proteomics_analysis_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "inst  <-  suppressMessages(lapply(c('DEP',\n",
    "                                    'SummarizedExperiment',\n",
    "                                    'pheatmap',\n",
    "                                    'ggplot2',\n",
    "                                    'EnhancedVolcano',\n",
    "                                    'VennDiagram',\n",
    "                                    'org.Mm.eg.db',\n",
    "                                    'clusterProfiler',\n",
    "                                    'readxl',\n",
    "                                    'tidyverse',\n",
    "                                    'KEGGREST'),\n",
    "                                library,\n",
    "                                character.only=TRUE)\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_palette = colorRampPalette(c(\"blue\", \"white\", \"red\"))(n=255)\n",
    "options(repr.plot.width=8, repr.plot.height=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set whether anndata objects are recomputed or loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_recomp = FALSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set whether to produce plots, set to False for test runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_plot = FALSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(bool_recomp){  \n",
    "    # load the imputed and normalized data from \n",
    "    input_file='~/Documents/consultation/Luiza/omics_data/data/astrocyteprotoeme12102017-1.xlsx'\n",
    "    datraw <- read_excel(input_file, \n",
    "                         sheet=2)[,c(4, 3, 11:57)]\n",
    "\n",
    "    dat <- apply(datraw[,-c(1,2)], \n",
    "                 2, \n",
    "                 function(x) as.numeric(x)\n",
    "    )\n",
    "    gen = as.character(as.matrix(datraw[,1]))\n",
    "\n",
    "    dat <- aggregate(dat,\n",
    "                     by=list(gen),\n",
    "                     FUN=mean,\n",
    "                     na.rm=TRUE\n",
    "    )\n",
    "    rownames(dat) = dat[,1]\n",
    "    dat = dat[,-1]\n",
    "    write.csv(dat,'dat.csv', row.names=T)\n",
    "} else{\n",
    "    dat=read.csv('dat.csv', row.names=1)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Acsa2 positive vs. negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(bool_recomp){ \n",
    "    datfin = data.frame(CTX = apply(dat %>% select(ends_with(\"CTX\")), 1, mean),\n",
    "                        HPT = apply(dat %>% select(ends_with(\"HPT\")), 1, mean),\n",
    "                        Hippo = apply(dat %>% select(ends_with(\"Hippo\")), 1, mean),\n",
    "                        CTX_FT = apply(dat %>% select(ends_with(\"CTX_FT\")), 1, mean),\n",
    "                        HPT_FT = apply(dat %>% select(ends_with(\"HPT_FT\")), 1, mean),\n",
    "                        Hippo_FT = apply(dat %>% select(ends_with(\"Hippo_FT\")), 1, mean))\n",
    "\n",
    "    astro = datfin[rownames(datfin)%in%c('Slc1a2', 'Slc1a3', 'Aqp4', 'Gfap', 'Aldh1l1', 'Gja1', 'Gjb6', 'Atp1b2'),]\n",
    "    neuro = datfin[rownames(datfin)%in%c('Syp', 'Tubb3', 'Snap25', 'Syt1'),]\n",
    "    mglia = datfin[rownames(datfin)%in%c('Itgam', 'Aif1'),]\n",
    "    oligo = datfin[rownames(datfin)%in%c('Mog', 'Mag'),]\n",
    "    endo = datfin[rownames(datfin)%in%c('Slco1c1'),]\n",
    "    mural = datfin[rownames(datfin)%in%c('Mustn1', 'Pdgfrb', 'Des'),]\n",
    "    tcyes = datfin[rownames(datfin)%in%c('Crym'),]\n",
    "    npc = datfin[rownames(datfin)%in%c('Nes', 'Sox2', 'Prom1'),]\n",
    "    vlmc = datfin[rownames(datfin)%in%c('Col1a1'),]\n",
    "    mat = as.matrix(rbind(astro, neuro, mglia, oligo, endo, mural)) \n",
    "    \n",
    "    write.csv(mat,'celltype_heatmap.csv', row.names=T)\n",
    "} else{\n",
    "    mat=read.csv('celltype_heatmap.csv', row.names=1)\n",
    "}    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = as.matrix(mat)\n",
    "pheatmap(mat, \n",
    "         scale=\"row\",\n",
    "         show_rownames=F,\n",
    "         show_colnames=F,\n",
    "         color=my_palette,\n",
    "         cluster_cols=F,\n",
    "         cluster_rows=F, \n",
    "         gaps_col=3,\n",
    "         gaps_row=c(8,12,14,16,17,20),#11, 15, 17, 19, 20, 23),\n",
    "         fontsize=18,\n",
    "         legend=T,\n",
    "         filename=\"heatmap_ACSA2.pdf\",\n",
    "         width=8,\n",
    "         height=8\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### volcano plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ft <- read_excel('~/Documents/consultation/Luiza/omics_data/data/astrocyteprotoeme12102017-1.xlsx', \n",
    "                 sheet=2)[,c(4, 6 ,10)]\n",
    "colnames(ft) = c(\"GeneName\", \"pvalue\", \"log2FoldChange\")\n",
    "for (i in 1:nrow(ft)) ft[i,2]=10 ^ -ft[i,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf(file=\"SM/volcano_acsa2posVSneg_allgene.pdf\", width=10, height=10)\n",
    "    EnhancedVolcano(ft,\n",
    "                    lab=ft$GeneName,\n",
    "                    pCutoff=0.05,\n",
    "                    x='log2FoldChange',\n",
    "                    y='pvalue',\n",
    "                    ylim=c(-0, 25),\n",
    "                    col=c(\"grey30\", \"grey30\", \"red2\", \"red2\"),\n",
    "                    title=\"Acsa2_neg vs. Acsa2_pos\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2 rows containing missing values (geom_vline).”\n"
     ]
    }
   ],
   "source": [
    "pdf(file=\"SM/volcano_acsa2posVSneg_astro.pdf\", width=10, height=10)\n",
    "    EnhancedVolcano(ft,\n",
    "                    lab=ft$GeneName,\n",
    "                    pCutoff=0.05,\n",
    "                    FCcutoff=10,\n",
    "                    x='log2FoldChange',\n",
    "                    y='pvalue',\n",
    "                    ylim=c(-0, 25),\n",
    "                    selectLab=rownames(mat),\n",
    "                    labSize=5,\n",
    "                    labCol='blue',\n",
    "                    col=c(\"grey30\", \"grey30\", \"red2\", \"red2\"),\n",
    "                    title=\"Acsa2_neg vs. Acsa2_pos\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = dat[,1:24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat1 <- read.table(file=\"/Users/viktorian.miok/Documents/data/Protein_Data/proteingroups_astrocytes_imputed.txt\",\n",
    "                   header=T, \n",
    "                   sep='\\t'\n",
    ")\n",
    "dat1 = dat1[-1, c(1:24)]\n",
    "\n",
    "dat1 <- apply(dat1, \n",
    "              2,\n",
    "              function(x) as.numeric(x)\n",
    ")\n",
    "df_pca <- prcomp(t(dat1), \n",
    "                 scale=T\n",
    ")\n",
    "\n",
    "df_out=as.data.frame(df_pca$x)\n",
    "df_out$tissue=as.factor(condition = c(rep(\"Hippo_CD\", 4), rep(\"Hippo_hfd\", 4), rep(\"HPT_CD\", 4),\n",
    "                                      rep(\"HPT_hfd\", 4), rep(\"CTX_CD\", 4), rep(\"CTX_hfd\", 4))\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"PCA.pdf\", width=10, height=10)\n",
    "    ggplot(df_out, aes(x=PC1, y=PC2, color=tissue)) + \n",
    "           geom_point(aes(color=tissue), size=10) + \n",
    "           geom_point(shape=1, color=\"black\", size=10) + \n",
    "           xlab(paste0(\"PC1: \",round(df_pca$sdev[1]), \"% variance\")) + \n",
    "           ylab(paste0(\"PC2: \",round(df_pca$sdev[2]), \"% variance\")) +\n",
    "           theme(axis.text=element_text(size=25), \n",
    "                 axis.title=element_text(size=30)) + \n",
    "           scale_color_manual(values=c(\"aquamarine4\", \"aquamarine2\", \"darkorange2\", \n",
    "                                       \"orange\",\"blueviolet\",\"mediumpurple1\")) +\n",
    "           theme(legend.position=\"none\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential gene expression - diet effect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### volcano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(bool_recomp){ \n",
    "    # load the results from Persus\n",
    "    hippo = read_excel(input_file, sheet=10)[,c(4, 6, 7)]\n",
    "    for (i in 1:nrow(hippo)) hippo[i,2]=10 ^ -hippo[i,2]\n",
    "\n",
    "    ctx = read_excel(input_file, sheet=8)[,c(4, 6, 7)]\n",
    "    for (i in 1:nrow(ctx)) ctx[i,2]=10 ^ -ctx[i,2]\n",
    "\n",
    "    hpt = read_excel(input_file, sheet=6)[,c(4, 6, 7)]\n",
    "    for (i in 1:nrow(hpt)) hpt[i,2]=10 ^ -hpt[i,2]\n",
    "    \n",
    "    colnames(hippo) = colnames(ctx) = colnames(hpt) = c(\"GeneName\", \"pvalue\", \"log2FoldChange\")\n",
    "    dge <- list(hpt=hpt,\n",
    "                hippo=hippo, \n",
    "                ctx=ctx\n",
    "    )\n",
    "    save(dge, file=\"dge.RData\")\n",
    "} else {\n",
    "    load(\"dge.RData\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2 rows containing missing values (geom_vline).”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"volcano_hippo.pdf\", width=10, height=10)\n",
    "    EnhancedVolcano(dge[['hippo']],\n",
    "                    lab=dge[['hippo']]$GeneName,\n",
    "                    x='log2FoldChange',\n",
    "                    y='pvalue',\n",
    "                    FCcutoff=10,\n",
    "                    pCutoff=0.05,\n",
    "                    axisLabSize=30,\n",
    "                    title=NULL,\n",
    "                    subtitle=NULL,\n",
    "                    caption=NULL,\n",
    "                    xlim=c(-5, 5),\n",
    "                    ylim=c(0, 5),\n",
    "                    col=c(\"grey30\", \"grey30\", \"red2\", \"red2\"),\n",
    "                    legendLabels=c('NS', expression(Log[2]~FC), 'p-value>0.05', expression(p-value~and~log[2]~FC)),\n",
    "                    legendLabSize=30\n",
    "    )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2 rows containing missing values (geom_vline).”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"volcano_ctx.pdf\", width=10, height=10)\n",
    "    EnhancedVolcano(dge[['ctx']],\n",
    "                    lab=dge[['ctx']]$GeneName,\n",
    "                    x='log2FoldChange',\n",
    "                    y='pvalue',\n",
    "                    FCcutoff=10,\n",
    "                    pCutoff=0.05,\n",
    "                    axisLabSize=30,\n",
    "                    title=NULL,\n",
    "                    subtitle=NULL,\n",
    "                    caption=NULL,\n",
    "                    xlim=c(-5, 5),\n",
    "                    ylim=c(0, 5),\n",
    "                    col=c(\"grey30\", \"grey30\", \"red2\", \"red2\"),\n",
    "                    legendLabels=c('NS', expression(Log[2]~FC), 'p-value>0.05', expression(p-value~and~log[2]~FC)),\n",
    "                    legendLabSize=30\n",
    "   )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2 rows containing missing values (geom_vline).”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"volcano_hpt.pdf\", width=10, height=10)\n",
    "    EnhancedVolcano(dge[['hpt']],\n",
    "                    lab=dge[['hpt']]$GeneName,\n",
    "                    x='log2FoldChange',\n",
    "                    y='pvalue',\n",
    "                    FCcutoff=10,\n",
    "                    pCutoff=0.05,\n",
    "                    axisLabSize=30,\n",
    "                    title=NULL,\n",
    "                    subtitle=NULL,\n",
    "                    caption=NULL,\n",
    "                    xlim=c(-5, 5),\n",
    "                    ylim=c(0, 5),\n",
    "                    col=c(\"grey30\", \"grey30\", \"red2\", \"red2\"),\n",
    "                    legendLabels=c('NS', expression(Log[2]~FC), 'p-value>0.05', expression(p-value~and~log[2]~FC)),\n",
    "                    legendLabSize=30)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"SM/Nr_dge_0.05.pdf\", width=10, height=10)\n",
    "    df = data.frame(location=c(\"Hypothalamus\", \"Hippocampus\", \"Cortex\"),\n",
    "                    Sig_genes=c(dim(dge[['hpt']][which(dge[['hpt']][,2]<0.05),])[1], \n",
    "                                dim(dge[['hippo']][which(dge[['hippo']][,2]<0.05),])[1],\n",
    "                                dim(dge[['ctx']][which(dge[['ctx']][,2]<0.05),])[1])\n",
    "    )\n",
    "    ggplot(df, aes(x=location, y=Sig_genes, fill=location)) +\n",
    "           geom_bar(stat=\"identity\") +\n",
    "           theme(axis.text=element_text(size=20), \n",
    "                  axis.title=element_text(size=20), \n",
    "                  legend.title=element_text(siz=20),\n",
    "                  legend.text=element_text(size=20),\n",
    "                  plot.title=element_text(size=20)) +\n",
    "           theme(legend.position=\"none\") +\n",
    "           scale_fill_manual(values=c(\"lightblue\",\"lightgreen\",\"tan1\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### venn diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"venn_tran.pdf\", width=8, height=8)\n",
    "vp <- venn.diagram(list(' '=as.matrix(dge[['hpt']][dge[['hpt']][,2]<0.05,1]),\n",
    "                        ' '=as.matrix(dge[['hippo']][dge[['hippo']][,2]<0.05,1]),\n",
    "                        ' '=na.omit(as.matrix(dge[['ctx']][dge[['ctx']][,2]<0.05,])[,1])), \n",
    "                 fill=c(\"orange\", \"mediumpurple1\", \"aquamarine2\"),\n",
    "                 filename=NULL, \n",
    "                 cex=3.5,\n",
    "                 col=\"transparent\",\n",
    "                 cat.cex=0.01,\n",
    "                 margin=0.01,\n",
    "                 main=NULL,\n",
    "                 main.cex=3);\n",
    "    grid.draw(vp)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(bool_recomp){ \n",
    "    datr <- read_excel(input_file,\n",
    "                       sheet=8)[,c(4,9:16)]\n",
    "    datctx <- apply(datr[,-1],\n",
    "                    2,\n",
    "                    function(x) as.numeric(x)\n",
    "    )\n",
    "    gen = as.character(as.matrix(datr[,1]))\n",
    "    datctx <- aggregate(datctx,by=list(gen),\n",
    "                        FUN=mean,\n",
    "                        na.rm=TRUE\n",
    "    )\n",
    "\n",
    "    datr <- read_excel(input_file,\n",
    "                       sheet=10)[,c(4, 9:16)]\n",
    "    dathip = apply(datr[,-1],\n",
    "                 2,\n",
    "                 function(x) as.numeric(x)\n",
    "    )\n",
    "    gen = as.character(as.matrix(datr[,1]))\n",
    "    dathip <- aggregate(dathip,\n",
    "                        by=list(gen),\n",
    "                        FUN=mean,\n",
    "                        na.rm=TRUE\n",
    "    )\n",
    "\n",
    "    datr <- read_excel(input_file, \n",
    "                       sheet=6)[,c(4,9:16)]\n",
    "    dathpt <- apply(datr[,-1],\n",
    "                    2, \n",
    "                    function(x) as.numeric(x))\n",
    "    gen = as.character(as.matrix(datr[,1]))\n",
    "    dathpt <- aggregate(dathpt,\n",
    "                        by=list(gen), \n",
    "                        FUN=mean, \n",
    "                        na.rm=TRUE\n",
    "    )\n",
    "                 \n",
    "    hm <- list(hpt=dathpt, \n",
    "               hippo=dathip, \n",
    "               ctx=datctx\n",
    "    )\n",
    "    save(hm, file=\"hm.RData\")\n",
    "} else {\n",
    "    load(\"hm.RData\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "rownames(hm[['ctx']]) = hm[['ctx']][,1]\n",
    "hm[['ctx']] = hm[['ctx']][,-1]\n",
    "vsd_ctx = hm[['ctx']][rownames(hm[['ctx']])%in%as.character(as.matrix(dge[['ctx']][dge[['ctx']][,2] < 0.05,1])),]\n",
    "\n",
    "pheatmap(vsd_ctx, \n",
    "         scale=\"row\", \n",
    "         show_rownames=FALSE,\n",
    "         show_colnames=FALSE,\n",
    "         color=my_palette,\n",
    "         cluster_cols=FALSE, \n",
    "         fontsize=15,\n",
    "         legend=FALSE,\n",
    "         filename=\"heatmap_ctx.pdf\",\n",
    "         width=5, \n",
    "         height=8,\n",
    "         breaks=seq(-2, 2, length.out=255)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(hm[['hippo']]) = hm[['hippo']][,1]\n",
    "hm[['hippo']] = hm[['hippo']][,-1]\n",
    "vsd_hip = hm[['hippo']][rownames(hm[['hippo']])%in%as.character(as.matrix(dge[['hippo']][dge[['hippo']][,2] < 0.05,1])),]\n",
    "\n",
    "pheatmap(vsd_hip,\n",
    "         scale=\"row\",\n",
    "         show_rownames=FALSE,\n",
    "         show_colnames=FALSE,\n",
    "         color=my_palette, \n",
    "         cluster_cols=FALSE,\n",
    "         fontsize=15,\n",
    "         legend=FALSE,\n",
    "         filename=\"heatmap_hippo.pdf\",\n",
    "         width=5, \n",
    "         height=8,\n",
    "         breaks=seq(-2, 2, length.out=255)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(hm[['hpt']]) = hm[['hpt']][,1]\n",
    "hm[['hpt']] = hm[['hpt']][,-1]\n",
    "vsd_hpt = hm[['hpt']][rownames(hm[['hpt']])%in%as.character(as.matrix(dge[['hpt']][dge[['hpt']][,2] < 0.05,1])),]\n",
    "\n",
    "pheatmap(vsd_hpt,\n",
    "         scale=\"row\",\n",
    "         show_rownames=FALSE,\n",
    "         show_colnames=FALSE,\n",
    "         color=my_palette,\n",
    "         cluster_cols=F,\n",
    "         fontsize=15,\n",
    "         legend=T,\n",
    "         filename=\"heatmap_hpt.pdf\",\n",
    "         width=5, \n",
    "         height=8,\n",
    "         breaks=seq(-2, 2, length.out=255)\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GO - pathway enrichment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(bool_recomp){\n",
    "    sg.ctx <- bitr(rownames(vsd_ctx), \n",
    "                   fromType=\"SYMBOL\",\n",
    "                   toType=\"ENTREZID\",\n",
    "                   OrgDb=org.Mm.eg.db\n",
    "    )\n",
    "    go.ctx <- enrichGO(sg.ctx[,2],\n",
    "                       'org.Mm.eg.db',\n",
    "                       ont=\"BP\", \n",
    "                       pvalueCutoff=0.1\n",
    "    )\n",
    "    sg.hpt <- bitr(rownames(vsd_hpt),\n",
    "                   fromType=\"SYMBOL\",\n",
    "                   toType=\"ENTREZID\",\n",
    "                   OrgDb=org.Mm.eg.db\n",
    "    )\n",
    "    go.hpt <- enrichGO(sg.hpt[,2],\n",
    "                       'org.Mm.eg.db', \n",
    "                       ont=\"BP\",\n",
    "                       pvalueCutoff=0.1\n",
    "    )\n",
    "    sg.hip <- bitr(rownames(vsd_hip),\n",
    "                   fromType=\"SYMBOL\",\n",
    "                   toType=\"ENTREZID\",\n",
    "                   OrgDb=org.Mm.eg.db\n",
    "    )\n",
    "    go.hip <- enrichGO(sg.hip[,2],\n",
    "                       'org.Mm.eg.db',\n",
    "                       ont=\"BP\", \n",
    "                       pvalueCutoff=0.1\n",
    "    )\n",
    "    go = c(\"insulin receptor signaling pathway\",\n",
    "           \"leptin-mediated signaling pathway\",\n",
    "           \"response to leptin\",\n",
    "           \"glycolytic process\",\n",
    "           \"cellular response to glucose stimulus\",\n",
    "           \"cellular carbohydrate metabolic process\",\n",
    "           \"glycogen metabolic process\",\n",
    "           \"glucose transmembrane transport\",\n",
    "           \"fatty acid catabolic process\",\n",
    "           \"lipid import into cell\",\n",
    "           \"response to endoplasmic reticulum stress\",\n",
    "           \"response to reactive oxygen species\",\n",
    "           \"response to oxidative stress\",\n",
    "           \"positive regulation of G1/S transition of mitotic cell cycle\",\n",
    "           #\"glial cell proliferation\",\n",
    "           \"microtubule cytoskeleton organization involved in mitosis\",\n",
    "           \"smoothened signaling pathway\",\n",
    "           \"positive regulation of inflammatory response\",\n",
    "           \"cell-substrate junction assembly\",\n",
    "           \"regulation of cell shape\",\n",
    "           \"calcium ion transport into cytosol\",\n",
    "           \"calcium-mediated signaling\",\n",
    "           \"axon guidance\",\n",
    "           \"regulation of neurotransmitter levels\",\n",
    "           \"glutamate receptor signaling pathway\",\n",
    "           \"axonogenesis\",\n",
    "           \"regulation of synaptic transmission, dopaminergic\",\n",
    "           \"regulation of neuronal synaptic plasticity\"\n",
    "    )\n",
    "    enr_ctx.go = go.ctx@result[go.ctx@result[,5]<0.05, c(2, 5, 6, 8, 9)]\n",
    "    enr_ctx.go$Tissue = 'CTX'\n",
    "    enr_hpt.go = go.hpt@result[go.hpt@result[,5]<0.05, c(2, 5, 6, 8, 9)]\n",
    "    enr_hpt.go$Tissue = 'HPT'\n",
    "    enr_hip.go = go.hip@result[go.hip@result[,5]<0.05, c(2, 5, 6, 8, 9)]\n",
    "    enr_hip.go$Tissue = 'Hippo'\n",
    "    \n",
    "    all <- rbind(enr_ctx.go, enr_hpt.go, enr_hip.go)\n",
    "    write.csv(all, file=\"GO_proteome.csv\")\n",
    "    all <- all[all$Description%in%go,]\n",
    "    colnames(all) = c('Description', 'pvalue', 'p.adjust', 'geneID', 'count', 'Tissue')\n",
    "    \n",
    "    all = rbind(enr_ctx.go, enr_hpt.go, enr_hip.go)\n",
    "    all1 = all[all$Description%in%go,]\n",
    "    \n",
    "    sig.gene.hpt.all <- bitr(dge[['hpt']]$GeneName,\n",
    "                             fromType=\"SYMBOL\",\n",
    "                             toType=\"ENTREZID\",\n",
    "                             OrgDb=org.Mm.eg.db\n",
    "    )\n",
    "    sig.gene.ctx.all <- bitr(dge[['ctx']]$GeneName, \n",
    "                             fromType=\"SYMBOL\", \n",
    "                             toType=\"ENTREZID\", \n",
    "                             OrgDb=org.Mm.eg.db\n",
    "    )\n",
    "    sig.gene.hip.all <- bitr(dge[['hippo']]$GeneName,\n",
    "                             fromType=\"SYMBOL\",\n",
    "                             toType=\"ENTREZID\",\n",
    "                             OrgDb=org.Mm.eg.db\n",
    "    )\n",
    "    # CTX\n",
    "    df_total.ctx = data.frame()\n",
    "    for(i in 1:nrow(all1)){   \n",
    "        p = as.data.frame(dge[['ctx']][which(dge[['ctx']]$GeneName%in%\n",
    "                          sig.gene.ctx.all[sig.gene.ctx.all[,2]%in%unlist(strsplit(all1$geneID[i],\"/\")),1]), c(2,3)])\n",
    "        p$pathway = all1$Description[i]\n",
    "        p$padjPath = all1$p.adjust[i]\n",
    "        p$count = all1$Count[i]\n",
    "        df_total.ctx = rbind(df_total.ctx, p)    \n",
    "    }\n",
    "    df_total.ctx$significance <- ifelse(df_total.ctx$pvalue<0.05,\n",
    "                                        \"significant\",\n",
    "                                        \"not significant\"\n",
    "    )\n",
    "    # HPT\n",
    "    df_total.hpt=data.frame()\n",
    "    for(i in 1:nrow(all1)){\n",
    "        p = as.data.frame(dge[['hpt']][which(dge[['hpt']]$GeneName%in%\n",
    "                          sig.gene.hpt.all[sig.gene.hpt.all[,2]%in%unlist(strsplit(all1$geneID[i],\"/\")),1]), c(2,3)])\n",
    "        p$pathway = all1$Description[i]\n",
    "        p$tissue = all1$Tissue[i]\n",
    "        p$padjPath = all1$pvalue[i]\n",
    "        p$count = all1$Count[i]\n",
    "        df_total.hpt = rbind(df_total.hpt, p)    \n",
    "    } \n",
    "    for(j in 1:nrow(df_total.hpt)) {if((df_total.hpt$tissue[j]=='HPT')&(df_total.hpt$pvalue[j] > 0.05)) df_total.hpt$pvalue[j]<-0.05}\n",
    "    df_total.hpt$significance <- ifelse(df_total.hpt$pvalue <= 0.05,\n",
    "                                        \"significant\",\n",
    "                                        \"not significant\"\n",
    "    )\n",
    "    # Hippo\n",
    "    df_total.hip = data.frame()\n",
    "    for(i in 1:nrow(all1)){\n",
    "        p = as.data.frame(dge[['hippo']][which(dge[['hippo']]$GeneName%in%\n",
    "                        sig.gene.hip.all[sig.gene.hip.all[,2]%in%unlist(strsplit(all1$geneID[i],\"/\")),1]),c(2,3)])\n",
    "        p$pathway = all1$Description[i]\n",
    "        p$padjPath = all1$p.adjust[i]\n",
    "        p$count = all1$Count[i]\n",
    "        df_total.hip = rbind(df_total.hip,p)    \n",
    "    }    \n",
    "    df_total.hip$col <- ifelse(df_total.hip$pvalue<=0.05,\n",
    "                               \"red\",\n",
    "                               \"grey\"\n",
    "    )\n",
    "    gsea <- list(all=all1,\n",
    "                 ctx=df_total.ctx,\n",
    "                 hpt=df_total.hpt,\n",
    "                 hippo=df_total.hip,\n",
    "                 go=go\n",
    "    )\n",
    "    save(gsea, file=\"gsea.RData\")\n",
    "} else {\n",
    "    load(\"gsea.RData\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"enrich_path.pdf\", width=15, height=15)\n",
    "    ggplot(gsea[['all']],   # \n",
    "           aes(Tissue, y=factor(Description, level=gsea[['go']][length(gsea[['go']]):1]), col=pvalue, size=Count)) + \n",
    "           geom_point() + \n",
    "           scale_colour_gradient(low=\"red\", high=\"blue\") +\n",
    "           scale_size_continuous(range=c(3, 10)) +\n",
    "           xlab(\"\") + \n",
    "           ylab(\"\") + \n",
    "           theme(axis.text.x=element_blank()) + \n",
    "           theme(legend.position=\"none\") +\n",
    "           theme(text=element_text(size=40))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"enrichdot_ctx.pdf\", width=7, height=15)\n",
    "    ggplot(gsea[['ctx']], aes(log2FoldChange, y=factor(pathway, level=gsea[['go']][length(gsea[['go']]):1]))) + \n",
    "           geom_point(aes(colour=significance, size=-log10(pvalue)), alpha=0.5) + \n",
    "           scale_color_manual(values=c(\"gray85\", \"red\")) + \n",
    "           scale_size_continuous(range=c(0, 7.5)) +  #\n",
    "           geom_vline(xintercept=0) +\n",
    "           theme(axis.title=element_text(size=20),\n",
    "                 title=element_text(size=20), \n",
    "                 text = element_text(size=35)) + #guides(color=guide_legend(override.aes=list(size=5) ) ) +\n",
    "           theme(legend.position=\"none\") + \n",
    "           theme(axis.text.y=element_blank()) + \n",
    "           ylab(\"\") +\n",
    "           xlab(\"\") + \n",
    "           xlim(-2.68, 3.82)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"enrichdot_hpt.pdf\", width=7, height=15)\n",
    "    ggplot(gsea[['hpt']], aes(log2FoldChange, y=factor(pathway, level=gsea[['go']][length(gsea[['go']]):1]))) + \n",
    "           geom_point(aes(colour=significance, size= -log10(pvalue)), alpha=0.5) + \n",
    "           scale_color_manual(values=c(\"gray85\", \"red\")) + \n",
    "           scale_size_continuous(range=c(0, 8.5)) +  #\n",
    "           geom_vline(xintercept=0) +\n",
    "           theme(axis.title=element_text(size=20),\n",
    "                 title=element_text(size=20), \n",
    "                 text=element_text(size=35)) + \n",
    "           theme(legend.position=\"none\") + \n",
    "           theme(axis.text.y=element_blank()) + \n",
    "           ylab(\"\") +\n",
    "           xlab(\"\") + \n",
    "           xlim(-2.68, 3.82)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"enrichdot_hippo.pdf\", width=7, height=15)\n",
    "\n",
    "    ggplot(gsea[['hippo']], aes(log2FoldChange, y=factor(pathway, level=gsea[['go']][length(gsea[['go']]):1]))) + \n",
    "           geom_point(aes(colour=col, size=-log10(pvalue)), alpha=0.5) + \n",
    "           scale_color_manual(values=c(\"gray85\", \"red\"))+ \n",
    "           scale_size_continuous(range = c(0, 7.5)) +\n",
    "           geom_vline(xintercept=0) +\n",
    "           theme(axis.title=element_text(size=20),\n",
    "                 title=element_text(size=20), \n",
    "                 text = element_text(size=35)) +\n",
    "           theme(legend.position=\"none\") + \n",
    "           theme(axis.text.y=element_blank()) + \n",
    "           ylab(\"\") + \n",
    "           xlab(\"\") + \n",
    "           xlim(-2.68, 3.82)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap microglia genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "datfin = data.frame(CTX_chow = apply(dat %>% select(ends_with(\"CTX\") & starts_with(\"Chow\")), 1, mean),\n",
    "                    CTX_hfd = apply(dat %>% select(ends_with(\"CTX\") & starts_with(\"HFD\")), 1, mean),\n",
    "                    HPT_chow = apply(dat %>% select(ends_with(\"HPT\") & starts_with(\"Chow\")), 1, mean),\n",
    "                    HPT_hfd = apply(dat %>% select(ends_with(\"HPT\") & starts_with(\"HFD\")), 1, mean),\n",
    "                    Hippo_chow = apply(dat %>% select(ends_with(\"Hippo\") & starts_with(\"Chow\")), 1, mean),\n",
    "                    Hippo_hfd = apply(dat %>% select(ends_with(\"Hippo\") & starts_with(\"HFD\")), 1, mean))\n",
    "\n",
    "\n",
    "pan = datfin[rownames(datfin)%in%c('Lcn2','Steap4','S1pr3','Timp1','Hsbp1','Cxcl10','Cd44',\n",
    "                                   'Osmr','Cp','Serpina3n','Aspg','Vim','Gfap'),]\n",
    "a1 = datfin[rownames(datfin)%in%c('Serping1','H2-D1','Ggta1','Iigp1','Gbp2','Fbln5','Fkbp5','Psmb8',\n",
    "                                  'Srgn','Amigo2','C3','H2-T23'),]\n",
    "a2 = datfin[rownames(datfin)%in%c('Clcf1','Tgm1','Ptx3','S100a10','Sphk1','Cd109','Ptgs2',\n",
    "                                  'Emp1','Slc10a6','Tm4sf1','B3gnt5','Cd14','Stat3'),]\n",
    "\n",
    "pan = pan[order(row.names(pan)),]\n",
    "a1 = a1[order(row.names(a1)),]\n",
    "a2 = a2[order(row.names(a2)),]\n",
    "mat = rbind(pan, a1, a2)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=5)\n",
    "mat1 = as.matrix(t(mat))\n",
    "pheatmap(mat1, \n",
    "         scale=\"column\",\n",
    "         show_rownames=T, \n",
    "         show_colnames=T,\n",
    "         color=my_palette, \n",
    "         cluster_cols=F,\n",
    "         cluster_rows=F, \n",
    "         gaps_col=c(6,9),\n",
    "         gaps_row=c(2,4,6), \n",
    "         fontsize=18,\n",
    "         #filename=\"heatmap_ACSA2.pdf\",\n",
    "         legend=F,\n",
    "         width=5, \n",
    "         height=8\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}