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.quarto/_freeze/FluorPLA/execute-results/html.json

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+{
+  "hash": "127e0c74e08b57523f8019ce51a35998",
+  "result": {
+    "markdown": "---\ntitle: \"FluorPLA\"\n---\n\n\n## Quarto\n\nQuarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see <https://quarto.org>.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n",
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.quarto/_freeze/supplementary_fig02/execute-results/html.json

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+{
+  "hash": "ee3e66bb65c1abd229894992e738c549",
+  "result": {
+    "markdown": "---\ntitle: \"supplementary_fig02\"\nauthor: \"Daniel Fürth\"\nformat: html\n---\n\n\n## Create a table of raw data files\n\nThe raw data text files are in the folder `./data/lc/`.\nLets list the content of that folder:\n\n::: {.cell}\n\n```{.r .cell-code}\nfolder <- './data/lc'\nfiles <- dir(folder)\nfiles\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] \"Azido_PEG_tetrazine_chromatogram_280nm.txt\"\n [2] \"Azido_PEG_tetrazine_MS.txt\"                \n [3] \"DBCO488_chromatogram_280nm.txt\"            \n [4] \"DBCO488_MS.txt\"                            \n [5] \"DBCO594_chromatogram_280nm.txt\"            \n [6] \"DBCO594_MS.txt\"                            \n [7] \"RXN488_chromatogram_280nm.txt\"             \n [8] \"RXN488_MS.txt\"                             \n [9] \"RXN594_chromatogram_280nm.txt\"             \n[10] \"RXN594_MS.txt\"                             \n```\n:::\n:::\n\nWe have two types of files: `MS` and `chromatogram`. Lets check the presence of string `MS` and `chromatogram` in the filenames using `grepl()` command and make a boolean index based on it:\n\n::: {.cell}\n\n```{.r .cell-code}\nms <- grepl(\"MS\", files)\nchromatogram <- grepl(\"chromatogram\", files)\n```\n:::\n\nLets make a data frame object, `myfiles`, that stores all info about our files including the full file path:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles<-data.frame(folder, files, ms, chromatogram)\n#show the data frame in console\nmyfiles\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n      folder                                      files    ms chromatogram\n1  ./data/lc Azido_PEG_tetrazine_chromatogram_280nm.txt FALSE         TRUE\n2  ./data/lc                 Azido_PEG_tetrazine_MS.txt  TRUE        FALSE\n3  ./data/lc             DBCO488_chromatogram_280nm.txt FALSE         TRUE\n4  ./data/lc                             DBCO488_MS.txt  TRUE        FALSE\n5  ./data/lc             DBCO594_chromatogram_280nm.txt FALSE         TRUE\n6  ./data/lc                             DBCO594_MS.txt  TRUE        FALSE\n7  ./data/lc              RXN488_chromatogram_280nm.txt FALSE         TRUE\n8  ./data/lc                              RXN488_MS.txt  TRUE        FALSE\n9  ./data/lc              RXN594_chromatogram_280nm.txt FALSE         TRUE\n10 ./data/lc                              RXN594_MS.txt  TRUE        FALSE\n```\n:::\n\n```{.r .cell-code}\n#get the names of the the variables\nnames(myfiles)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"folder\"       \"files\"        \"ms\"           \"chromatogram\"\n```\n:::\n:::\n\nadd full file paths:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles$path<-dir(folder, full.names=TRUE)\n#get the names of the the variables\nnames(myfiles)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"folder\"       \"files\"        \"ms\"           \"chromatogram\" \"path\"        \n```\n:::\n:::\n\n\n## Running Code\n\nWhen you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n\n\nYou can add options to executable code like this \n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\n[1] 4\n```\n:::\n:::\n\n\nThe `echo: false` option disables the printing of code (only output is displayed).\n",
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.quarto/_freeze/supplementary_fig02/execute-results/md.json

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+    "markdown": "---\ntitle: \"supplementary_fig02\"\nauthor: \"Daniel Fürth\"\nformat: gfm\n---\n\n\n## Create a table of raw data files\n\nThe raw data text files are in the folder `./data/lc/`.\nLets list the content of that folder:\n\n::: {.cell}\n\n```{.r .cell-code}\nfolder <- './data/lc'\nfiles <- dir(folder)\nfiles\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] \"Azido_PEG_tetrazine_chromatogram_280nm.txt\"\n [2] \"Azido_PEG_tetrazine_MS.txt\"                \n [3] \"DBCO488_chromatogram_280nm.txt\"            \n [4] \"DBCO488_MS.txt\"                            \n [5] \"DBCO594_chromatogram_280nm.txt\"            \n [6] \"DBCO594_MS.txt\"                            \n [7] \"RXN488_chromatogram_280nm.txt\"             \n [8] \"RXN488_MS.txt\"                             \n [9] \"RXN594_chromatogram_280nm.txt\"             \n[10] \"RXN594_MS.txt\"                             \n```\n:::\n:::\n\nWe have two types of files: `MS` and `chromatogram`. Lets check the presence of string `MS` and `chromatogram` in the filenames using `grepl()` command and make a boolean index based on it:\n\n::: {.cell}\n\n```{.r .cell-code}\nms <- grepl(\"MS\", files)\nchromatogram <- grepl(\"chromatogram\", files)\n```\n:::\n\nLets make a data frame object, `myfiles`, that stores all info about our files including the full file path:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles<-data.frame(folder, files, ms, chromatogram)\n#show the data frame in console\nmyfiles\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n      folder                                      files    ms chromatogram\n1  ./data/lc Azido_PEG_tetrazine_chromatogram_280nm.txt FALSE         TRUE\n2  ./data/lc                 Azido_PEG_tetrazine_MS.txt  TRUE        FALSE\n3  ./data/lc             DBCO488_chromatogram_280nm.txt FALSE         TRUE\n4  ./data/lc                             DBCO488_MS.txt  TRUE        FALSE\n5  ./data/lc             DBCO594_chromatogram_280nm.txt FALSE         TRUE\n6  ./data/lc                             DBCO594_MS.txt  TRUE        FALSE\n7  ./data/lc              RXN488_chromatogram_280nm.txt FALSE         TRUE\n8  ./data/lc                              RXN488_MS.txt  TRUE        FALSE\n9  ./data/lc              RXN594_chromatogram_280nm.txt FALSE         TRUE\n10 ./data/lc                              RXN594_MS.txt  TRUE        FALSE\n```\n:::\n\n```{.r .cell-code}\n#get the names of the the variables\nnames(myfiles)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"folder\"       \"files\"        \"ms\"           \"chromatogram\"\n```\n:::\n:::\n\nadd full file paths:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles$path<-dir(folder, full.names=TRUE)\n#get the names of the the variables\nnames(myfiles)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"folder\"       \"files\"        \"ms\"           \"chromatogram\" \"path\"        \n```\n:::\n:::\n\n\n## Import the data\n\nNext lets import a chromatogram file. We can open the file manually and find that on line 76 we have the text `Raw data:` followed by the data in TSV format. \n```\n76    Raw Data:\n77    Time(min)\tStep(sec)\tValue(mAU)\n78    0.000000\t0.00\t0.000000\t\n79    0.001667\t0.10\t0.000030\t\n80    0.003333\t0.10\t0.000580\t\n81    0.005000\t0.10\t0.002410\t\n```\nWe can simply then import the data into a data frame by adding `skip = 76` and the `read.table()`command will skip all the line suntil line 77. If we also add `header=TRUE` we will import the variable names into the header of the data frame.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata<-read.table(myfiles$path[1], skip=76, header=TRUE)\nhead(data) #just show the first 6 lines of the table, tail() command shows the last 6 lines.\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n  Time.min. Step.sec. Value.mAU.\n1  0.000000       0.0    0.00000\n2  0.001667       0.1    0.00003\n3  0.003333       0.1    0.00058\n4  0.005000       0.1    0.00241\n5  0.006667       0.1    0.00534\n6  0.008333       0.1    0.00690\n```\n:::\n:::\n\n\nLets plot the chromatogram as a line plot:\n\n::: {.cell}\n\n```{.r .cell-code}\nplot(data$Time.min., data$Value.mAU., type='l')\n```\n\n::: {.cell-output-display}\n![](supplementary_fig02_files/figure-commonmark/unnamed-chunk-6-1.png)\n:::\n:::\n\n\n# Make a master data frame with all data.\n\nLets examine the different files we have from the chromatogram:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles[myfiles$chromatogram,]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n     folder                                      files    ms chromatogram\n1 ./data/lc Azido_PEG_tetrazine_chromatogram_280nm.txt FALSE         TRUE\n3 ./data/lc             DBCO488_chromatogram_280nm.txt FALSE         TRUE\n5 ./data/lc             DBCO594_chromatogram_280nm.txt FALSE         TRUE\n7 ./data/lc              RXN488_chromatogram_280nm.txt FALSE         TRUE\n9 ./data/lc              RXN594_chromatogram_280nm.txt FALSE         TRUE\n                                                  path\n1 ./data/lc/Azido_PEG_tetrazine_chromatogram_280nm.txt\n3             ./data/lc/DBCO488_chromatogram_280nm.txt\n5             ./data/lc/DBCO594_chromatogram_280nm.txt\n7              ./data/lc/RXN488_chromatogram_280nm.txt\n9              ./data/lc/RXN594_chromatogram_280nm.txt\n```\n:::\n:::\n\nWe can see that the sample name preceeds the first `_` in the file name so lets add a variable `sample` with that text string:\n\n::: {.cell}\n\n```{.r .cell-code}\n#split the string by _ character\nunderscore_split<-strsplit( myfiles$files, \"_\")\nunderscore_split\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[[1]]\n[1] \"Azido\"        \"PEG\"          \"tetrazine\"    \"chromatogram\" \"280nm.txt\"   \n\n[[2]]\n[1] \"Azido\"     \"PEG\"       \"tetrazine\" \"MS.txt\"   \n\n[[3]]\n[1] \"DBCO488\"      \"chromatogram\" \"280nm.txt\"   \n\n[[4]]\n[1] \"DBCO488\" \"MS.txt\" \n\n[[5]]\n[1] \"DBCO594\"      \"chromatogram\" \"280nm.txt\"   \n\n[[6]]\n[1] \"DBCO594\" \"MS.txt\" \n\n[[7]]\n[1] \"RXN488\"       \"chromatogram\" \"280nm.txt\"   \n\n[[8]]\n[1] \"RXN488\" \"MS.txt\"\n\n[[9]]\n[1] \"RXN594\"       \"chromatogram\" \"280nm.txt\"   \n\n[[10]]\n[1] \"RXN594\" \"MS.txt\"\n```\n:::\n:::\n\nHere `underscore_split` is a list object where each file name is an entry in the list and it then has a nested list within where text between each \"_\" is entered as a single entry. We can then extract the first entry from each list using `lapply()`\"list apply\" function:\n\n::: {.cell}\n\n```{.r .cell-code}\n# Create a new list 'sample.name' by applying a function to each element of 'underscore_split'\nsample.name <- lapply(underscore_split, function(x) {\n  return(x[1])  # Return the first element (index 1) of each element in 'underscore_split'\n})\n\n# Display the resulting 'sample.name' list\nsample.name\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[[1]]\n[1] \"Azido\"\n\n[[2]]\n[1] \"Azido\"\n\n[[3]]\n[1] \"DBCO488\"\n\n[[4]]\n[1] \"DBCO488\"\n\n[[5]]\n[1] \"DBCO594\"\n\n[[6]]\n[1] \"DBCO594\"\n\n[[7]]\n[1] \"RXN488\"\n\n[[8]]\n[1] \"RXN488\"\n\n[[9]]\n[1] \"RXN594\"\n\n[[10]]\n[1] \"RXN594\"\n```\n:::\n\n```{.r .cell-code}\n# Check class of the object\nclass(sample.name)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"list\"\n```\n:::\n:::\n\nHere `sample.name`is a object with the class list. If we now want to go from list object to a character vector to insert it into our data frame we can do like this:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles$sample<-unlist(sample.name)\nmyfiles\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n      folder                                      files    ms chromatogram\n1  ./data/lc Azido_PEG_tetrazine_chromatogram_280nm.txt FALSE         TRUE\n2  ./data/lc                 Azido_PEG_tetrazine_MS.txt  TRUE        FALSE\n3  ./data/lc             DBCO488_chromatogram_280nm.txt FALSE         TRUE\n4  ./data/lc                             DBCO488_MS.txt  TRUE        FALSE\n5  ./data/lc             DBCO594_chromatogram_280nm.txt FALSE         TRUE\n6  ./data/lc                             DBCO594_MS.txt  TRUE        FALSE\n7  ./data/lc              RXN488_chromatogram_280nm.txt FALSE         TRUE\n8  ./data/lc                              RXN488_MS.txt  TRUE        FALSE\n9  ./data/lc              RXN594_chromatogram_280nm.txt FALSE         TRUE\n10 ./data/lc                              RXN594_MS.txt  TRUE        FALSE\n                                                   path  sample\n1  ./data/lc/Azido_PEG_tetrazine_chromatogram_280nm.txt   Azido\n2                  ./data/lc/Azido_PEG_tetrazine_MS.txt   Azido\n3              ./data/lc/DBCO488_chromatogram_280nm.txt DBCO488\n4                              ./data/lc/DBCO488_MS.txt DBCO488\n5              ./data/lc/DBCO594_chromatogram_280nm.txt DBCO594\n6                              ./data/lc/DBCO594_MS.txt DBCO594\n7               ./data/lc/RXN488_chromatogram_280nm.txt  RXN488\n8                               ./data/lc/RXN488_MS.txt  RXN488\n9               ./data/lc/RXN594_chromatogram_280nm.txt  RXN594\n10                              ./data/lc/RXN594_MS.txt  RXN594\n```\n:::\n:::\n\nYou can see how we now have a variable `myfiles$sample` with all the sample names:\n\n::: {.cell}\n\n```{.r .cell-code}\nmyfiles$sample\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] \"Azido\"   \"Azido\"   \"DBCO488\" \"DBCO488\" \"DBCO594\" \"DBCO594\" \"RXN488\" \n [8] \"RXN488\"  \"RXN594\"  \"RXN594\" \n```\n:::\n:::\n\nSome stats, showing that each sample has two files (one `MS` and one `chromatogram`):\n\n::: {.cell}\n\n```{.r .cell-code}\ntable(myfiles$sample)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n  Azido DBCO488 DBCO594  RXN488  RXN594 \n      2       2       2       2       2 \n```\n:::\n:::\n\n\nLets create a new data frame called `trace` where we have all the traces from each experiment into one file:\n\n::: {.cell}\n\n```{.r .cell-code}\n#create an empty data frame where we will store our data\ntrace<-data.frame(sample = character(), time = numeric(), value = numeric())\n\n#loop through each chromatogram file and load it in and add it to trace data frame\nfor(i in which(myfiles$chromatogram)){\n  tmp<-read.table(myfiles$path[i], skip=76, header=TRUE)\n  \n  trace.tmp<-data.frame(\n                        sample = myfiles$sample[i], \n                        time = tmp$Time.min, \n                        value = tmp$Value.mAU\n                        )\n  \n  #add the just loaded file into master data frame, trace\n  trace <- rbind(trace, trace.tmp)\n}\n```\n:::\n\n\nLets check the new data frame `trace`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntable(trace$sample)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n  Azido DBCO488 DBCO594  RXN488  RXN594 \n   1801    1801    1801    1801    1801 \n```\n:::\n:::\n\nSo we have each sample loaded with 1801 measurements in each.\n\n# Make a plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmysamples<- rev(unique(trace$sample)) #reverse order because we want reactions RXN at bottom so one reads the graph top to bottom.\n\nscale.factor.y <- 1.2 #adds some space between lines when we stack them on top. \ntext.under.y <- 0.2 #placement of the text label under the trace. \ntext.under.x <- 0.1 #placement of the text label under the trace. \n\n\nplot(trace$time[trace$sample == mysamples[1]], \n     trace$value[trace$sample == mysamples[1]], \n     type='l',\n     xlab='Time',\n     ylab='',\n     ylim=c(-max(trace$value)*text.under.y, scale.factor.y*max(trace$value)*length(mysamples))\n     )\n\nfor(i in seq_along(mysamples)){\n  #k is a variable that adds some space for sample 3 and above so the two reactions are vertically seperated from the rest.\n  if(i > 2){\n    k <- 280\n  }else{\n    k <- 0\n  }\n  lines(trace$time[trace$sample == mysamples[i]], \n     trace$value[trace$sample == mysamples[i]]+scale.factor.y*max(trace$value)*(i-1)+k, col=i )\n  \n  text(max(trace$time)*text.under.x, scale.factor.y*max(trace$value)*(i-1)-max(trace$value)*text.under.y+k, mysamples[i], col=i)\n}\n```\n\n::: {.cell-output-display}\n![](supplementary_fig02_files/figure-commonmark/unnamed-chunk-15-1.png)\n:::\n:::\n",
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