Yu_Pekkurnaz_MitoOGlcNAc_ProteomicAnalyss_02012022_withLimmaAndEnrichment.ipynb

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    "## Yu Pekkurnaz Mitochondrial Mass Spectrometry Data Analysis\n",
    "\n",
    "#### Thomas Whisenant, CCBB, UCSD (twhisenant@ucsd.edu)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a id='toc'></a>\n",
    "## Table of Contents\n",
    "1. [Background](#background)\n",
    "2. [Input Parameters](#param)\n",
    "3. [Environment Set-Up](#environ)\n",
    "4. [Data Loading and Pre-Processing](#load)\n",
    "5. [Import Perseus Processed Data](#import)\n",
    "6. [Testing for Differential Binding](#diffbind)\n",
    "7. [Overlap Analysis](#overlap)\n",
    "8. [Enrichment Analysis](#enrich)\n",
    "9. [Generate Plots for Figures](#GenerateFigPlots)\n",
    "10. [References](#ref)\n",
    "11. [Appendix: R Session Info](#app)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background\n",
    "<a id='background'></a>\n",
    "The data includes 6 proteomic data points from two different treatments of isolated mitochondria. The proteins extracted from the mitochondria have been enriched for O-GlcNac conjugated proteins. The two treatments are +/- TMG. \n",
    "\n",
    "Samples were processed and analyzed by the UCSD Mass Spectrometry Core Facility. Quantitative peptide analysis was performed using PEAKS Studio 8.5. The manual can be found here: https://www.bioinfor.com/wp-content/uploads/2017/08/PEAKS_Studio_85_Manual.pdf\n",
    "\n",
    "[Table of Contents](#toc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input Parameters\n",
    "<a id='param'></a>\n",
    "Data Provided:\n",
    "\n",
    "For each set of 3 replicates per sample group (matched):\n",
    "\n",
    "* 3 individual folders with label free quantification results for pairs of samples (+/- TMG) from each mass spectrometry run.\n",
    "\n",
    "* In each quantification folder, there are two relevant files:\n",
    "    1. protein-peptides.csv - list of peptides for each sample \n",
    "    2. proteins.csv - list of proteins and their quantifications for each samples\n",
    "    3. peptides.html - list/images of peptides for each sample \n",
    "    4. proteins.html - list/images of proteins and their quantifications for each samples\n",
    "\n",
    "\n",
    "[Table of Contents](#toc)"
   ]
  },
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      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>SampleName</th><th scope=col>MassSpecName</th><th scope=col>RunDate</th><th scope=col>Treatment</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>P1_A</td><td>SampleA  </td><td>20180531</td><td>plusTMG </td></tr>\n",
       "\t<tr><td>P1_B</td><td>SampleB  </td><td>20180531</td><td>minusTMG</td></tr>\n",
       "\t<tr><td>P2_A</td><td>SampleA  </td><td>20180710</td><td>plusTMG </td></tr>\n",
       "\t<tr><td>P2_B</td><td>SampleB  </td><td>20180710</td><td>minusTMG</td></tr>\n",
       "\t<tr><td>P3_A</td><td>Gul_A_HCD</td><td>20180816</td><td>plusTMG </td></tr>\n",
       "\t<tr><td>P3_B</td><td>Gul_B_HCD</td><td>20180816</td><td>minusTMG</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " SampleName & MassSpecName & RunDate & Treatment\\\\\n",
       " <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t P1\\_A & SampleA   & 20180531 & plusTMG \\\\\n",
       "\t P1\\_B & SampleB   & 20180531 & minusTMG\\\\\n",
       "\t P2\\_A & SampleA   & 20180710 & plusTMG \\\\\n",
       "\t P2\\_B & SampleB   & 20180710 & minusTMG\\\\\n",
       "\t P3\\_A & Gul\\_A\\_HCD & 20180816 & plusTMG \\\\\n",
       "\t P3\\_B & Gul\\_B\\_HCD & 20180816 & minusTMG\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| SampleName &lt;chr&gt; | MassSpecName &lt;chr&gt; | RunDate &lt;chr&gt; | Treatment &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| P1_A | SampleA   | 20180531 | plusTMG  |\n",
       "| P1_B | SampleB   | 20180531 | minusTMG |\n",
       "| P2_A | SampleA   | 20180710 | plusTMG  |\n",
       "| P2_B | SampleB   | 20180710 | minusTMG |\n",
       "| P3_A | Gul_A_HCD | 20180816 | plusTMG  |\n",
       "| P3_B | Gul_B_HCD | 20180816 | minusTMG |\n",
       "\n"
      ],
      "text/plain": [
       "  SampleName MassSpecName RunDate  Treatment\n",
       "1 P1_A       SampleA      20180531 plusTMG  \n",
       "2 P1_B       SampleB      20180531 minusTMG \n",
       "3 P2_A       SampleA      20180710 plusTMG  \n",
       "4 P2_B       SampleB      20180710 minusTMG \n",
       "5 P3_A       Gul_A_HCD    20180816 plusTMG  \n",
       "6 P3_B       Gul_B_HCD    20180816 minusTMG "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "All_meta <- data.frame(SampleName=c(\"P1_A\", \"P1_B\", \"P2_A\", \"P2_B\", \"P3_A\", \"P3_B\"),\n",
    "                       MassSpecName=c(\"SampleA\", \"SampleB\", \"SampleA\", \"SampleB\", \"Gul_A_HCD\", \"Gul_B_HCD\"),\n",
    "                       RunDate=c(\"20180531\", \"20180531\", \"20180710\", \"20180710\", \"20180816\", \"20180816\"),\n",
    "                       Treatment=c(\"plusTMG\", \"minusTMG\",\"plusTMG\", \"minusTMG\",\"plusTMG\", \"minusTMG\"))\n",
    "All_meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Environment Setup\n",
    "<a id='environ'></a>\n",
    "\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
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    "# This line prevents SVG output, which interacts badly with export to HTML\n",
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      "'getOption(\"repos\")' replaces Bioconductor standard repositories, see\n",
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   "source": [
    "#source(\"https://bioconductor.org/biocLite.R\")\n",
    "#biocLite(\"org.Hs.eg.db\")\n",
    "library(org.Rn.eg.db)\n",
    "library(limma)\n",
    "library(stringr) \n",
    "library(ggplot2)\n",
    "library(gplots)\n",
    "library(ggpubr)\n",
    "library(plyr)\n",
    "library(dplyr)\n",
    "library(RColorBrewer)\n",
    "library(org.Hs.eg.db)\n",
    "library(reshape2)\n",
    "library(ggrepel)\n",
    "library(gprofiler2)\n",
    "#library(Glimma)\n",
    "library(IRdisplay)\n",
    "EG2SYM <- toTable(org.Rn.egSYMBOL)\n",
    "EG2AltSYM <- toTable(org.Rn.egALIAS2EG)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "runLimma <- function(dat, met=meta, norm=FALSE, filt=TRUE) {\n",
    "    des <- model.matrix(~0 + met$Treatment + met$Group)\n",
    "    colnames(des) <- gsub(\"^met\\\\$\", \"\", colnames(des))\n",
    "    rownames(des) <- as.character(met$Sample)\n",
    "    cm <- makeContrasts(TreatmentB - TreatmentA, levels=des)\n",
    "    vfit <- lmFit(dat, des)\n",
    "    vfit <- contrasts.fit(vfit, contrasts=cm)\n",
    "    efit <- eBayes(vfit)\n",
    "    topTab <- topTable(efit, n=Inf, coef=1,  sort.by=\"p\")\n",
    "    topTab <- topTab[!is.na(topTab$logFC),]\n",
    "    dt <- decideTests(efit,p.value = 0.1)\n",
    "    dt[is.na(dt[,1]),1] <- 0\n",
    "    list(topTab, dt, efit)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "runGProfiler2onGL = function(gl, bkgd, name, species, outputDir, maxAdjPVal=0.05){\n",
    "    tryCatch({\n",
    "        top <- gl[!is.na(gl)]\n",
    "        top <- top[!duplicated(top)]\n",
    "        ALL_genes <- bkgd$SYMBOL\n",
    "        ALL_genes <- bkgd[!is.na(bkgd)]\n",
    "                \n",
    "        gostres <- gost(query = top, \n",
    "                        organism = species, \n",
    "                        ordered_query = FALSE, \n",
    "                        multi_query = FALSE, \n",
    "                        significant = TRUE, \n",
    "                        exclude_iea = FALSE,\n",
    "                        measure_underrepresentation = FALSE, \n",
    "                        evcodes = TRUE,\n",
    "                        user_threshold = maxAdjPVal, \n",
    "                        correction_method = \"fdr\",\n",
    "                        domain_scope = \"custom_annotated\", \n",
    "                        custom_bg = ALL_genes, \n",
    "                        numeric_ns = \"\", \n",
    "                        sources = NULL, \n",
    "                        as_short_link = FALSE)\n",
    "        \n",
    "        gpro_res <- gostres$result[,c(\"p_value\", \"term_size\", \"query_size\", \"intersection_size\", \"precision\",\"recall\", \"term_id\", \"source\", \"term_name\", \"intersection\")]\n",
    "        write.csv(gpro_res, sprintf('%s/%s_GProfiler_ORA_strict_%s.csv', outputDir, gRunName, name), row.names = FALSE)\n",
    "        \n",
    "        p <- gostplot(gostres, capped = FALSE, interactive = FALSE)\n",
    "        pp <- publish_gostplot(p, highlight_terms = gostres$result[c(1:5),], width = NA, height = NA, \n",
    "                               filename = sprintf(\"%s/%s_GProfiler_plot_ORA_strict_%s.pdf\", outputDir, gRunName, name))\n",
    "        gostres_link <- gost(query = top, \n",
    "                             organism = species, \n",
    "                             ordered_query = FALSE, \n",
    "                             multi_query = FALSE, \n",
    "                             significant = TRUE, \n",
    "                             exclude_iea = FALSE, \n",
    "                             measure_underrepresentation = FALSE, \n",
    "                             evcodes = TRUE, \n",
    "                             user_threshold = 0.05, \n",
    "                             correction_method = \"fdr\", \n",
    "                             domain_scope = \"custom_annotated\", \n",
    "                             custom_bg = ALL_genes, \n",
    "                             numeric_ns = \"\", \n",
    "                             sources = NULL, \n",
    "                            as_short_link = TRUE)\n",
    "        link_df <- data.frame(\"Contrast\" = name, \"GProfiler_interactive_link\" = gostres_link)\n",
    "        write.csv(link_df, sprintf(\"%s/%s_Gprofiler_interactive_linkdf_%s.csv\", outputDir, gRunName, name), row.names = FALSE)\n",
    "    }, error=function(e){print(paste0(\"runGProfiler2 failed for maxAdjPval <\", maxAdjPVal, \" with message:\", e))})\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loading and Pre-Processing\n",
    "<a id='load'></a>\n",
    "\n",
    "\n",
    "1. The original unprocessed protein level data file (proteins.csv) includes protein identifiers and two sets of quantification values that are the same for each sample. The LFQ quantification of the samples will be used as recommended by the creators of the software used to generate the data file (1).\n",
    "2. All the irrelevant proteins have been previously removed including: a. proteins hits from a \"Reverse\" database which can be removed by the regular expression \"^REV\". b. proteins identified as contaminants which can be removed by the regular expression \"^CON\" c. proteins flagged as \"Only identified by site\" which can be removed by filtering \"+\" values rows\n",
    "3. Each quantification dataset and set of threshold parameters will be analyzed prior to determining the best input for differential analysis\n",
    "\n",
    "We will start with the following guidelines for the analysis:\n",
    "1. The pipeline used to identify proteins found in 1 or both groups and subsequent visualizations:\n",
    "    A. Import quantification data for each individual group\n",
    "    B. log2 Transformation of the raw quantified values (+ 1.1 pseduosignal to avoid NA values)\n",
    "2. The pipeline used to generate the input for differential analysis incorporated the following steps:\n",
    "    A. Import quantification data for combined groups\n",
    "    B. log2 Transformation of the raw quantified values \n",
    "    C. Removal of proteins with limited detection\n",
    "    \n",
    "        1. Removal of proteins not identified in 50% or more of the samples in 1 or both groups \n",
    "        2. Removal of proteins not identified in 100% of samples\n",
    "    \n",
    "    D. Width Adjustment based normalization using the Perseus software \n",
    "    E. Imputation from a normal distribution for missing data points.\n",
    "\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj1 <- read.csv(file=\"./Data/proteins_Round1.csv\", header=TRUE)\n",
    "proj2 <- read.csv(file=\"./Data/proteins_Round2.csv\", header=TRUE)\n",
    "proj3 <- read.csv(file=\"./Data/proteins_Round3.csv\", header=TRUE)\n",
    "proj3 <- proj3[proj3$Significance > 0,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantification values are properly treated as intensities and intensities are usually log2-transformed in order to generate approximately normal distributions for statistical analysis. However, log2 is -infinity for zero, so it is necessary to add a small pseudosignal to all values so that none are zero. To determine what an appropriately small value is, examine the minimum non-zero value in all samples:\n",
    "\n",
    "A pseudosignal of 1.1 will therefore be miniscule even compared to the smallest non-zero signal, so 1.1 is added to all values. Then the values are log transformed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj1_log <- apply(proj1[,9:10], 2, function(x) as.numeric(gsub(\"-\", \"0\", as.character(x))) + 1.1)\n",
    "proj1_log <- round(apply(proj1_log, 2, function(x) log2(x)), 1) \n",
    "rownames(proj1_log) <- gsub(\"_RAT$\", \"\", unlist(lapply(strsplit(as.character(proj1$Accession), \"\\\\|\"), function(x) x[[2]])))\n",
    "\n",
    "proj2_log <-  apply(proj2[,9:10], 2, function(x) as.numeric(gsub(\"-\", \"0\", as.character(x))) + 1.1)\n",
    "proj2_log <- round(apply(proj2_log, 2, function(x) log2(x)), 1) \n",
    "rownames(proj2_log) <- gsub(\"_RAT$\", \"\", unlist(lapply(strsplit(as.character(proj2$Accession), \"\\\\|\"), function(x) x[[2]])))\n",
    "\n",
    "proj3_log <-  apply(proj3[,9:10], 2, function(x) as.numeric(gsub(\"-\", \"0\", as.character(x))) + 1.1)\n",
    "proj3_log <- round(apply(proj3_log, 2, function(x) log2(x)), 1) \n",
    "rownames(proj3_log) <- gsub(\"_RAT$\", \"\", unlist(lapply(strsplit(as.character(proj3$Accession), \"\\\\|\"), function(x) x[[2]])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj1_filt <- subset(proj1, Sample.1.Area != 0 | Sample.2.Area != 0)\n",
    "proj2_filt <- subset(proj2, Sample.1.Area != 0 | Sample.2.Area != 0)\n",
    "proj3_filt <- subset(proj3, Sample.2.Area != 0 | Sample.4.Area != 0)\n",
    "proj1_filtB <- proj1[proj1$X.Peptides > 1,]\n",
    "proj2_filtB <- proj2[proj2$X.Peptides > 1,]\n",
    "proj3_filtB <- proj3[proj3$X.Peptides > 1,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_proj_venninp <- as.data.frame(matrix(0, nrow=length(unique(c(as.character(proj1$Accession),\n",
    "                                                                   as.character(proj2$Accession),\n",
    "                                                                   as.character(proj3$Accession)))), ncol=3))\n",
    "merged_proj_raw <- merged_proj_raw_log<- as.data.frame(matrix(0, nrow=length(unique(c(as.character(proj1$Accession),\n",
    "                                                                as.character(proj2$Accession),\n",
    "                                                                as.character(proj3$Accession)))), ncol=6))\n",
    "merged_proj_pep <- as.data.frame(matrix(0, nrow=length(unique(c(as.character(proj1$Accession),\n",
    "                                                                as.character(proj2$Accession),\n",
    "                                                                as.character(proj3$Accession)))), ncol=3))\n",
    "rownames(merged_proj_pep) <- rownames(merged_proj_raw) <- rownames(merged_proj_venninp) <- \n",
    "rownames(merged_proj_raw_log) <- unique(c(as.character(proj1$Accession),as.character(proj2$Accession),\n",
    "                                            as.character(proj3$Accession)))\n",
    "colnames(merged_proj_pep) <- colnames(merged_proj_venninp) <- c(\"Project1\", \"Project2\", \"Project3\")\n",
    "colnames(merged_proj_raw) <- colnames(merged_proj_raw_log) <- c(\"P1_A\", \"P1_B\", \"P2_A\", \"P2_B\", \"P3_A\", \"P3_B\")\n",
    "\n",
    "merged_proj_pep[which(rownames(merged_proj_pep) %in% as.character(proj1$Accession)), 1] <-  proj1$X.Peptides[match(rownames(merged_proj_pep)[which(rownames(merged_proj_pep) %in% \n",
    "                                                                                            as.character(proj1$Accession))], as.character(proj1$Accession))]\n",
    "merged_proj_pep[which(rownames(merged_proj_pep) %in% as.character(proj2$Accession)), 2] <-  proj2$X.Peptides[match(rownames(merged_proj_pep)[which(rownames(merged_proj_pep) %in% \n",
    "                                                                                            as.character(proj2$Accession))], as.character(proj2$Accession))]\n",
    "merged_proj_pep[which(rownames(merged_proj_pep) %in% as.character(proj3$Accession)), 3] <-  proj3$X.Peptides[match(rownames(merged_proj_pep)[which(rownames(merged_proj_pep) %in% \n",
    "                                                                                            as.character(proj3$Accession))], as.character(proj3$Accession))]\n",
    "\n",
    "merged_proj_venninp[which(rownames(merged_proj_venninp) %in% as.character(proj1$Accession)), 1] <-  1\n",
    "merged_proj_venninp[which(rownames(merged_proj_venninp) %in% as.character(proj2$Accession)), 2] <-  1\n",
    "merged_proj_venninp[which(rownames(merged_proj_venninp) %in% as.character(proj3$Accession)), 3] <-  1\n",
    "\n",
    "merged_proj_raw[which(rownames(merged_proj_raw) %in% as.character(proj1$Accession)), 1] <-  proj1$Sample.1.Area[match(rownames(merged_proj_raw)[which(rownames(merged_proj_raw) %in% \n",
    "                                                                                            as.character(proj1$Accession))], as.character(proj1$Accession))]\n",
    "merged_proj_raw[which(rownames(merged_proj_raw) %in% as.character(proj1$Accession)), 2] <-  proj1$Sample.2.Area[match(rownames(merged_proj_raw)[which(rownames(merged_proj_raw) %in% \n",
    "                                                                                            as.character(proj1$Accession))], as.character(proj1$Accession))]\n",
    "merged_proj_raw[which(rownames(merged_proj_raw) %in% as.character(proj2$Accession)), 3] <-  proj2$Sample.1.Area[match(rownames(merged_proj_raw)[which(rownames(merged_proj_raw) %in%\n",
    "                                                                                            as.character(proj2$Accession))], as.character(proj2$Accession))]\n",
    "merged_proj_raw[which(rownames(merged_proj_raw) %in% as.character(proj2$Accession)), 4] <-  proj2$Sample.2.Area[match(rownames(merged_proj_raw)[which(rownames(merged_proj_raw) %in% \n",
    "                                                                                            as.character(proj2$Accession))], as.character(proj2$Accession))]\n",
    "merged_proj_raw[which(rownames(merged_proj_raw) %in% as.character(proj3$Accession)), 5] <-  proj3$Sample.2.Area[match(rownames(merged_proj_raw)[which(rownames(merged_proj_raw) %in% \n",
    "                                                                                            as.character(proj3$Accession))], as.character(proj3$Accession))]\n",
    "merged_proj_raw[which(rownames(merged_proj_raw) %in% as.character(proj3$Accession)), 6] <-  proj3$Sample.4.Area[match(rownames(merged_proj_raw)[which(rownames(merged_proj_raw) %in% \n",
    "                                                                                            as.character(proj3$Accession))], as.character(proj3$Accession))]\n",
    "merged_proj_raw_log[which(rownames(merged_proj_raw_log) %in% as.character(proj1$Accession)), 1] <-  proj1_log[match(rownames(merged_proj_raw_log)[which(rownames(merged_proj_raw_log) %in% \n",
    "                                                                                                                      as.character(proj1$Accession))], as.character(proj1$Accession)), 1]\n",
    "merged_proj_raw_log[which(rownames(merged_proj_raw_log) %in% as.character(proj1$Accession)), 2] <-  proj1_log[match(rownames(merged_proj_raw_log)[which(rownames(merged_proj_raw_log) %in% \n",
    "                                                                                                                      as.character(proj1$Accession))], as.character(proj1$Accession)), 2]\n",
    "merged_proj_raw_log[which(rownames(merged_proj_raw_log) %in% as.character(proj2$Accession)), 3] <-  proj2_log[match(rownames(merged_proj_raw_log)[which(rownames(merged_proj_raw_log) %in%\n",
    "                                                                                                                      as.character(proj2$Accession))], as.character(proj2$Accession)), 1]\n",
    "merged_proj_raw_log[which(rownames(merged_proj_raw_log) %in% as.character(proj2$Accession)), 4] <-  proj2_log[match(rownames(merged_proj_raw_log)[which(rownames(merged_proj_raw_log) %in% \n",
    "                                                                                                                      as.character(proj2$Accession))], as.character(proj2$Accession)), 2]\n",
    "merged_proj_raw_log[which(rownames(merged_proj_raw_log) %in% as.character(proj3$Accession)), 5] <-  proj3_log[match(rownames(merged_proj_raw_log)[which(rownames(merged_proj_raw_log) %in% \n",
    "                                                                                                                      as.character(proj3$Accession))], as.character(proj3$Accession)), 1]\n",
    "merged_proj_raw_log[which(rownames(merged_proj_raw_log) %in% as.character(proj3$Accession)), 6] <-  proj3_log[match(rownames(merged_proj_raw_log)[which(rownames(merged_proj_raw_log) %in% \n",
    "                                                                                                                      as.character(proj3$Accession))], as.character(proj3$Accession)), 2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter for proteins with more than one unique peptide in at least one experiment\n",
    "filt_listB <- unique(c(as.character(proj1_filtB$Accession), as.character(proj2_filtB$Accession), as.character(proj3_filtB$Accession)))\n",
    "merged_proj_pep_filtB <- merged_proj_pep[rownames(merged_proj_pep) %in% filt_listB,]\n",
    "merged_proj_raw_filtB <- merged_proj_raw[rownames(merged_proj_raw) %in% filt_listB,]\n",
    "merged_proj_venninp_filtB <- merged_proj_venninp[rownames(merged_proj_venninp) %in% filt_listB,]\n",
    "merged_proj_raw_log_filtB <- merged_proj_raw_log[rownames(merged_proj_raw_log) %in% filt_listB,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ## Export data for Perseus Processing/Import Perseus Processed Data\n",
    "<a id='import'></a>\n",
    "\n",
    "Due to the presence of available stand alone software, Perseus (Tyanova S. et al. 2016), for analyzing data produced by MaxQuant, some processing of the data was done in parallel. Multiple files were produced and will be imported for evaluation of the results. With each of the subsets of proteins described above, the next step was to perform log transformation on the raw values. This was followed by the process of normalization by width adjustment and imputation of missing values based on a normal distribution with mean and standard deviation taken from the remaining data. The normalization and imputation steps were each performed in both possible order on each dataset resulting in 12 total files (6 subsets x 2). \n",
    "\n",
    "Finally, a statistical comparison by Student's t-test was made between the two groups for each protein. Benjamini-Hochberg adjusted p-values were then calculated from the resulting p-values in order to correct for multiple testing.\n",
    "\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "d <- dir(pattern=\"_ImpOnly\", path=\"./Data\", full.names=TRUE)\n",
    "d2 <- gsub(\"_ImpOnly_out.txt$\", \"\", gsub(\"YuPekkurnaz_MitoOGlcNAc_Proteomics_\", \"X\", dir(pattern=\"_ImpOnly\")))u\n",
    "perInpImpOnly.l <- list()\n",
    "for (i in 1:length(d)) {\n",
    "    f <- read.csv(file=d[i], sep=\"\\t\", header=TRUE, stringsAsFactors=FALSE)\n",
    "    perInpImpOnly.l[[i]] <- f[-c(1:3),]\n",
    "    names(perInpImpOnly.l)[i] <- d2[i]\n",
    "    rownames(perInpImpOnly.l[[i]]) <-  perInpImpOnly.l[[i]]$X\n",
    "    for (j in 1:6) {\n",
    "        perInpImpOnly.l[[i]][,j] <- as.numeric(perInpImpOnly.l[[i]][,j])\n",
    "    }\n",
    "    colnames(perInpImpOnly.l[[i]]) <- gsub(\"^Student\\\\.s\", \"St\", gsub(\"^X\\\\.Log\", \"negLog\", \n",
    "                                          colnames(perInpImpOnly.l[[i]])))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing for Differential Binding\n",
    "<a id='diffbind'></a>\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gEntrezToUniprotMappingDf = rbind(toTable(org.Rn.egUNIPROT))\n",
    "EG2SYM2Uniprot <- cbind.data.frame(gEntrezToUniprotMappingDf, EG2SYM$symbol[match(gEntrezToUniprotMappingDf$gene_id, \n",
    "                                                                                  EG2SYM$gene_id)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "gEntrezToUniprotMappingDf = rbind(toTable(org.Rn.egUNIPROT))\n",
    "EG2SYM2Uniprot <- cbind.data.frame(gEntrezToUniprotMappingDf, SYM=EG2SYM$symbol[match(gEntrezToUniprotMappingDf$gene_id, \n",
    "                                                                                  EG2SYM$gene_id)])\n",
    "AnnotationIDs.filt.l <- lapply(perInpImpOnly.l, function(x) {\n",
    "    Acc <- unlist(lapply(rownames(x), function(x) strsplit(gsub(\"tr\\\\|\", \"\", x), \n",
    "                                                                            split = \"\\\\|\")[[1]][1]))\n",
    "    ann <- cbind.data.frame(Acc, EG2SYM2Uniprot[match(Acc, gEntrezToUniprotMappingDf$uniprot_id),c(\"SYM\", \"gene_id\")])\n",
    "    rownames(ann) <- rownames(x)\n",
    "    ann\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta <- data.frame(Sample=c(\"P1_A\", \"P1_B\", \"P2_A\", \"P2_B\", \"P3_A\", \"P3_B\"),\n",
    "                   Treatment=c(\"A\", \"B\", \"A\", \"B\", \"A\", \"B\"),\n",
    "                   Group=c(\"P1\", \"P1\", \"P2\", \"P2\", \"P3\", \"P3\"))\n",
    "mm <- model.matrix(~0 + meta$Treatment + meta$Group)\n",
    "colnames(mm) <- gsub(\"^meta\\\\$\", \"\", colnames(mm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "perInpImpOnly_limma_out.l <- list()\n",
    "perInpImpOnly_limma_out.l[[1]] <- runLimma(perInpImpOnly.l[[1]][,c(1:6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "perInpImpOnly_limma_out.l[[1]][[1]] <- cbind.data.frame(perInpImpOnly_limma_out.l[[1]][[1]],\n",
    "                                                      AnnotationIDs.filt.l[[1]][match(rownames(perInpImpOnly_limma_out.l[[1]][[1]]), \n",
    "                                                          rownames(AnnotationIDs.filt.l[[1]])),])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional manual annotation to the Limma output was added as columns to the dataframe. These new data are reflected in the intermediate file (\"YuPekkurnaz_MitoProteomics_Overlaps_ImpTMM_limmaOutput_anno_filt2.csv\") loaded in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write.csv(perInpWAImp_limma_out.l[[1]][[1]], file=\"YuPekkurnaz_MitoProteomics_Overlaps_ImpTMM_limmaOutput_anno_filt2.csv\")\n",
    "#perInpImpOnly_DGE_limma_786_anno_filt <- perInpImpOnly_limma_out.l[[1]][[1]]\n",
    "perInpImpOnly_DGE_limma_786_anno_filt <- read.csv(file=\"./Data/YuPekkurnaz_MitoProteomics_Overlaps_ImpTMM_limmaOutput_anno_filt2.csv\", \n",
    "                                                header=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overlap Analysis\n",
    "<a id='overlap'></a>\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPI annotation files \n",
    "IMPI_ver_mito_union.df <- read.csv(\"./Data/IMPI_ver_mito_union_orig.csv\", header=TRUE)\n",
    "IMPI_ver_mito_union.df2 <- read.csv(\"./Data/IMPI_ver_mito_union.csv\", header=TRUE)\n",
    "\n",
    "# The O-GlcNAc Database v1.2 annotation file\n",
    "OGlc_rat <- read.csv(\"./Data/OGlc_rat.csv\", header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WIfVaJEiWwfT/92T0hISH9QX19/zZo1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ffkDz5vaMBgBHjvw2duyTfzPZvPnHIUNG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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vennInpOverlap <- as.data.frame(matrix(0, nrow=length(unique(c(as.character(IMPI_ver_mito_union.df$Sym),\n",
    "                                                                   as.character(OGlc_rat$UniSYM),\n",
    "                                                                   as.character(perInpImpOnly_DGE_limma_786_anno_filt$matchSYM)))), ncol=3))\n",
    "rownames(vennInpOverlap) <- unique(c(as.character(IMPI_ver_mito_union.df$Sym),\n",
    "                                     as.character(OGlc_rat$UniSYM),\n",
    "                                     as.character(perInpImpOnly_DGE_limma_786_anno_filt$matchSYM)))\n",
    "colnames(vennInpOverlap) <- c(\"Proteins\", \"MitoIMPI\", \"O-GlcNAc\")\n",
    "vennInpOverlap[which(rownames(vennInpOverlap) %in% as.character(perInpImpOnly_DGE_limma_786_anno_filt$matchSYM)),1] <- 1\n",
    "vennInpOverlap[which(rownames(vennInpOverlap) %in% as.character(IMPI_ver_mito_union.df$Sym)),2] <- 1\n",
    "vennInpOverlap[which(rownames(vennInpOverlap) %in% as.character(OGlc_rat$UniSYM)),3] <- 1\n",
    "#pdf(file=\"YuPekkurnaz_MitoProteomics_FigureS5E_venndiagram.pdf\", height=8, width=8)\n",
    "vennDiagram(vennInpOverlap, circle.col = c(\"red\", \"blue\", \"gray\"))\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mitoOverlap <- vennInpOverlap[vennInpOverlap[,1] == 1 & vennInpOverlap[,2] == 1,]\n",
    "prot786_All_mito <- perInpImpOnly_DGE_limma_786_anno_filt[perInpImpOnly_DGE_limma_786_anno_filt$matchSYM %in% \n",
    "                                                           rownames(mitoOverlap),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrichment Analysis\n",
    "<a id='enrich'></a>\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 73 of 80 mitochondrial overlap proteins have unique symbols that can be used as input into the enrichment analysis\n",
    "MitoOverlaps <- read.csv(file=\"YuPekkurnaz_MitoProteomics_Mitooverlap_73Proteins_ImpTMM_limmaOutput_anno2.csv\")\n",
    "gostres_Mito <- gost(query = MitoOverlaps$matchSYM, \n",
    "                        organism = \"rnorvegicus\", \n",
    "                        ordered_query = FALSE, \n",
    "                        multi_query = FALSE, \n",
    "                        significant = TRUE, \n",
    "                        exclude_iea = FALSE,\n",
    "                        measure_underrepresentation = FALSE, \n",
    "                        evcodes = TRUE,\n",
    "                        user_threshold = 0.05, \n",
    "                        correction_method = \"fdr\",\n",
    "                        domain_scope = \"custom_annotated\", \n",
    "                        custom_bg = unique(c(perInpImpOnly_DGE_limma_786_anno_filt$UniSYM, MitoOverlaps$matchSYM)),\n",
    "                        numeric_ns = \"\", \n",
    "                        sources = NULL, \n",
    "                        as_short_link = FALSE)\n",
    "gpro_res_Mito <- gostres_Mito$result[,c(\"p_value\", \"term_size\", \"query_size\", \"intersection_size\", \"precision\",\"recall\", \"term_id\", \"source\", \"term_name\", \"intersection\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ODrIyUlZdu2bXv37s3JyYkO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\nWFi4YsWK8Eo04bnnn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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpro_res_Mito_REAC <- gpro_res_Mito[grep(\"REAC\", gpro_res_Mito$source),]\n",
    "gpro_res_Mito_REAC$\"-log(p-value)\" <- -log(gpro_res_Mito_REAC$p_value, 10)\n",
    "gpro_res_Mito_REAC$gene.ratio <- gpro_res_Mito_REAC$intersection_size/gpro_res_Mito_REAC$term_size\n",
    "colnames(gpro_res_Mito_REAC)[4] <- \"Count\" \n",
    "\n",
    "options(repr.plot.width=16, repr.plot.height=7)\n",
    "barplot.inp <- gpro_res_Mito_REAC[1:10,]\n",
    "barplot.inp$term_name <- factor(barplot.inp$term_name, \n",
    "                                levels=barplot.inp$term_name[order(barplot.inp$`-log(p-value)`, decreasing=FALSE)])\n",
    "S1 <- ggplot(barplot.inp, aes(x=`-log(p-value)`, y=term_name, fill=`-log(p-value)`)) + \n",
    "#geom_bar(stat='identity') + \n",
    "theme_classic(base_size=14) + ylab(\"\") + xlab(\"-log(p-value)\") + geom_col()\n",
    "S1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Plots for Figures\n",
    "<a id='GenerateFigPlots'></a>\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 17 rows containing missing values (geom_point).”\n"
     ]
    },
    {
     "data": {
      "image/png": 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IAJBGgAAADABAI0AAAAYAIBGgAAADCB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69Ojy5csBAQEeHh6KioqrVq3q0KGDtJ6V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\nABpQoAEAAAANKNAAAACABv8FLSKbIiXCLesAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- perInpImpOnly_DGE_limma_786_anno_filt\n",
    "x$neglogq <- -log((as.numeric(x$adj.P.Val)+0.0000000000001), 10)\n",
    "x$neglogp <- -log((as.numeric(x$P.Value)+0.0000000000001), 10)\n",
    "x$sig <- factor(as.numeric(x$neglogp) > 1.299)\n",
    "x$label <- \"\"\n",
    "x$label[as.numeric(x$neglogp) > 1.299][1:20] <- x$UniSYM[as.numeric(x$neglogp) > 1.299][1:20]\n",
    "options(repr.plot.width=8, repr.plot.height=8)\n",
    "#pdf(file=\"YuPekkurnaz_MitoProteomics_Figure5B_pval_0.05_update.pdf\", width=6, height=6)\n",
    "p <- ggplot(x, aes(x=logFC, y=neglogp)) + geom_point(aes(colour=sig), size=0.75) + xlim(0, 5) + ylim(0,3) +\n",
    "        theme_bw(base_size=16) + ylab(\"-Log(Pval)\") + labs(title=\"\") +  xlab(\"LogFC\") + \n",
    "        scale_color_manual(values=c(\"black\", \"red\")) + theme(legend.position=\"none\") #+\n",
    "show(p)\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Input</th><th scope=col>Input.Type</th><th scope=col>MGI.Gene.Marker.ID</th><th scope=col>Symbol</th><th scope=col>Name</th><th scope=col>Feature.Type</th><th scope=col>GO.ID</th><th scope=col>Term</th><th scope=col>Code</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Scyl2</td><td>current symbol</td><td>MGI:1289172</td><td>Scyl2</td><td>SCY1-like 2 (S. cerevisiae)</td><td>protein coding gene</td><td>GO:0005524</td><td>ATP binding                   </td><td>IEA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Scyl2</td><td>current symbol</td><td>MGI:1289172</td><td>Scyl2</td><td>SCY1-like 2 (S. cerevisiae)</td><td>protein coding gene</td><td>GO:0007420</td><td>brain development             </td><td>IDA</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Scyl2</td><td>current symbol</td><td>MGI:1289172</td><td>Scyl2</td><td>SCY1-like 2 (S. cerevisiae)</td><td>protein coding gene</td><td>GO:0031410</td><td>cytoplasmic vesicle           </td><td>IEA</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Scyl2</td><td>current symbol</td><td>MGI:1289172</td><td>Scyl2</td><td>SCY1-like 2 (S. cerevisiae)</td><td>protein coding gene</td><td>GO:0005768</td><td>endosome                      </td><td>IEA</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Scyl2</td><td>current symbol</td><td>MGI:1289172</td><td>Scyl2</td><td>SCY1-like 2 (S. cerevisiae)</td><td>protein coding gene</td><td>GO:0008333</td><td>endosome to lysosome transport</td><td>ISO</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Scyl2</td><td>current symbol</td><td>MGI:1289172</td><td>Scyl2</td><td>SCY1-like 2 (S. cerevisiae)</td><td>protein coding gene</td><td>GO:0005794</td><td>Golgi apparatus               </td><td>IEA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & Input & Input.Type & MGI.Gene.Marker.ID & Symbol & Name & Feature.Type & GO.ID & Term & Code\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & Scyl2 & current symbol & MGI:1289172 & Scyl2 & SCY1-like 2 (S. cerevisiae) & protein coding gene & GO:0005524 & ATP binding                    & IEA\\\\\n",
       "\t2 & Scyl2 & current symbol & MGI:1289172 & Scyl2 & SCY1-like 2 (S. cerevisiae) & protein coding gene & GO:0007420 & brain development              & IDA\\\\\n",
       "\t3 & Scyl2 & current symbol & MGI:1289172 & Scyl2 & SCY1-like 2 (S. cerevisiae) & protein coding gene & GO:0031410 & cytoplasmic vesicle            & IEA\\\\\n",
       "\t4 & Scyl2 & current symbol & MGI:1289172 & Scyl2 & SCY1-like 2 (S. cerevisiae) & protein coding gene & GO:0005768 & endosome                       & IEA\\\\\n",
       "\t5 & Scyl2 & current symbol & MGI:1289172 & Scyl2 & SCY1-like 2 (S. cerevisiae) & protein coding gene & GO:0008333 & endosome to lysosome transport & ISO\\\\\n",
       "\t6 & Scyl2 & current symbol & MGI:1289172 & Scyl2 & SCY1-like 2 (S. cerevisiae) & protein coding gene & GO:0005794 & Golgi apparatus                & IEA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | Input &lt;chr&gt; | Input.Type &lt;chr&gt; | MGI.Gene.Marker.ID &lt;chr&gt; | Symbol &lt;chr&gt; | Name &lt;chr&gt; | Feature.Type &lt;chr&gt; | GO.ID &lt;chr&gt; | Term &lt;chr&gt; | Code &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Scyl2 | current symbol | MGI:1289172 | Scyl2 | SCY1-like 2 (S. cerevisiae) | protein coding gene | GO:0005524 | ATP binding                    | IEA |\n",
       "| 2 | Scyl2 | current symbol | MGI:1289172 | Scyl2 | SCY1-like 2 (S. cerevisiae) | protein coding gene | GO:0007420 | brain development              | IDA |\n",
       "| 3 | Scyl2 | current symbol | MGI:1289172 | Scyl2 | SCY1-like 2 (S. cerevisiae) | protein coding gene | GO:0031410 | cytoplasmic vesicle            | IEA |\n",
       "| 4 | Scyl2 | current symbol | MGI:1289172 | Scyl2 | SCY1-like 2 (S. cerevisiae) | protein coding gene | GO:0005768 | endosome                       | IEA |\n",
       "| 5 | Scyl2 | current symbol | MGI:1289172 | Scyl2 | SCY1-like 2 (S. cerevisiae) | protein coding gene | GO:0008333 | endosome to lysosome transport | ISO |\n",
       "| 6 | Scyl2 | current symbol | MGI:1289172 | Scyl2 | SCY1-like 2 (S. cerevisiae) | protein coding gene | GO:0005794 | Golgi apparatus                | IEA |\n",
       "\n"
      ],
      "text/plain": [
       "  Input Input.Type     MGI.Gene.Marker.ID Symbol Name                       \n",
       "1 Scyl2 current symbol MGI:1289172        Scyl2  SCY1-like 2 (S. cerevisiae)\n",
       "2 Scyl2 current symbol MGI:1289172        Scyl2  SCY1-like 2 (S. cerevisiae)\n",
       "3 Scyl2 current symbol MGI:1289172        Scyl2  SCY1-like 2 (S. cerevisiae)\n",
       "4 Scyl2 current symbol MGI:1289172        Scyl2  SCY1-like 2 (S. cerevisiae)\n",
       "5 Scyl2 current symbol MGI:1289172        Scyl2  SCY1-like 2 (S. cerevisiae)\n",
       "6 Scyl2 current symbol MGI:1289172        Scyl2  SCY1-like 2 (S. cerevisiae)\n",
       "  Feature.Type        GO.ID      Term                           Code\n",
       "1 protein coding gene GO:0005524 ATP binding                    IEA \n",
       "2 protein coding gene GO:0007420 brain development              IDA \n",
       "3 protein coding gene GO:0031410 cytoplasmic vesicle            IEA \n",
       "4 protein coding gene GO:0005768 endosome                       IEA \n",
       "5 protein coding gene GO:0008333 endosome to lysosome transport ISO \n",
       "6 protein coding gene GO:0005794 Golgi apparatus                IEA "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MGI annotation file containing cellular location \n",
    "MGI.df <- read.csv(\"./Data/MGIBatchReport_20220317_144345.csv\", header=TRUE, stringsAsFactors=FALSE)\n",
    "head(MGI.df)\n",
    "primary_local <- c(\"Endoplasmic reticulum\", \"Lysosome\", \"Endosome\", \"Golgi\", \"Peroxisome\", \"Cytoskeleton\", \n",
    "                   \"Stress granules\", \"Glycolytic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "MGI.df2 <- MGI.df[-grep(\"regulation of|activity|process|transport|translocation|binding|signaling|organization|release|\n",
    "export|import\", MGI.df$Term),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "MGI.df2_mito <- MGI.df2[grep(\"^mitochondrion$\", tolower(MGI.df2$Term)),]\n",
    "MGI.df2_mito <- MGI.df2_mito[!duplicated(MGI.df2_mito[,1]),]\n",
    "MGI.df2_mito$Term <- \"Mitochondrion\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "MGI.df2_mito <- MGI.df2[MGI.df2$Input %in% IMPI_ver_mito_union.df2$Sym | \n",
    "                         MGI.df2$Input %in% \"Atad3\",]\n",
    "MGI.df2_mito <- MGI.df2_mito[!duplicated(MGI.df2_mito[,c(1)]),]\n",
    "MGI.df2_mito$Term <- \"Mitochondrion\"\n",
    "MGI.genes_rem <- MGI.df2_mito$Input\n",
    "MGI.df2_A <- MGI.df2[!MGI.df2$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_golgi <- MGI.df2_A[grep(\"^golgi\", tolower(MGI.df2_A$Term)),]\n",
    "MGI.df2_golgi <- MGI.df2_golgi[!duplicated(MGI.df2_golgi[,1:6]),]\n",
    "MGI.df2_golgi$Term <- \"Golgi\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_golgi$Input)\n",
    "MGI.df2_B <- MGI.df2_A[!MGI.df2_A$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_lys <- MGI.df2_B[grep(\"lysosome$\", tolower(MGI.df2_B$Term)),]\n",
    "MGI.df2_lys <- MGI.df2_lys[!duplicated(MGI.df2_lys[,1:6]),]\n",
    "MGI.df2_lys$Term <- \"Lysosome\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_lys$Input)\n",
    "MGI.df2_C <- MGI.df2_B[!MGI.df2_B$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_endo <- MGI.df2_C[grep(\"endosome$\", tolower(MGI.df2_C$Term)),]\n",
    "MGI.df2_endo <- MGI.df2_endo[!duplicated(MGI.df2_endo[,1:6]),]\n",
    "MGI.df2_endo$Term <- \"Endosome\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_endo$Input)\n",
    "MGI.df2_D <- MGI.df2_C[!MGI.df2_C$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_perox <- MGI.df2_D[grep(\"peroxisome$\", tolower(MGI.df2_D$Term)),]\n",
    "MGI.df2_perox <- MGI.df2_perox[!duplicated(MGI.df2_perox[,1:6]),]\n",
    "MGI.df2_perox$Term <- \"Peroxisome\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_perox$Input)\n",
    "MGI.df2_E <- MGI.df2_D[!MGI.df2_D$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_prim <- MGI.df2_E[tolower(MGI.df2_E$Term) %in% tolower(primary_local[-c(2:5,9)]),]\n",
    "MGI.df2_prim <- MGI.df2_prim[!duplicated(MGI.df2_prim[,1]),]\n",
    "#MGI.df2_prim$Term <- \"Cytosol\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_prim$Input)\n",
    "MGI.df2_F <- MGI.df2_E[!MGI.df2_E$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_cyto <- MGI.df2_F[grep(\"cytosol$|cytoplasm$\", tolower(MGI.df2_F$Term)),]\n",
    "MGI.df2_cyto <- MGI.df2_cyto[!duplicated(MGI.df2_cyto[,1:6]),]\n",
    "MGI.df2_cyto$Term <- \"Cytosol\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_cyto$Input)\n",
    "MGI.df2_G <- MGI.df2_F[!MGI.df2_F$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_nucleus <- MGI.df2_G[grep(\"nucleus$|nucleoplasm$\", tolower(MGI.df2_G$Term)),]\n",
    "MGI.df2_nucleus <- MGI.df2_nucleus[!duplicated(MGI.df2_nucleus[,1:6]),]\n",
    "MGI.df2_nucleus$Term <- \"Nucleus\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_nucleus$Input)\n",
    "MGI.df2_H <- MGI.df2_G[!MGI.df2_G$Input %in% MGI.genes_rem,]\n",
    "MGI.df2_axon <- MGI.df2_H[grep(\"axon\", tolower(MGI.df2_G$Term)),]\n",
    "MGI.df2_axon <- MGI.df2_axon[!duplicated(MGI.df2_axon[,1:6]),]\n",
    "MGI.df2_axon$Term <- \"Axon\"\n",
    "MGI.genes_rem <- c(MGI.genes_rem, MGI.df2_axon$Input)\n",
    "\n",
    "MGI.df2_terms_1 <- rbind.data.frame(MGI.df2_mito, MGI.df2_lys, MGI.df2_endo, MGI.df2_perox, \n",
    "                                    MGI.df2_cyto, MGI.df2_nucleus, MGI.df2_axon, MGI.df2_golgi, MGI.df2_prim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "MGI.df_genes_1 <- unique(c(MGI.df2$Input[grep(\"^golgi\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[grep(\"lysosome$\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[grep(\"endosome$\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[grep(\"peroxisome$\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[grep(\"cytosol$|cytoplasm$\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[grep(\"nucleus$|nucleoplasm$\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[grep(\"axon\", tolower(MGI.df2$Term))],\n",
    "MGI.df2$Input[tolower(MGI.df2$Term) %in% tolower(primary_local)],\n",
    "MGI.df2_mito$Input\n",
    "))\n",
    "\n",
    "MGI.df2_filt_1 <- MGI.df2[!MGI.df2$Input %in% MGI.df_genes_1,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write.csv(MGI.df2_filt_1, file=\"MGIBatchReport_firstround_filt.csv\")\n",
    "MGI.df2_filt_2 <- read.csv(file=\"./Data/MGIBatchReport_firstround_filt_output.csv\", header=TRUE, stringsAsFactors=FALSE)\n",
    "MGI.df2_filt_comb <- rbind.data.frame(MGI.df2_terms_1, MGI.df2_filt_2)\n",
    "MGI.df2_filt_pieinp <- as.data.frame(table(MGI.df2_filt_comb$Term))\n",
    "MGI.df2_filt_pieinp <- MGI.df2_filt_pieinp[MGI.df2_filt_pieinp$Freq > 3,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C7PnXx14LRZzvwX/I4DDDZlJZJSKzr6drWVawfKwm5arX4sbG8FNPfzyhuj1v3a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\nCWrs0J9OYZB5t5bt0refVUWGyjwAYGigeAMDjtNRW/3kBxuX3TO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VzDMhltIP5utA1O8cAJCVKN4YyuKVy/yO0AuJquXhEXP8TjEINC79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Z8j6dxoxebNsXA7VZf3VObZySbJp4Y/HmRq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\nR3eT6jjAQTS0bxdxB+aIt8Pl+KphlVVnuCA2V3UW//dm3eZ2V/f1SYM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ukV7TLov3tw/jxoWnLi+55MGscWEGs+osAIBARPEGIHqd6aT+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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 510,
       "width": 660
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MGI.df2_filt_pieinp$Percent <- round(100*(MGI.df2_filt_pieinp$Freq/sum(MGI.df2_filt_pieinp[,2])), digits = 1)\n",
    "MGI.df2_filt_pieinp$labels <- paste0(MGI.df2_filt_pieinp$Percent, \"%\")\n",
    "MGI.df2_filt_pieinp$Var1 <- str_to_title(MGI.df2_filt_pieinp$Var1)\n",
    "options(repr.plot.width=11, repr.plot.height=8.5)\n",
    "#pdf(file=\"YuPekkurnaz_MitoProtemics_FigureS5F_SubcellPie.pdf\", height=8.5, width=11)\n",
    "ggplot(MGI.df2_filt_pieinp, aes(x=\"\", y=Freq, fill=Var1)) +\n",
    "  geom_bar(stat=\"identity\", width=2, color=\"white\") +\n",
    "    geom_label(aes(label = labels, x=1.9), color = \"white\",\n",
    "            position = position_stack(vjust = 0.5),\n",
    "            show.legend = FALSE) +\n",
    "  coord_polar(\"y\", start=0) + theme_void() + theme(legend.title=element_text(size=0))\n",
    "#dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "MGI.df.l <- MGI.df %>% group_by(Input) %>% summarize(Term=list(Term))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MitoCarta 3.0 annotation file containing submitochondrial location\n",
    "mito_mouse <- read.csv(file=\"MitoCarta_mouse.csv\", header=TRUE)\n",
    "prot786_All_mito$MitoLocal <- mito_mouse$MitoCarta3.0_SubMitoLocalization[match(prot786_All_mito$matchSYM,\n",
    "                                                                                mito_mouse$Symbol)]\n",
    "prot786_All_mito$MitoLocal <- as.character(prot786_All_mito$MitoLocal)\n",
    "prot786_All_mito$MitoLocal <- as.character(prot786_All_mito$MitoLocal)\n",
    "prot786_All_mito$MitoLocal[prot786_All_mito$matchSYM %in% c(\"Atp5po\", \"Atp5f1c\", \"Atp5f1a\", \"Atp5f1b\", \"Atp5pd\")] <- \"MIM\"\n",
    "prot786_All_mito$MitoLocal[prot786_All_mito$matchSYM %in% c(\"Slit3\", \"Mark2\", \"Gramd4\", \"Hspa5\", \"Lrrc59\",\n",
    "                                                           \"Ddx3x\", \"Hnrnpk\", \"Ncstn\")] <- \"Unknown\"\n",
    "prot786_All_mito$MitoLocal[prot786_All_mito$matchSYM %in% c(\"Acsl4\", \"Abce1\", \"Actb\", \"Vim\", \"Sybu\", \"Ogt\",\n",
    "                                                           \"Rps3\", \"Erbb4\", \"Nrp1\", \"Snph\", \"Slc44a2\",\n",
    "                                                           \"Rab5c\", \"Tmx2\")] <- \"Associated\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 3 rows containing missing values (geom_point).”\n",
      "Warning message:\n",
      "“Removed 3 rows containing missing values (geom_text_repel).”\n",
      "Warning message:\n",
      "“ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps”\n"
     ]
    },
    {
     "data": {
      "image/png": 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OrVq5ctW7Z3715nZ+cffvjB0iwrK2vgwIH79+/fsWOHj49Pu3btLN8ThBA//PCD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\noIAGAAAA1EABDQAAAKjhv6B6agHdqa8LAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- prot786_All_mito\n",
    "    x$neglogq <- -log((as.numeric(x$adj.P.Val)+0.0000000000001), 10)\n",
    "    x$neglogp <- -log((as.numeric(x$P.Value)+0.0000000000001), 10)\n",
    "    #x$sig <- c(\"black\",\"red\")[as.numeric(as.numeric(x$neglogp) > 1.299) + 1]\n",
    "    x$sig <- factor(as.numeric(x$neglogp) > 1.299)\n",
    "    x$label <- \"\"\n",
    "    x$label[as.numeric(x$neglogp) > 1.299][1:20] <- x$UniSYM[as.numeric(x$neglogp) > 1.299][1:20]\n",
    "    options(repr.plot.width=8, repr.plot.height=8)\n",
    "    #pdf(file=\"YuPekkurnaz_MitoProteomics_Figure5C_pval_0.05_update.pdf\", width=6, height=6)\n",
    "    p <- ggplot(x, aes(x=logFC, y=neglogp)) + geom_point(aes(colour=sig), size=0.75) + xlim(0, 5) + ylim(0,3) +\n",
    "            theme_bw(base_size=16) + ylab(\"-Log(Pval)\") + labs(title=\"\") +  xlab(\"LogFC\") + \n",
    "            scale_color_manual(values=c(\"black\", \"red\")) + theme(legend.position=\"none\") +\n",
    "            geom_text_repel(aes(label=label), size=3)\n",
    "    show(p)\n",
    "#main=\"\", xlab=\"LogFC\", ylab=\"-Log(Pval)\", pch=19, cex=0.5, col=\"red\", ylim=c(0,5)))\n",
    "    #dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mitochondrial overlapping proteins with submitochondrial location annotation\n",
    "prot786_All_mito_subMito <- read.csv(\"./Data/YuPekkurnaz_MitoProteomics_All_Mito_80proteins_submitoloc.csv\", header=TRUE)\n",
    "all_mito_pieinp <- as.data.frame(table(prot786_All_mito_subMito$MitoLocal))\n",
    "all_mito_pieinp <- all_mito_pieinp[all_mito_pieinp$Freq > 3,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jrm87ieWLxrfWWz3XFERMrqwlc9sWLL3la7gwBAiqF4A05jmhLm2DAASH1/uWny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ZEd1+++133HHH4sWLi4qK9Hp9JBKZMWPG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\nGwAAAAIS3gAAABDQvwEk5U3B9ptVIwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 510,
       "width": 660
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_mito_pieinp$Var1 <- str_to_title(all_mito_pieinp$Var1)\n",
    "all_mito_pieinp$Percent <- round(100*(all_mito_pieinp$Freq/sum(all_mito_pieinp[,2])), digits = 1)\n",
    "all_mito_pieinp$labels <- paste0(all_mito_pieinp$Percent, \"%\")\n",
    "\n",
    "options(repr.plot.width=11, repr.plot.height=8.5)\n",
    "#pdf(file=\"YuPekkurnaz_MitoProtemics_Figure5F_SubmitoPie.pdf\", height=8.5, width=11)\n",
    "ggplot(all_mito_pieinp, aes(x=\"\", y=Freq, fill=Var1)) + \n",
    "scale_fill_brewer(palette=\"Blues\") +\n",
    "  geom_bar(stat=\"identity\", width=2, color=\"white\") +\n",
    "    geom_label(aes(label = labels, x=1.9), color = c(\"white\", \"white\", \"white\", \"black\", \"black\"),\n",
    "            position = position_stack(vjust = 0.5),\n",
    "            show.legend = FALSE) +\n",
    "  coord_polar(\"y\", start=0) + theme_void() + theme(legend.title=element_text(size=0))\n",
    "#dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MitoCarta 3.0 annotation file containing functional mitochondrial pathways\n",
    "mitopaths <- read.csv(file=\"./Data/Mouse.MitoCarta3.0_mitopaths.csv\", header=TRUE, stringsAsFactors=FALSE)\n",
    "top_paths <- mitopaths[-grep(\">\", mitopaths[,2]),]\n",
    "top_paths$Genes <- gsub(\" \", \"\", top_paths$Genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "secondmitopaths_overlap.l <- thirdmitopaths_overlap.l <- top_paths_overlap.l <-list()\n",
    "for (i in 1:nrow(top_paths)) {\n",
    "    secondmitopaths_overlap.l[[i]] <- thirdmitopaths_overlap.l[[i]] <- top_paths_overlap.l[[i]] <- list()\n",
    "    names(top_paths_overlap.l)[i] <- top_paths$MitoPathway[i]\n",
    "    u <- unlist(strsplit(top_paths$Genes[i], split = \",\"))\n",
    "    top_paths_overlap.l[[i]] <- prot786_All_mito[prot786_All_mito$SYM %in% u| prot786_All_mito$matchSYM %in% u | \n",
    "                                               prot786_All_mito$UniSYM %in% u,]\n",
    "}\n",
    "top_paths_overlap.df <- do.call(rbind.data.frame, top_paths_overlap.l) \n",
    "top_paths_overlap.df$TopMitoPath <- unlist(lapply(1:length(top_paths_overlap.l), function(i) {\n",
    "    rep(names(top_paths_overlap.l)[i], nrow(top_paths_overlap.l[[i]]))\n",
    "}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "t <- mitopaths[grep(\">\", mitopaths[,2]),1:3]\n",
    "Second_paths <- do.call(rbind.data.frame, lapply(1:nrow(t), function(i) {\n",
    "    s <- strsplit(t$MitoPathway.Hierarchy[i], split = \">\")\n",
    "    if(length(s[[1]]) == 2) {\n",
    "    return(as.vector(c(t[i,1:3], s[[1]][1], s[[1]][2])))\n",
    "       }\n",
    "       }))\n",
    "colnames(Second_paths)[4:5] <- c(\"TopPath\", \"SecondPath\")\n",
    "Second_paths$Genes <- gsub(\" \", \"\", Second_paths$Genes)\n",
    "Third_paths <- do.call(rbind.data.frame, lapply(1:nrow(t), function(i) {\n",
    "    s <- strsplit(t$MitoPathway.Hierarchy[i], split = \">\")\n",
    "    if(length(s[[1]]) == 3) {\n",
    "    return(as.vector(c(t[i,1:3], s[[1]][1], s[[1]][2], s[[1]][3])))\n",
    "       }\n",
    "       }))\n",
    "colnames(Third_paths)[4:6] <- c(\"TopPath\", \"SecondPath\", \"ThirdPath\")\n",
    "Third_paths$Genes <- gsub(\" \", \"\", Third_paths$Genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (i in 1:nrow(Second_paths)) {\n",
    "    secondmitopaths_overlap.l[[i]] <- list()\n",
    "    names(secondmitopaths_overlap.l)[i] <- as.character(Second_paths$SecondPath)[i]\n",
    "    u <- unlist(strsplit(as.character(Second_paths$Genes)[i], split = \",\"))\n",
    "    secondmitopaths_overlap.l[[i]] <- prot786_All_mito[prot786_All_mito$SYM %in% u| prot786_All_mito$matchSYM %in% u | \n",
    "                                               prot786_All_mito$UniSYM %in% u,]\n",
    "}\n",
    "Second_paths_sum.df <- data.frame(Total=unlist(lapply(1:nrow(Second_paths), function(i) length(unlist(strsplit(as.character(Second_paths$Genes)[i], split=\",\"))))),\n",
    "                                  SecondPath=as.character(Second_paths$SecondPath))\n",
    "Second_paths_sum.df$TopPath <- as.character(Second_paths$TopPath)[match(Second_paths_sum.df$SecondPath,\n",
    "                                                                       as.character(Second_paths$SecondPath))]\n",
    "Second_paths_overlap_sum.df <- data.frame(Total=unlist(lapply(secondmitopaths_overlap.l, nrow)),\n",
    "                                          SecondPath=names(secondmitopaths_overlap.l))\n",
    "Second_paths_overlap_sum.df$TopPath <- as.character(Second_paths$TopPath)[match(Second_paths_overlap_sum.df$SecondPath,\n",
    "                                                                                as.character(Second_paths$SecondPath))]\n",
    "Second_paths_overlap_sum.df$TotalProteins <- Second_paths_sum.df$Total[match(Second_paths_overlap_sum.df$SecondPath,\n",
    "                                                                             Second_paths_sum.df$SecondPath)]\n",
    "second_paths_overlap.df <- do.call(rbind.data.frame, secondmitopaths_overlap.l) \n",
    "\n",
    "second_paths_overlap.df$SecondMitoPath <- unlist(lapply(1:length(secondmitopaths_overlap.l), function(i) {\n",
    "    rep(names(secondmitopaths_overlap.l)[i], nrow(secondmitopaths_overlap.l[[i]]))\n",
    "}))\n",
    "second_paths_overlap.df$TopMitoPath <- as.character(Second_paths$TopPath)[match(second_paths_overlap.df$SecondMitoPath,\n",
    "                                                                  as.character(Second_paths$SecondPath))]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (i in 1:nrow(Third_paths)) {\n",
    "    thirdmitopaths_overlap.l[[i]] <- list()\n",
    "    names(thirdmitopaths_overlap.l)[i] <- as.character(Third_paths$ThirdPath)[i]\n",
    "    u <- unlist(strsplit(as.character(Third_paths$Genes)[i], split = \",\"))\n",
    "    thirdmitopaths_overlap.l[[i]] <- prot786_All_mito[prot786_All_mito$SYM %in% u| prot786_All_mito$matchSYM %in% u | \n",
    "                                               prot786_All_mito$UniSYM %in% u,]\n",
    "}\n",
    "\n",
    "Third_paths_sum.df <- data.frame(Total=unlist(lapply(1:nrow(Third_paths), function(i) length(unlist(strsplit(as.character(Third_paths$Genes)[i], split=\",\"))))),\n",
    "                                 ThirdPath=as.character(Third_paths$ThirdPath))\n",
    "Third_paths_sum.df$SecondPath <- as.character(Third_paths$SecondPath)[match(Third_paths_sum.df$ThirdPath,\n",
    "                                                                      as.character(Third_paths$ThirdPath))]\n",
    "Third_paths_sum.df$TopPath <- as.character(Third_paths$TopPath)[match(Third_paths_sum.df$ThirdPath,\n",
    "                                                                      as.character(Third_paths$ThirdPath))]\n",
    "Third_paths_overlap_sum.df <- data.frame(Total=unlist(lapply(thirdmitopaths_overlap.l, nrow)))\n",
    "Third_paths_overlap_sum.df$ThirdPath <- rownames(Third_paths_overlap_sum.df)\n",
    "Third_paths_overlap_sum.df$SecondPath <- as.character(Third_paths$SecondPath)[match(Third_paths_overlap_sum.df$ThirdPath,\n",
    "                                                                                as.character(Third_paths$ThirdPath))]\n",
    "Third_paths_overlap_sum.df$TopPath <- as.character(Third_paths$TopPath)[match(Third_paths_overlap_sum.df$ThirdPath,\n",
    "                                                                                as.character(Third_paths$ThirdPath))]\n",
    "Third_paths_overlap_sum.df$TotalProteins <- Third_paths_sum.df$Total[match(Third_paths_overlap_sum.df$ThirdPath,\n",
    "                                                                             Third_paths_sum.df$ThirdPath)]\n",
    "third_paths_overlap.df <- do.call(rbind.data.frame, thirdmitopaths_overlap.l) \n",
    "\n",
    "third_paths_overlap.df$ThirdMitoPath <- unlist(lapply(1:length(thirdmitopaths_overlap.l), function(i) {\n",
    "    rep(names(thirdmitopaths_overlap.l)[i], nrow(thirdmitopaths_overlap.l[[i]]))\n",
    "}))\n",
    "third_paths_overlap.df$SecondMitoPath <- as.character(Third_paths$SecondPath)[match(third_paths_overlap.df$ThirdMitoPath,\n",
    "                                                                  as.character(Third_paths$ThirdPath))]\n",
    "third_paths_overlap.df$TopMitoPath <- as.character(Third_paths$TopPath)[match(third_paths_overlap.df$ThirdMitoPath,\n",
    "                                                                  as.character(Third_paths$ThirdPath))]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "<a id='ref'></a>\n",
    "\n",
    "1. Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics. 2014 Sep;13(9):2513-26. doi: 10.1074/mcp.M113.031591. Epub 2014 Jun 17. PubMed PMID: 24942700; PubMed Central PMCID: PMC4159666.\n",
    "2. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, Cox J. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016 Sep;13(9):731-40. doi: 10.1038/nmeth.3901. Epub 2016 Jun 27. PubMed PMID: 27348712.\n",
    "3. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47. doi: 10.1093/nar/gkv007. Epub 2015 Jan 20. PubMed PMID: 25605792; PubMed Central PMCID: PMC4402510.\n",
    "4. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag A, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics volume 34, pages 267–273 (2003).\n",
    "5. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences Oct 2005, 102 (43) 15545-15550; DOI: 10.1073/pnas.0506580102.\n",
    "\n",
    "[Table of Contents](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix: R Session Info\n",
    "<a id='app'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 4.1.3 (2022-03-10)\n",
       "Platform: x86_64-conda-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 20.04.2 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /home/ubuntu/anaconda3/envs/rbase_4_1/lib/libopenblasp-r0.3.20.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n",
       " [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n",
       " [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n",
       "[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n",
       "\n",
       "attached base packages:\n",
       "[1] stats4    stats     graphics  grDevices utils     datasets  methods  \n",
       "[8] base     \n",
       "\n",
       "other attached packages:\n",
       " [1] IRdisplay_1.1        gprofiler2_0.2.1     ggrepel_0.9.1       \n",
       " [4] reshape2_1.4.4       org.Hs.eg.db_3.14.0  RColorBrewer_1.1-3  \n",
       " [7] dplyr_1.0.9          plyr_1.8.7           ggpubr_0.4.0        \n",
       "[10] gplots_3.1.3         ggplot2_3.3.6        stringr_1.4.0       \n",
       "[13] limma_3.50.3         org.Rn.eg.db_3.14.0  AnnotationDbi_1.56.2\n",
       "[16] IRanges_2.28.0       S4Vectors_0.32.4     Biobase_2.54.0      \n",
       "[19] BiocGenerics_0.40.0 \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] httr_1.4.3             tidyr_1.2.0            viridisLite_0.4.0     \n",
       " [4] bit64_4.0.5            jsonlite_1.8.0         carData_3.0-5         \n",
       " [7] gtools_3.9.3           assertthat_0.2.1       blob_1.2.3            \n",
       "[10] GenomeInfoDbData_1.2.7 pillar_1.8.0           RSQLite_2.2.15        \n",
       "[13] backports_1.4.1        glue_1.6.2             uuid_1.1-0            \n",
       "[16] digest_0.6.29          XVector_0.34.0         ggsignif_0.6.3        \n",
       "[19] colorspace_2.0-3       htmltools_0.5.3        pkgconfig_2.0.3       \n",
       "[22] broom_1.0.0            zlibbioc_1.40.0        purrr_0.3.4           \n",
       "[25] scales_1.2.0           tibble_3.1.7           KEGGREST_1.34.0       \n",
       "[28] farver_2.1.1           generics_0.1.3         car_3.1-0             \n",
       "[31] ellipsis_0.3.2         cachem_1.0.6           withr_2.5.0           \n",
       "[34] repr_1.1.4             lazyeval_0.2.2         cli_3.3.0             \n",
       "[37] magrittr_2.0.3         crayon_1.5.1           memoise_2.0.1         \n",
       "[40] evaluate_0.15          fansi_1.0.3            rstatix_0.7.0         \n",
       "[43] data.table_1.14.2      tools_4.1.3            lifecycle_1.0.1       \n",
       "[46] plotly_4.10.0          munsell_0.5.0          Biostrings_2.62.0     \n",
       "[49] compiler_4.1.3         GenomeInfoDb_1.30.1    caTools_1.18.2        \n",
       "[52] rlang_1.0.4            grid_4.1.3             RCurl_1.98-1.7        \n",
       "[55] pbdZMQ_0.3-7           IRkernel_1.3           htmlwidgets_1.5.4     \n",
       "[58] labeling_0.4.2         bitops_1.0-7           base64enc_0.1-3       \n",
       "[61] gtable_0.3.0           abind_1.4-5            DBI_1.1.3             \n",
       "[64] R6_2.5.1               fastmap_1.1.0          bit_4.0.4             \n",
       "[67] utf8_1.2.2             KernSmooth_2.23-20     stringi_1.7.6         \n",
       "[70] Rcpp_1.0.9             vctrs_0.4.1            png_0.1-7             \n",
       "[73] tidyselect_1.1.2      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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