Setting up Directory

.Rbuildignore

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+^\.pbattributes$
+^manifest\.json$

.Rprofile

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+source("renv/activate.R")

.gitignore

@@ -37,3 +37,9 @@ vignettes/*.pdf
 
 # R Environment Variables
 .Renviron
+manifest.json
+Datasets/Raw/*.txt
+Datasets/Raw/*.dat
+Datasets/Raw/*.zip
+Datasets/Raw/*.csv
+Datasets/Raw/*.RData

.pbattributes

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+Datasets/Raw/*.txt
+Datasets/Raw/*.dat
+Datasets/Raw/*.zip
+Datasets/Raw/*.csv
+Datasets/Raw/*.RData

Codes/00_Dir_Setup.R

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+#Settting up directory
+##Settting up directory
+options(repos = getOption("repos")["CRAN"])
+install.packages("PTXQC")
+install.packages("pacman")
+pacman::p_load(piggyback, renv, here, tidyverse, targets,
+               visNetwork)
+install.packages('piggyback')
+install.packages('renv')
+install.packages('here')
+install.packages('tidyverse')
+install.packages('targets')
+install.packages('visNetwork')
+#testthat::use_test()
+
+## Created a first release directly on Github
+#pb_new_release("Skourtis/Project_Template")
+piggyback::pb_track(c("Datasets/Raw/*.txt",
+                      "Datasets/Raw/*.dat",
+                      "Datasets/Raw/*.zip",
+                      "Datasets/Raw/*.csv",
+                      "Datasets/Raw/*.RData"))
+
+piggyback::pb_track() %>%
+    pb_upload(repo = "Skourtis/Project_Template")
+
+##end
+renv::snapshot()
+
+

Codes/01_Downloading_Files.R

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+#Downloading from Git template
+#Cachem install error bc 'make' was not installed, Rtools installed
+#Manually created .Renv and paste the path from Rtools webpage.
+renv::restore()
+install.packages('devtools')
+
+options(repos = getOption("repos")["CRAN"])
+devtools::install_github("bartongroup/Proteus", build_opts= c("--no-resave-data", "--no-manual"), build_vignettes=FALSE)
+pacman::p_load(piggyback, renv, here, tidyverse)
+pb_download()

Codes/functions.R

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+Quality_Control <- function(x){
+    ### takes as input the txt output folder of MaxQuant 
+    ### in the form of the here::here function
+    ### and produces a QC report
+    pacman::p_temp("PTXQC")
+    require(methods)
+    r = createReport(x)
+}
+
+load_MaxQuant <- function(txt_folder){
+    ### This function takes the txt folder, 
+    ### the name of the measure_col,number of samples and 
+    ### names of the conditions and output the peptides per condition and 
+    ### reads the Maxquant proteinGroups file
+    #txt_folder <-  here::here("Datasets", "Raw",
+     #                         "2020MQ044txt",
+      #                        "txt")
+
+    condition_names <-  c(rep("control",3),
+                          rep("sh_1",3),
+                          rep("sh_2",3))
+    samples_ids <-  1:9
+    measure_col <-  "Intensity"
+
+    measCols <- list(
+        HL = measure_col
+    )
+
+    eviCols <- list(
+        sequence = 'Sequence',
+        modified_sequence = 'Modified sequence',
+        modifications = 'Modifications',
+        protein_group = 'Proteins',
+        protein = 'Leading razor protein',
+        experiment = 'Experiment',
+        charge = 'Charge',
+        reverse = 'Reverse',
+        contaminant = 'Potential contaminant'
+    )
+    evi <- proteus::readEvidenceFile(paste0(txt_folder,
+                                           "/evidence.txt"), 
+                                     measure.cols=measCols, 
+                                     data.cols=eviCols, 
+                                     zeroes.are.missing=FALSE)
+
+
+
+    meta <- data.frame(experiment = sort(evi$experiment %>% unique()),
+                       measure = measure_col,
+                       condition = condition_names,
+                       sample = sort(evi$experiment %>% unique()),
+                       replicate = c(rep(1,3),rep(2,3),rep(3,3)))
+
+    pepdat <- proteus::makePeptideTable(evi, 
+                                       meta,
+                                       measure.cols=measCols, 
+                                       experiment.type="label-free")
+
+    measure_columns <- list(paste(measure_col,samples_ids, sep = " " )) %>% unlist %>%
+        setNames(paste("Sample", samples_ids, sep = "_"))
+
+    list(proteins = proteus::readProteinGroups(paste0(txt_folder,
+                            "/proteinGroups.txt"), 
+                      meta,
+                      measure.cols = measure_columns),
+         peptides = pepdat, meta, measure.cols = measure_columns
+         )
+}
+load_MaxQuanttest <- function(txt_folder){
+  ### This function takes the txt folder, 
+  ### the name of the measure_col,number of samples and 
+  ### names of the conditions and output the peptides per condition and 
+  ### reads the Maxquant proteinGroups file
+  #txt_folder <-  here::here("Datasets", "Raw",
+  #                         "2020MQ044txt",
+  #                        "txt")
+
+  condition_names <-  c(1:9)
+  samples_ids <-  1:9
+  measure_col <-  "Intensity"
+
+  measCols <- list(
+    HL = measure_col
+  )
+
+  eviCols <- list(
+    sequence = 'Sequence',
+    modified_sequence = 'Modified sequence',
+    modifications = 'Modifications',
+    protein_group = 'Proteins',
+    protein = 'Leading razor protein',
+    experiment = 'Experiment',
+    charge = 'Charge',
+    reverse = 'Reverse',
+    contaminant = 'Potential contaminant'
+  )
+  evi <- proteus::readEvidenceFile(paste0(txt_folder,
+                                          "/evidence.txt"), 
+                                   measure.cols=measCols, 
+                                   data.cols=eviCols, 
+                                   zeroes.are.missing=FALSE)
+
+
+
+  meta <- data.frame(experiment = sort(evi$experiment %>% unique()),
+                     measure = measure_col,
+                     condition = condition_names,
+                     sample = sort(evi$experiment %>% unique()),
+                     replicate = 1,1,1,2,2,2,3,3,3)
+
+  pepdat <- proteus::makePeptideTable(evi, 
+                                      meta,
+                                      measure.cols=measCols, 
+                                      experiment.type="label-free")
+
+  measure_columns <- list(paste(measure_col,samples_ids, sep = " " )) %>% unlist %>%
+    setNames(paste("Sample", samples_ids, sep = "_"))
+
+  list(proteins = proteus::readProteinGroups(paste0(txt_folder,
+                                                    "/proteinGroups.txt"), 
+                                             meta,
+                                             measure.cols = measure_columns),
+       peptides = pepdat, meta, measure.cols = measure_columns
+  )
+}
+
+
+is_function = function (expr) {
+    if (! is_assign(expr))
+        return(FALSE)
+    value = expr[[3]]
+    is.call(value) && as.character(value[[1]]) == 'function'
+}
+
+function_name = function (expr){
+    as.character(expr[[2]])}
+
+is_assign = function (expr){
+    is.call(expr) && as.character(expr[[1]]) %in% c('=', '<-', 'assign')}
+
+
+produce_ipath_map <- function(df,
+                              column_name = NULL){
+    ### takes as input a dataframe produced which converts ratios to colours
+    ### and the columns to map and produces iPath3 images 
+    #df = For_ipath_comparisons
+    #column_name = columns_for_mapping[1]
+
+    Filtered_proteins <- df %>% 
+        dplyr::select(all_of(c("V1","Width",column_name))) %>% 
+        na.omit()
+
+    #Filtered_proteins$Width <- paste("W",as.character(ntile(Filtered_proteins[,i], 30)),sep="")
+    Filtered_proteins$Ipath <- paste(Filtered_proteins$V1, 
+                                     Filtered_proteins %>% pull(column_name), 
+                                     Filtered_proteins$Width)
+
+    file_name <- here::here("Project_Output",
+                      paste0(column_name,
+                             "_ipath_all_enzymes_bins.tsv"))
+
+    selections <- paste('selection=',
+                        paste(paste(Filtered_proteins$Ipath,
+                                    "%0A ",sep = ""),
+                              collapse = " "),
+                        collapse = " ")
+
+    export_type <- "export_type=svg"
+
+    file_ipath <- paste("./../Project_Output/",
+                        column_name,"_ipath_all_enzymes_bins.svg",
+                        sep="")
+
+    ipath_command <- paste(paste("curl -d ",
+                                 selections, " -d ", 
+                                 export_type, 
+                                 " https://pathways.embl.de/mapping.cgi -o ", 
+                                 sep = '\"'),
+                           file_ipath,
+                           sep="")
+    system(ipath_command)
+    print(column_name)
+    write_tsv(Filtered_proteins,file_name,col_names = FALSE)
+    #pacman::p_load("rsvg")
+    bitmap <- rsvg::rsvg_raw( here::here("Project_Output",
+                                   paste0(column_name,"_ipath_all_enzymes_bins.svg")),
+                              width = 3600)
+
+    cowplot::ggdraw()+cowplot::draw_image(bitmap)+ cowplot::draw_figure_label(column_name,"top", size = 65)
+
+
+}
+
+calculateCoveredProtein_sav <- function (sample_id,proteinIDs, markerproteins) 
+{
+    compartments <- c("S1", "S2", "S3", "S4", "N1", "N2", "N3", 
+                      "N4", "C1", "C2", "C3", "C4", "C5", "M1", "M2")
+    color.code <- c("gold", "orange", "salmon", "tomato2", "grey90", 
+                    "grey70", "grey50", "grey30", "lightblue", "aquamarine", 
+                    "cyan", "deepskyblue2", "turquoise3", "burlywood4", 
+                    "tan4")
+    compartment.size <- c(358, 351, 252, 174, 192, 121, 231, 
+                          198, 242, 132, 220, 215, 341, 69, 269)
+    covered.proteins <- intersect(proteinIDs, markerproteins)
+    if (length(covered.proteins) < 1) 
+        warning("There is no overlap between marker proteins and data!")
+    c.marker.df <- SubCellBarCode::markerProteins[covered.proteins, 
+    ]
+    coverageCompWise <- lapply(seq_len(length(compartments)), 
+                               function(x) {
+                                   temp.df <- c.marker.df[c.marker.df$Compartments == 
+                                                              compartments[x], ]
+                                   values <- list(Compartments = compartments[x], ColorCode = color.code[x], 
+                                                  ProteinCoverage = 100 * ((dim(temp.df)[1])/compartment.size[x]))
+                               })
+    coverage.df <- as.data.frame(do.call("rbind", coverageCompWise))
+    non.enriched.loc <- coverage.df[coverage.df$ProteinCoverage < 
+                                        20, ]
+    if (nrow(non.enriched.loc) == 1) {
+        warning("There is not enough enrichment at: ", as.character(non.enriched.loc$Compartments), 
+                "\nWe recommend you to perform the fractionation, again.")
+    }
+    else if (nrow(non.enriched.loc) > 1) {
+        comp <- paste(as.character(non.enriched.loc$Compartments), 
+                      collapse = ",")
+        warning("There are not enough enrichments at: ", comp, 
+                "\nWe recommend you to perform the fractionation!")
+    }
+    coverage.df$ProteinCoverage <- as.numeric(coverage.df$ProteinCoverage)
+    coverage.df$Compartments <- as.character(coverage.df$Compartments)
+    coverage.df$ColorCode <- as.character(coverage.df$ColorCode)
+    ggplot(data = coverage.df, aes(x = coverage.df$Compartments, 
+                                         y = coverage.df$ProteinCoverage)) + geom_bar(stat = "identity", 
+                                                                                      fill = coverage.df$ColorCode) + 
+        scale_x_discrete(limits = c(compartments)) + 
+              theme_bw() + theme(text = element_text(size = 5), plot.title = element_text(hjust = 0.5), 
+                                 axis.text.x = element_text(face = "bold", color = "black"), 
+                                 axis.text.y = element_text(face = "bold", color = "black")) +
+        ylim(0, 100)+
+
+              labs(title = paste("Marker Protein Coverage Sample",sample_id), 
+                   y = "% Protein Coverage", x = "Compartment")
+
+}

Codes/functions_minimal.R

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+pacman::p_load("biglm",
+               "readxl",
+               "rmarkdown",
+               "tarchetypes",
+               "targets",
+               "tidyverse")
+
+#' @title Plot ozone from the preprocessed air quality data.
+#' @description Plot a histogram of ozone concentration.
+#' @return A ggplot histogram showing ozone content.
+#' @param data Data frame, preprocessed air quality dataset.
+#' @examples
+#' library(ggplot2)
+#' library(tidyverse)
+#' data <- airquality %>%
+#'   mutate(Ozone = replace_na(Ozone, mean(Ozone, na.rm = TRUE)))
+#' create_plot(data)
+create_plot <- function(data) {
+  ggplot(data) +
+    geom_histogram(aes(x = Ozone), bins = 12) +
+    theme_gray(24)+
+        ggtitle("Hist_1")
+}
+
+####
+
+

Codes/tests/testthat/test_functions.R

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+source_file_name <- here::here("Codes",
+                               "functions.R")
+source(source_file_name)
+library(testthat)
+
+
+test_that("addition results is correct",{
+    expect_true(is.data.frame(addition(5,6) %>% as.data.frame()))
+    testthat::expect_error(addition("ab","cd"))
+})
+

Output/report.Rmd

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+---
+title: report
+output: html_document
+---
+
+```{r}
+library(targets)
+tar_read(dataset)
+tar_read(dataset)
+tar_read(hist) 
+```
+I have seen the hist, and it shows....
+
+```{r}
+
+```
+

Translation.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 4
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX