a file the list of file names using the files created previously in ./RDatas. file.create(source_file_names, showWarnings = FALSE) # erase the file directory <- "RDatas" files <- list.files(directory, full.names = TRUE) for (f in files) { f <- substr(f, 8, nchar(f) - 6) write(f, file = source_file_names, append = TRUE) } } titles_to_columns <- function(indicators) { # Parse the "title" column of the indicator dataframe to separate the different parameters and their values. # # Args: # indicators: dataframe (one row per test) containing the results. # # Returns: # new_indicators: copy of "indicators" with one column added for each parameter that varied. default_settings <- FALSE # parse the title param_names <- c() for (title in indicators$title) { if (title == "default_settings") { default_settings <- TRUE break } else { params <- strsplit(title, "_")[[1]] for (param in params) { name <- strsplit(param, "=")[[1]][1] # some lowercase values create errors because they are primitive functions substr(name, 1, 1) <- toupper(substr(name, 1, 1)) if (!name %in% param_names) param_names <- c(param_names, name) value <- strsplit(param, "=")[[1]][2] if (!exists(name)) assign(name, c()) assign(name, c(get(name), value)) } } } new_indicators <- indicators # add the columns to the dataframe if (!default_settings) { for (name in param_names) { new_indicators <- cbind(new_indicators, get(name)) names(new_indicators)[ncol(new_indicators)] <- name } # order the dataframe's columns order <- c(1, (ncol(new_indicators) - length(param_names) + 1) : ncol(new_indicators), 2 : (ncol(new_indicators) - length(param_names))) new_indicators <- new_indicators[, order] } return (new_indicators) } create_summary <- function(nb_cores, count_duplicates = FALSE) { # Reads the files created by runGC_vary_parameters() and calculates the quality criteria for each test. # # Args: # nb_cores: maximum number of cores to use. # count_duplicates: calculate the number of count_duplicates obtained by each test (slow). # # Returns: # A dataframe (one row per test) containing the results: # title, nb ions, nb zeros/line, nb ions/intensity range, presence of valine, nb count_duplicates (opt). # # # Returns: # # A list containing: # # 1: dataframe (one row per test) containing the results: # # title, nb ions, nb zeros/line, nb ions/intensity range, presence of valine, nb count_duplicates (opt). # # 2: list of the settings sets for each test. time.start <- Sys.time() # start the timer file_names <- readLines(source_file_names) dir.create("count_duplicates", showWarnings = FALSE) #create the folder where the details of the count_duplicates will be saved # summ <- create_summary_parallel(1, file_names) # cat("indic:\n",summary[1][[1]],"\n") # run the function on several cores if (length(file_names) != 0) { if (length(file_names) < nb_cores) nb_cores <- length(file_names) cluster <- makeCluster(nb_cores) #, outfile = "") summaries <- parLapplyLB(cluster, 1:length(file_names), create_summary_parallel, file_names = file_names, count_duplicates = count_duplicates) #, pb = pb) stopCluster(cluster) } else stop("There are no files to generate the output from.") # concatenate the results obtained by all the cores indicators <- NULL for (summary in summaries) indicators <- rbind(indicators, t(summary)) # settings_list <- c() # for (summary in summaries) { # settings <- summary[2][[1]] # indic <- summary[1][[1]] # # indicators <- rbind(indicators, t(indic)) # settings_list <- c(settings_list, settings) # } indicators <- data.frame(indicators) col_names <- c("title", "nb_pseudospectra", "zeros_per_line", "f0to10E3", "f10E3to10E4", "f10E4to10E5", "f10E5to10E6", "f10E6to10E7", "f10E7") if (check_ions) col_names <- c(col_names, ions_name) names(indicators)[1:length(col_names)] <- col_names if (count_duplicates) names(indicators)[(ncol(indicators) - 1):ncol(indicators)] <- c("count_duplicates", "nb_ions") indicators <- titles_to_columns(indicators) time.end <- Sys.time() # stop the timer Tdiff <- difftime(time.end, time.start) print(Tdiff) # return (list(indicators, settings_list)) return (indicators) } create_summary_parallel <- function(n, file_names, count_duplicates = FALSE) { #}, pb) { # Reads the files created by runGC_vary_parameters() and calculates the quality criteria for each test. # # Args: # n: index of the current test, to select the corresponding title. # file_names: list of titles (one for each test) (concatenation of the values taken by the varied parameters). # nb_cores: maximum number of cores to use. # count_duplicates: calculate the number of count_duplicates obtained by each test (slow). # # Returns: # A dataframe (one row per test) containing the results: # title, nb ions, nb zeros/line, nb ions/intensity range, presence of valine. # # # Returns: # # A list containing: # # 1: dataframe (one row per test) containing the results: # # title, nb ions, nb zeros/line, nb ions/intensity range, presence of valine. # # 2: list of the settings sets for each test. source(source_spectrum, environment()) intensities_x <- c(1000, 10000, 100000, 1000000, 10000000) this_title <- file_names[n] # calculate the number of zeros file_title <- paste0("Peak_tables/", this_title, ".tsv") peak_table <- read.table(file_title, sep="\t", header=TRUE) peak_table_values <- peak_table[,5:ncol(peak_table)] zeros <- sum(peak_table$nb_zeros) nb_lines <- nrow(peak_table) zeros_per_line <- round(zeros/nb_lines, 4) # count the number of ions by intensity range intensities_y <- c() intensities_y[1] <- length(rowMeans( peak_table_values, na.rm = TRUE)[rowMeans(peak_table_values, na.rm = TRUE) < intensities_x[1]]) for (i in 2:length(intensities_x)) intensities_y[i] <- length(rowMeans( peak_table_values, na.rm = TRUE)[rowMeans(peak_table_values, na.rm = TRUE) < intensities_x[i] & rowMeans(peak_table_values, na.rm = TRUE) > intensities_x[i - 1]]) intensities_y[i + 1] <- length(rowMeans( peak_table_values, na.rm = TRUE)[rowMeans(peak_table_values, na.rm = TRUE) > intensities_x[i]]) # load the settings file_title <- paste0("RDatas/", this_title, ".RData") load(file_title) # count the number of count_duplicates and record in a file if (count_duplicates) { file_title <- paste0("count_duplicates/", this_title, ".tsv") nb_count_duplicates <- data.frame(count_duplicates_function(GC_results)) names(nb_count_duplicates) <- c("mz_min", "mz_max", "rt", "count_duplicates") write.table(nb_count_duplicates, file = file_title, sep = "\t", row.names = FALSE) count_duplicates <- nrow(nb_count_duplicates) nb_ions <- 0 pseudospectra <- GC_results$PseudoSpectra for (ps in pseudospectra) { nb_ions <- nb_ions + nrow(ps$pspectrum) } } summary <- c(this_title, nb_lines, zeros_per_line, intensities_y) # check the presence of ions if (nb_ions_to_check > 0) { for (i in 1:nb_ions_to_check) { name <- ions_name[[i]] rt <- ions_rt[[i]] mzs <- ions_mzs[[i]] cat("Check:", name, rt, mzs, "\n") value <- is_ion_present(GC_results, rt = rt, mz_list = mzs) assign(name, value) summary <- c(summary, get(name)) } } # check the presence of ion valine 12C and 13C # valine <- is_ion_present(GC_results, rt = 9.67, mz_list = list(218.1105, 219.1146)) # summary <- c(summary, valine) if (count_duplicates) summary <- c(summary, count_duplicates, nb_ions) # return (list(summary, settings)) return (summary) } ############ # graphs # ############ ions_per_intensity <- function(indicators) { # For each test, plots the number of ions for each range of intensity. # # Args: # indicators: dataframe (one row per test) containing the results. pdf(intensity_graph_out) par(mar = c(5.1,4.1,5,2.1)) for (i in 1:nrow(indicators)) { indic <- indicators[i,] names <- c("f0to10E3", "f10E3to10E4", "f10E4to10E5", "f10E5to10E6", "f10E6to10E7", "f10E7") intensities_y <- c() for (colname in names) { intensities_y <- c(intensities_y, indic[colname]) } intensities_y <- unlist(intensities_y) intensities_y <- as.numeric(levels(intensities_y))[intensities_y] title <- as.character(indic$title) title <- strsplit(title, "_")[[1]] plot_title <- "" for (i in 1:length(title)) { plot_title <- paste(plot_title, title[[i]]) if (i %% 2 == 0) plot_title <- paste(plot_title, "\n") else plot_title <- paste(plot_title, " ") } barplot(intensities_y, names.arg = names, xlab = "intensity", ylab = "number of ions", main = plot_title, cex.main = 0.8) } dev.off() } graph_results <- function(indicators, criteria) { # Plots the results. # # Args: # indicators: dataframe (one row per test) containing the results. first_criteria <- grep("nb_pseudospectra", names(indicators)) nb_params <- first_criteria - 2 this_criteria <- grep(criteria, names(indicators)) # values taken by each parameter values <- list() for (i in 2:(1 + nb_params)) { lev <- unique(indicators[,i]) values[[i - 1]] <- lev } length <- lapply(values, length) # indexes of the parameters taking the most values longest_1 <- which.max(length); length[longest_1] <- -1 longest_2 <- which.max(length); length[longest_2] <- -1 longest_3 <- which.max(length); length[longest_3] <- -1 # indexes of the other parameters shortest <- which(length != -1) # all combinations of the values taken by these parameters combinations <- list() for (s in shortest) { page <- indicators[,s + 1] combinations[[length(combinations) + 1]] <- sort(as.numeric(as.character(unique(page)))) } names(combinations) <- names(indicators)[shortest + 1] combinations <- expand.grid(combinations) # save the plots in a pdf file pdf(compare_graph_out) nb_rows <- length(values[longest_3][[1]]) nb_cols <- length(values[longest_2][[1]]) par(mfrow = c(nb_rows, nb_cols), mar = c(4.5,4.5,5,1)) # plot parameters x <- sort(as.numeric(as.character(unique(indicators[,longest_1 + 1])))) min_zeros <- min(as.numeric(as.character(indicators$zeros_per_line))) max_zeros <- max(as.numeric(as.character(indicators$zeros_per_line))) for (rowi in 1:nrow(combinations)) { row <- combinations[rowi,] title <- "" lines <- indicators for (coli in 1:length(row)) { value <- as.numeric(as.character(row[coli])) title <- paste(title, names(row[coli]), "=", value) if (coli %% 2 == 0) title <- paste(title, "\n") else title <- paste(title, " ") lines <- lines[lines[names(row[coli])] == value,] } # values taken by the parameters in_plot <- lines[,longest_1 + 1] vertical <- lines[,longest_2 + 1] horizontal <- lines[,longest_3 + 1] # for each horizontal value for (horiz in sort(unique(horizontal))) { # for each vertical value for (vertic in sort(unique(vertical))) { y <- c() for (this_y in sort(unique(in_plot))) { # line <- line[page == p & horizontal == horiz & vertical == vertic & in_plot == this_y,] line <- lines[horizontal == horiz & vertical == vertic & in_plot == this_y,] if (nrow(line) != 1) stop("To plot the results, each set of the parameters' values must be represented exactly 1 time") value <- line[,this_criteria] value <- as.numeric(as.character(value)) y <- c(y, value) } this_title <- paste(title, names(indicators)[longest_3 + 1], "=", horiz, "\n", names(indicators)[longest_2 + 1], "=", vertic) # plot this graph plot(x, y, ylim = c(min_zeros, max_zeros), type = "b", xlab = names(indicators)[longest_1 + 1], ylab = criteria, main = this_title, cex.main = 0.8) } } } par(mfrow = c(1,1)) dev.off() } ############ # main # ############ update_list_of_file_names() summary <- create_summary(nb_cores, count_duplicates = count_duplicates) # settings_list <- summary[2][[1]] # indicators_ <- summary[1][[1]] indicators_ <- summary indicators <- indicators_[order(indicators_$zeros_per_line),] # sort by number of zeros per line # indicators_ <- indicators[order(as.numeric(row.names(indicators))),] # order back to row numbers # record the summary in a file indicators_to_write <- indicators[, !names(indicators) %in% c("title", "f0to10E3", "f10E3to10E4", "f10E4to10E5", "f10E5to10E6", "f10E6to10E7", "f10E7")] write.table(indicators_to_write, file = summary_out, quote = FALSE, row.names = FALSE, sep = "\t") # ions per intensity graph ions_per_intensity(indicators_) # plots to compare each set of parameters graph_results(indicators_, criteria = "zeros_per_line") # zip of pseudospectra .msp and .tsv files system(paste0('cd Pseudospectra ; ls . | grep -e "msp$" -e "tsv$" | zip -r -@ "peakspectra_out.zip"')) system(paste("cd Pseudospectra ; mv peakspectra_out.zip", peakspectra_out)) # zip of count_duplicates .tsv files if (count_duplicates) { system(paste0('cd count_duplicates ; ls . | grep "tsv$" | zip -r -@ "count_duplicates_out.zip"')) system(paste("cd count_duplicates ; mv count_duplicates_out.zip", count_duplicates_out)) }