200401 scRNAseq_all_IEGs_comparison_statistics_Fig7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 method overwritten by 'R.oo':\n",
      "  method        from       \n",
      "  throw.default R.methodsS3\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style> .container { width:95% !important; } </style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(Seurat)\n",
    "library(dplyr)\n",
    "library(magrittr)\n",
    "IRdisplay::display_html(\"<style> .container { width:95% !important; } </style>\")\n",
    "library(\"ggplot2\")\n",
    "library(\"xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LHb.integrated<-readRDS(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/LHb_integrated_0.8.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cntl<-readRDS(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/cntl_0.8.rds\")\n",
    "stim<-readRDS(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/stim_0.8.rds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# assgin clusters to cell types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "new.ident <- c(\"MHb1\",\"MHb2\",\"MHb3\",\"LHb1\",\"LHb2\",\"LHb3\",\"MHb4\",\"LHb4\",\"MHb5\",\"LHb5\",\"LHb6\",\"MHb6\",\"Surr1\",\"Surr2\",\"Surr3\",\"Surr4\")\n",
    "names(x = new.ident) <- levels(x =LHb.integrated)\n",
    "LHb.integrated<- RenameIdents(object =LHb.integrated, new.ident)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (i in 1:length(new.ident)){\n",
    "assign(paste(new.ident[i],\"_barcode\",sep=\"\"),colnames(LHb.integrated@assays$RNA@data[,which(Idents(object=LHb.integrated) %in% new.ident[i])]))# this gives all barcodes in cluster\n",
    "assign(paste(new.ident[i],\"_barcode_cntl\",sep=\"\"),intersect(colnames(cntl@assays$RNA@data),eval(parse(text = paste(new.ident[i],\"_barcode\",sep=\"\")))))\n",
    "assign(paste(new.ident[i],\"_barcode_stim\",sep=\"\"),intersect(colnames(stim@assays$RNA@data),eval(parse(text = paste(new.ident[i],\"_barcode\",sep=\"\")))))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "LHb.integrated<-subset(x=LHb.integrated,cells=c(MHb1_barcode,MHb2_barcode,MHb3_barcode,LHb1_barcode,LHb2_barcode,LHb3_barcode,MHb4_barcode,MHb5_barcode,LHb4_barcode,LHb5_barcode,MHb6_barcode,LHb6_barcode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyzing all IEGs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MHb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# used https://www.sciencedirect.com/science/article/pii/S0092867419310712\n",
    "IEG<-read.xlsx(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/IEG.xlsx\",sheetIndex=1)\n",
    "IEG<-as.character(IEG[[1]])\n",
    "IEG<-intersect(IEG,rownames(LHb.integrated@assays$RNA@data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”"
     ]
    }
   ],
   "source": [
    "Cell_type<-c(\"MHb1\",\"MHb2\",\"MHb3\",\"MHb4\",\"MHb5\",\"MHb6\")\n",
    "# make a data frame for cell number (this data frame is a simple version giving single value for each cluster) \n",
    "Cell_number<- data.frame(\"type\" =c(rep(\"MHb1\",length(IEG)),rep(\"MHb2\",length(IEG)),rep(\"MHb3\",length(IEG)),rep(\"MHb4\",length(IEG)),rep(\"MHb5\",length(IEG)),rep(\"MHb6\",length(IEG))), \"gene\" = rep(IEG,6))\n",
    "\n",
    "E_p<-vector(mode=\"numeric\", length=0) # expression level p value\n",
    "E_change<-vector(mode=\"numeric\", length=0) # Expression level change\n",
    "\n",
    "for (i in 1:dim(Cell_number)[1]){\n",
    "    c_sum<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)\n",
    "    s_sum<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)\n",
    "    temp_c<-LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))][LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0]\n",
    "    temp_s<-LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))][LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0]\n",
    "    if(c_sum>0 & s_sum>0){\n",
    "        res <-wilcox.test(temp_c, temp_s, alternative = \"two.sided\")\n",
    "        if(length(Cell_type)*res$p.value>1){E_p[i]<- -log(1)}\n",
    "        else{E_p[i]<- -log(length(Cell_type)*res$p.value)}\n",
    "    }else if(c_sum==0 | s_sum==0){E_p[i]<- -log(1)}\n",
    "}\n",
    "Cell_number$E_p<-E_p\n",
    "for (i in 1:dim(Cell_number)[1]){if(sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)==0 & sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)==0)\n",
    "{E_change[i]<-0}else if(sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)==0){E_change[i]<-0-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)}\n",
    "   else if(sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)==0){E_change[i]<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)}\n",
    "    else{E_change[i]<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)}}\n",
    "Cell_number$E_change<-E_change\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MHb<-subset(Cell_number,type==\"MHb1\"|type==\"MHb2\"|type==\"MHb3\"|type==\"MHb4\"|type==\"MHb5\"|type==\"MHb6\")\n",
    "MHb$type<-factor(MHb$type,levels=rev(c(\"MHb1\",\"MHb2\",\"MHb3\",\"MHb4\",\"MHb5\",\"MHb6\")))\n",
    "E_change_p<-numeric()\n",
    "for(i in 1:dim(MHb)[1]){\n",
    "    if(MHb$E_p[i]> -log(0.05)){E_change_p[i]<-MHb$E_change[i]}else{E_change_p[i]<- 0}\n",
    "}\n",
    "MHb$E_change_p<-E_change_p\n",
    "MHb<-subset(MHb,gene==\"Arc\"|gene==\"Fosb\"|gene==\"Fos\"|gene==\"Junb\"|gene==\"Jund\"|gene==\"Egr1\"|gene==\"Homer1\"|gene==\"Nr4a2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(scales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nEwQ7AAAAmSDYAQAAyMT/ABrToZI87Wb2AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <-ggplot(MHb, aes(gene,type, fill= E_change_p)) + \n",
    "  geom_tile() + scale_fill_gradient(limits=c(0.1, 0.6), breaks=seq(0.1,0.6,by=0.2),low = \"white\",high = \"red\", oob=squish)\n",
    "pp<-p+ylab(\"Cell type\")+xlab(\"IEGs\")+theme(axis.title=element_text(size=20,face=\"bold\"),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\", size = 0.1),axis.ticks.y = element_blank())+ggtitle(\"IEGs\")\n",
    "pp\n",
    "#ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/IEG heatmap MHb2.pdf\",width=10,height=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 15 rows containing missing values (geom_point).”Warning message:\n",
      "“Removed 15 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#MHb1, expression\n",
    "color<- vector(mode=\"numeric\", length=0)\n",
    "MHb<- subset(Cell_number,type==\"MHb1\")\n",
    "for(i in 1:dim(MHb)[1]){\n",
    "    \n",
    "    if(MHb$gene[i]==\"Fos\"){color[i]<-\"yellow\"}\n",
    "    else if(MHb$gene[i]==\"Egr1\"){color[i]<-\"blue\"}      \n",
    "     else if (MHb$E_p[i] > -log(0.05) & MHb$E_change[i]>0.15 ){color[i]<- \"red\"}\n",
    "    else{color[i]<-\"black\"}\n",
    "}\n",
    "MHb$color<-color\n",
    "\n",
    "ggplot(MHb,aes(x=E_change,y=E_p,color=color))+geom_point(size=4)+ylab(\"- log p-vlaue\")+xlab(\"change (%)\")+scale_color_manual(values=c(\"black\",\"blue\",\"red\",\"yellow\"))+theme(axis.text.x=element_text(size=10,angle = 50, hjust = 1))+ggtitle(\"IEG proportion MHb1\")+\n",
    "theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=12),axis.text.x=element_text(size=15,angle = 50, hjust = 1),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+xlim(-0.5,1)+ylim(0,18)+ geom_hline(yintercept=-log(0.05), linetype=\"dashed\", color = \"black\")+ geom_vline(xintercept =c(-0.15,0.15), linetype=\"dashed\",color = \"black\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/MHb1_Expression_level_supple6_0.15_test.pdf\",width=10,height=10) # may use +scale_fill_manual(values=c(\"grey\", \"black\"))\n",
    "write.xlsx2(MHb,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/MHb1_Expression_level_supple6_axis.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>type</th><th scope=col>gene</th><th scope=col>P_change</th><th scope=col>P_p</th><th scope=col>E_p</th><th scope=col>E_change</th><th scope=col>color</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>129</th><td>MHb2       </td><td>Fos        </td><td> 2.49733516</td><td>0.0000000  </td><td> 5.1734978 </td><td> 0.34056693</td><td>red        </td></tr>\n",
       "\t<tr><th scope=row>130</th><td>MHb2       </td><td>Fosb       </td><td>-0.38640171</td><td>0.0000000  </td><td>-0.3923930 </td><td>-0.14054065</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>131</th><td>MHb2       </td><td>Fosl1      </td><td> 0.25125628</td><td>0.0000000  </td><td> 0.0000000 </td><td> 1.47563658</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>132</th><td>MHb2       </td><td>Fosl2      </td><td>-0.25506319</td><td>0.0000000  </td><td>-1.3862944 </td><td> 0.22734248</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>133</th><td>MHb2       </td><td>Jun        </td><td> 4.38366073</td><td>2.5460034  </td><td>-0.2483728 </td><td> 0.03645498</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>134</th><td>MHb2       </td><td>Junb       </td><td> 1.75118014</td><td>0.0000000  </td><td>-1.4398750 </td><td>-0.05633340</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>135</th><td>MHb2       </td><td>Jund       </td><td>-8.37330592</td><td>1.6554300  </td><td> 5.8722091 </td><td> 0.07432942</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>136</th><td>MHb2       </td><td>Egr1       </td><td> 0.73473428</td><td>0.0000000  </td><td> 0.6279802 </td><td> 0.13035000</td><td>blue       </td></tr>\n",
       "\t<tr><th scope=row>137</th><td>MHb2       </td><td>Egr2       </td><td> 0.00000000</td><td>0.0000000  </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>138</th><td>MHb2       </td><td>Egr4       </td><td> 0.25125628</td><td>0.0000000  </td><td> 0.0000000 </td><td> 1.30103111</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>139</th><td>MHb2       </td><td>Nr4a1      </td><td>-0.01142074</td><td>0.0000000  </td><td> 0.9212983 </td><td> 0.17008440</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>140</th><td>MHb2       </td><td>Nr4a2      </td><td> 1.27150906</td><td>0.0000000  </td><td> 0.7291733 </td><td> 0.06268799</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>141</th><td>MHb2       </td><td>Nr4a3      </td><td> 1.99862951</td><td>0.0000000  </td><td>-0.6499995 </td><td>-0.12320910</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>142</th><td>MHb2       </td><td>Arc        </td><td> 4.61968936</td><td>1.3064519  </td><td> 4.3905063 </td><td> 0.11803902</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>143</th><td>MHb2       </td><td>Homer1     </td><td>-1.05070809</td><td>0.0000000  </td><td> 4.2643059 </td><td> 0.14556440</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>144</th><td>MHb2       </td><td>Rheb       </td><td> 3.31582153</td><td>0.0000000  </td><td> 5.3797227 </td><td> 0.09615673</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>145</th><td>MHb2       </td><td>Rgs2       </td><td> 2.29366530</td><td>0.0000000  </td><td> 8.0302289 </td><td> 0.16396755</td><td>red        </td></tr>\n",
       "\t<tr><th scope=row>146</th><td>MHb2       </td><td>Plk2       </td><td>-2.73907416</td><td>0.0000000  </td><td> 7.9424006 </td><td> 0.15010880</td><td>red        </td></tr>\n",
       "\t<tr><th scope=row>147</th><td>MHb2       </td><td>Ptgs2      </td><td> 0.00000000</td><td>0.0000000  </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>148</th><td>MHb2       </td><td>Bdnf       </td><td>-0.25506319</td><td>0.0000000  </td><td>-0.6931472 </td><td>-0.23507667</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>149</th><td>MHb2       </td><td>Inhba      </td><td> 0.50251256</td><td>0.0000000  </td><td> 0.0000000 </td><td> 1.39488577</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>150</th><td>MHb2       </td><td>Nptx2      </td><td> 1.94533272</td><td>0.0000000  </td><td> 5.9771775 </td><td> 0.09919529</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>151</th><td>MHb2       </td><td>Plat       </td><td> 0.37498097</td><td>0.0000000  </td><td>-1.0986123 </td><td> 0.18716109</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>152</th><td>MHb2       </td><td>Nrn1       </td><td>-3.86972743</td><td>0.9149738  </td><td> 0.7055547 </td><td> 0.06766979</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>153</th><td>MHb2       </td><td>Myc        </td><td>-0.37878788</td><td>0.0000000  </td><td> 0.0000000 </td><td>-1.05989943</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>154</th><td>MHb2       </td><td>Dusp1      </td><td> 0.49870565</td><td>0.0000000  </td><td>-0.1335314 </td><td> 0.19385054</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>155</th><td>MHb2       </td><td>Dusp5      </td><td>-3.69080250</td><td>0.5990911  </td><td> 3.5855677 </td><td> 0.11833803</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>156</th><td>MHb2       </td><td>Dusp6      </td><td> 0.62623725</td><td>0.0000000  </td><td>-1.7917595 </td><td> 0.04901783</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>157</th><td>MHb2       </td><td>Pcdh8      </td><td>-0.52535404</td><td>0.0000000  </td><td> 3.8051726 </td><td> 0.15753838</td><td>red        </td></tr>\n",
       "\t<tr><th scope=row>158</th><td>MHb2       </td><td>Cyr61      </td><td>-0.37878788</td><td>0.0000000  </td><td> 0.0000000 </td><td>-2.13979048</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>227</th><td>MHb2       </td><td>Cxcl10     </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>228</th><td>MHb2       </td><td>Gbp2       </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>229</th><td>MHb2       </td><td>Clec4e     </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>230</th><td>MHb2       </td><td>Mmp13      </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>231</th><td>MHb2       </td><td>Il23a      </td><td>-0.3787879 </td><td>0.00000000 </td><td> 0.0000000 </td><td>-1.11556185</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>232</th><td>MHb2       </td><td>Arhgef3    </td><td> 0.2512563 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 1.15110820</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>233</th><td>MHb2       </td><td>Serpine1   </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>234</th><td>MHb2       </td><td>Traf1      </td><td>-0.1275316 </td><td>0.00000000 </td><td>-1.7917595 </td><td> 0.21906415</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>235</th><td>MHb2       </td><td>Vcam1      </td><td>-0.7575758 </td><td>0.04915679 </td><td> 0.0000000 </td><td>-1.03094962</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>236</th><td>MHb2       </td><td>Marcksl1   </td><td>-3.9725141 </td><td>0.04947343 </td><td>-0.2373420 </td><td> 0.03743812</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>237</th><td>MHb2       </td><td>Nfkbid     </td><td>-0.6376580 </td><td>0.00000000 </td><td> 1.0986123 </td><td> 0.19165546</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>238</th><td>MHb2       </td><td>Ikbke      </td><td>-0.3787879 </td><td>0.00000000 </td><td> 0.0000000 </td><td>-1.09238463</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>239</th><td>MHb2       </td><td>Ccl12      </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>240</th><td>MHb2       </td><td>Ifit3      </td><td>-0.3787879 </td><td>0.00000000 </td><td> 0.0000000 </td><td>-0.71270519</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>241</th><td>MHb2       </td><td>Cebpb      </td><td>-6.2147861 </td><td>3.47565898 </td><td> 1.4152217 </td><td> 0.08808224</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>242</th><td>MHb2       </td><td>Zfp36l1    </td><td>-0.8851074 </td><td>0.00000000 </td><td>-1.7917595 </td><td>-0.07588597</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>243</th><td>MHb2       </td><td>Txnip      </td><td> 0.3749810 </td><td>0.00000000 </td><td>-1.0986123 </td><td>-0.15334003</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>244</th><td>MHb2       </td><td>Nfib       </td><td>-4.2961017 </td><td>0.00000000 </td><td> 3.6198873 </td><td> 0.06658002</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>245</th><td>MHb2       </td><td>Hes1       </td><td>-0.2550632 </td><td>0.00000000 </td><td>-0.6931472 </td><td> 0.25236867</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>246</th><td>MHb2       </td><td>Pias1      </td><td> 3.8659205 </td><td>0.73348945 </td><td> 8.5045981 </td><td> 0.19131350</td><td>red        </td></tr>\n",
       "\t<tr><th scope=row>247</th><td>MHb2       </td><td>Klf2       </td><td>-0.2550632 </td><td>0.00000000 </td><td>-1.3862944 </td><td>-0.07876607</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>248</th><td>MHb2       </td><td>Dusp2      </td><td>-0.3787879 </td><td>0.00000000 </td><td> 0.0000000 </td><td>-0.71270519</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>249</th><td>MHb2       </td><td>Wee1       </td><td> 0.2512563 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 1.31322083</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>250</th><td>MHb2       </td><td>Thbs1      </td><td>-0.1275316 </td><td>0.00000000 </td><td>-1.7917595 </td><td>-0.10140492</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>251</th><td>MHb2       </td><td>Sik1       </td><td> 3.5842089 </td><td>0.00000000 </td><td>10.9877902 </td><td> 0.22567133</td><td>red        </td></tr>\n",
       "\t<tr><th scope=row>252</th><td>MHb2       </td><td>Gdf15      </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>253</th><td>MHb2       </td><td>Ier5       </td><td>-1.9396224 </td><td>0.00000000 </td><td> 2.2759443 </td><td> 0.07786422</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>254</th><td>MHb2       </td><td>Rgs1       </td><td> 0.0000000 </td><td>0.00000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>255</th><td>MHb2       </td><td>Id2        </td><td>-0.1275316 </td><td>0.00000000 </td><td>-1.7917595 </td><td> 0.49235530</td><td>black      </td></tr>\n",
       "\t<tr><th scope=row>256</th><td>MHb2       </td><td>Apold1     </td><td>-0.1275316 </td><td>0.00000000 </td><td>-1.7917595 </td><td>-0.87187387</td><td>black      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       "  & type & gene & P\\_change & P\\_p & E\\_p & E\\_change & color\\\\\n",
       "\\hline\n",
       "\t129 & MHb2        & Fos         &  2.49733516 & 0.0000000   &  5.1734978  &  0.34056693 & red        \\\\\n",
       "\t130 & MHb2        & Fosb        & -0.38640171 & 0.0000000   & -0.3923930  & -0.14054065 & black      \\\\\n",
       "\t131 & MHb2        & Fosl1       &  0.25125628 & 0.0000000   &  0.0000000  &  1.47563658 & black      \\\\\n",
       "\t132 & MHb2        & Fosl2       & -0.25506319 & 0.0000000   & -1.3862944  &  0.22734248 & black      \\\\\n",
       "\t133 & MHb2        & Jun         &  4.38366073 & 2.5460034   & -0.2483728  &  0.03645498 & black      \\\\\n",
       "\t134 & MHb2        & Junb        &  1.75118014 & 0.0000000   & -1.4398750  & -0.05633340 & black      \\\\\n",
       "\t135 & MHb2        & Jund        & -8.37330592 & 1.6554300   &  5.8722091  &  0.07432942 & black      \\\\\n",
       "\t136 & MHb2        & Egr1        &  0.73473428 & 0.0000000   &  0.6279802  &  0.13035000 & blue       \\\\\n",
       "\t137 & MHb2        & Egr2        &  0.00000000 & 0.0000000   &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t138 & MHb2        & Egr4        &  0.25125628 & 0.0000000   &  0.0000000  &  1.30103111 & black      \\\\\n",
       "\t139 & MHb2        & Nr4a1       & -0.01142074 & 0.0000000   &  0.9212983  &  0.17008440 & black      \\\\\n",
       "\t140 & MHb2        & Nr4a2       &  1.27150906 & 0.0000000   &  0.7291733  &  0.06268799 & black      \\\\\n",
       "\t141 & MHb2        & Nr4a3       &  1.99862951 & 0.0000000   & -0.6499995  & -0.12320910 & black      \\\\\n",
       "\t142 & MHb2        & Arc         &  4.61968936 & 1.3064519   &  4.3905063  &  0.11803902 & black      \\\\\n",
       "\t143 & MHb2        & Homer1      & -1.05070809 & 0.0000000   &  4.2643059  &  0.14556440 & black      \\\\\n",
       "\t144 & MHb2        & Rheb        &  3.31582153 & 0.0000000   &  5.3797227  &  0.09615673 & black      \\\\\n",
       "\t145 & MHb2        & Rgs2        &  2.29366530 & 0.0000000   &  8.0302289  &  0.16396755 & red        \\\\\n",
       "\t146 & MHb2        & Plk2        & -2.73907416 & 0.0000000   &  7.9424006  &  0.15010880 & red        \\\\\n",
       "\t147 & MHb2        & Ptgs2       &  0.00000000 & 0.0000000   &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t148 & MHb2        & Bdnf        & -0.25506319 & 0.0000000   & -0.6931472  & -0.23507667 & black      \\\\\n",
       "\t149 & MHb2        & Inhba       &  0.50251256 & 0.0000000   &  0.0000000  &  1.39488577 & black      \\\\\n",
       "\t150 & MHb2        & Nptx2       &  1.94533272 & 0.0000000   &  5.9771775  &  0.09919529 & black      \\\\\n",
       "\t151 & MHb2        & Plat        &  0.37498097 & 0.0000000   & -1.0986123  &  0.18716109 & black      \\\\\n",
       "\t152 & MHb2        & Nrn1        & -3.86972743 & 0.9149738   &  0.7055547  &  0.06766979 & black      \\\\\n",
       "\t153 & MHb2        & Myc         & -0.37878788 & 0.0000000   &  0.0000000  & -1.05989943 & black      \\\\\n",
       "\t154 & MHb2        & Dusp1       &  0.49870565 & 0.0000000   & -0.1335314  &  0.19385054 & black      \\\\\n",
       "\t155 & MHb2        & Dusp5       & -3.69080250 & 0.5990911   &  3.5855677  &  0.11833803 & black      \\\\\n",
       "\t156 & MHb2        & Dusp6       &  0.62623725 & 0.0000000   & -1.7917595  &  0.04901783 & black      \\\\\n",
       "\t157 & MHb2        & Pcdh8       & -0.52535404 & 0.0000000   &  3.8051726  &  0.15753838 & red        \\\\\n",
       "\t158 & MHb2        & Cyr61       & -0.37878788 & 0.0000000   &  0.0000000  & -2.13979048 & black      \\\\\n",
       "\t... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t227 & MHb2        & Cxcl10      &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t228 & MHb2        & Gbp2        &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t229 & MHb2        & Clec4e      &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t230 & MHb2        & Mmp13       &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t231 & MHb2        & Il23a       & -0.3787879  & 0.00000000  &  0.0000000  & -1.11556185 & black      \\\\\n",
       "\t232 & MHb2        & Arhgef3     &  0.2512563  & 0.00000000  &  0.0000000  &  1.15110820 & black      \\\\\n",
       "\t233 & MHb2        & Serpine1    &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t234 & MHb2        & Traf1       & -0.1275316  & 0.00000000  & -1.7917595  &  0.21906415 & black      \\\\\n",
       "\t235 & MHb2        & Vcam1       & -0.7575758  & 0.04915679  &  0.0000000  & -1.03094962 & black      \\\\\n",
       "\t236 & MHb2        & Marcksl1    & -3.9725141  & 0.04947343  & -0.2373420  &  0.03743812 & black      \\\\\n",
       "\t237 & MHb2        & Nfkbid      & -0.6376580  & 0.00000000  &  1.0986123  &  0.19165546 & black      \\\\\n",
       "\t238 & MHb2        & Ikbke       & -0.3787879  & 0.00000000  &  0.0000000  & -1.09238463 & black      \\\\\n",
       "\t239 & MHb2        & Ccl12       &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t240 & MHb2        & Ifit3       & -0.3787879  & 0.00000000  &  0.0000000  & -0.71270519 & black      \\\\\n",
       "\t241 & MHb2        & Cebpb       & -6.2147861  & 3.47565898  &  1.4152217  &  0.08808224 & black      \\\\\n",
       "\t242 & MHb2        & Zfp36l1     & -0.8851074  & 0.00000000  & -1.7917595  & -0.07588597 & black      \\\\\n",
       "\t243 & MHb2        & Txnip       &  0.3749810  & 0.00000000  & -1.0986123  & -0.15334003 & black      \\\\\n",
       "\t244 & MHb2        & Nfib        & -4.2961017  & 0.00000000  &  3.6198873  &  0.06658002 & black      \\\\\n",
       "\t245 & MHb2        & Hes1        & -0.2550632  & 0.00000000  & -0.6931472  &  0.25236867 & black      \\\\\n",
       "\t246 & MHb2        & Pias1       &  3.8659205  & 0.73348945  &  8.5045981  &  0.19131350 & red        \\\\\n",
       "\t247 & MHb2        & Klf2        & -0.2550632  & 0.00000000  & -1.3862944  & -0.07876607 & black      \\\\\n",
       "\t248 & MHb2        & Dusp2       & -0.3787879  & 0.00000000  &  0.0000000  & -0.71270519 & black      \\\\\n",
       "\t249 & MHb2        & Wee1        &  0.2512563  & 0.00000000  &  0.0000000  &  1.31322083 & black      \\\\\n",
       "\t250 & MHb2        & Thbs1       & -0.1275316  & 0.00000000  & -1.7917595  & -0.10140492 & black      \\\\\n",
       "\t251 & MHb2        & Sik1        &  3.5842089  & 0.00000000  & 10.9877902  &  0.22567133 & red        \\\\\n",
       "\t252 & MHb2        & Gdf15       &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t253 & MHb2        & Ier5        & -1.9396224  & 0.00000000  &  2.2759443  &  0.07786422 & black      \\\\\n",
       "\t254 & MHb2        & Rgs1        &  0.0000000  & 0.00000000  &  0.0000000  &  0.00000000 & black      \\\\\n",
       "\t255 & MHb2        & Id2         & -0.1275316  & 0.00000000  & -1.7917595  &  0.49235530 & black      \\\\\n",
       "\t256 & MHb2        & Apold1      & -0.1275316  & 0.00000000  & -1.7917595  & -0.87187387 & black      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | type | gene | P_change | P_p | E_p | E_change | color |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 129 | MHb2        | Fos         |  2.49733516 | 0.0000000   |  5.1734978  |  0.34056693 | red         |\n",
       "| 130 | MHb2        | Fosb        | -0.38640171 | 0.0000000   | -0.3923930  | -0.14054065 | black       |\n",
       "| 131 | MHb2        | Fosl1       |  0.25125628 | 0.0000000   |  0.0000000  |  1.47563658 | black       |\n",
       "| 132 | MHb2        | Fosl2       | -0.25506319 | 0.0000000   | -1.3862944  |  0.22734248 | black       |\n",
       "| 133 | MHb2        | Jun         |  4.38366073 | 2.5460034   | -0.2483728  |  0.03645498 | black       |\n",
       "| 134 | MHb2        | Junb        |  1.75118014 | 0.0000000   | -1.4398750  | -0.05633340 | black       |\n",
       "| 135 | MHb2        | Jund        | -8.37330592 | 1.6554300   |  5.8722091  |  0.07432942 | black       |\n",
       "| 136 | MHb2        | Egr1        |  0.73473428 | 0.0000000   |  0.6279802  |  0.13035000 | blue        |\n",
       "| 137 | MHb2        | Egr2        |  0.00000000 | 0.0000000   |  0.0000000  |  0.00000000 | black       |\n",
       "| 138 | MHb2        | Egr4        |  0.25125628 | 0.0000000   |  0.0000000  |  1.30103111 | black       |\n",
       "| 139 | MHb2        | Nr4a1       | -0.01142074 | 0.0000000   |  0.9212983  |  0.17008440 | black       |\n",
       "| 140 | MHb2        | Nr4a2       |  1.27150906 | 0.0000000   |  0.7291733  |  0.06268799 | black       |\n",
       "| 141 | MHb2        | Nr4a3       |  1.99862951 | 0.0000000   | -0.6499995  | -0.12320910 | black       |\n",
       "| 142 | MHb2        | Arc         |  4.61968936 | 1.3064519   |  4.3905063  |  0.11803902 | black       |\n",
       "| 143 | MHb2        | Homer1      | -1.05070809 | 0.0000000   |  4.2643059  |  0.14556440 | black       |\n",
       "| 144 | MHb2        | Rheb        |  3.31582153 | 0.0000000   |  5.3797227  |  0.09615673 | black       |\n",
       "| 145 | MHb2        | Rgs2        |  2.29366530 | 0.0000000   |  8.0302289  |  0.16396755 | red         |\n",
       "| 146 | MHb2        | Plk2        | -2.73907416 | 0.0000000   |  7.9424006  |  0.15010880 | red         |\n",
       "| 147 | MHb2        | Ptgs2       |  0.00000000 | 0.0000000   |  0.0000000  |  0.00000000 | black       |\n",
       "| 148 | MHb2        | Bdnf        | -0.25506319 | 0.0000000   | -0.6931472  | -0.23507667 | black       |\n",
       "| 149 | MHb2        | Inhba       |  0.50251256 | 0.0000000   |  0.0000000  |  1.39488577 | black       |\n",
       "| 150 | MHb2        | Nptx2       |  1.94533272 | 0.0000000   |  5.9771775  |  0.09919529 | black       |\n",
       "| 151 | MHb2        | Plat        |  0.37498097 | 0.0000000   | -1.0986123  |  0.18716109 | black       |\n",
       "| 152 | MHb2        | Nrn1        | -3.86972743 | 0.9149738   |  0.7055547  |  0.06766979 | black       |\n",
       "| 153 | MHb2        | Myc         | -0.37878788 | 0.0000000   |  0.0000000  | -1.05989943 | black       |\n",
       "| 154 | MHb2        | Dusp1       |  0.49870565 | 0.0000000   | -0.1335314  |  0.19385054 | black       |\n",
       "| 155 | MHb2        | Dusp5       | -3.69080250 | 0.5990911   |  3.5855677  |  0.11833803 | black       |\n",
       "| 156 | MHb2        | Dusp6       |  0.62623725 | 0.0000000   | -1.7917595  |  0.04901783 | black       |\n",
       "| 157 | MHb2        | Pcdh8       | -0.52535404 | 0.0000000   |  3.8051726  |  0.15753838 | red         |\n",
       "| 158 | MHb2        | Cyr61       | -0.37878788 | 0.0000000   |  0.0000000  | -2.13979048 | black       |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| 227 | MHb2        | Cxcl10      |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 228 | MHb2        | Gbp2        |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 229 | MHb2        | Clec4e      |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 230 | MHb2        | Mmp13       |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 231 | MHb2        | Il23a       | -0.3787879  | 0.00000000  |  0.0000000  | -1.11556185 | black       |\n",
       "| 232 | MHb2        | Arhgef3     |  0.2512563  | 0.00000000  |  0.0000000  |  1.15110820 | black       |\n",
       "| 233 | MHb2        | Serpine1    |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 234 | MHb2        | Traf1       | -0.1275316  | 0.00000000  | -1.7917595  |  0.21906415 | black       |\n",
       "| 235 | MHb2        | Vcam1       | -0.7575758  | 0.04915679  |  0.0000000  | -1.03094962 | black       |\n",
       "| 236 | MHb2        | Marcksl1    | -3.9725141  | 0.04947343  | -0.2373420  |  0.03743812 | black       |\n",
       "| 237 | MHb2        | Nfkbid      | -0.6376580  | 0.00000000  |  1.0986123  |  0.19165546 | black       |\n",
       "| 238 | MHb2        | Ikbke       | -0.3787879  | 0.00000000  |  0.0000000  | -1.09238463 | black       |\n",
       "| 239 | MHb2        | Ccl12       |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 240 | MHb2        | Ifit3       | -0.3787879  | 0.00000000  |  0.0000000  | -0.71270519 | black       |\n",
       "| 241 | MHb2        | Cebpb       | -6.2147861  | 3.47565898  |  1.4152217  |  0.08808224 | black       |\n",
       "| 242 | MHb2        | Zfp36l1     | -0.8851074  | 0.00000000  | -1.7917595  | -0.07588597 | black       |\n",
       "| 243 | MHb2        | Txnip       |  0.3749810  | 0.00000000  | -1.0986123  | -0.15334003 | black       |\n",
       "| 244 | MHb2        | Nfib        | -4.2961017  | 0.00000000  |  3.6198873  |  0.06658002 | black       |\n",
       "| 245 | MHb2        | Hes1        | -0.2550632  | 0.00000000  | -0.6931472  |  0.25236867 | black       |\n",
       "| 246 | MHb2        | Pias1       |  3.8659205  | 0.73348945  |  8.5045981  |  0.19131350 | red         |\n",
       "| 247 | MHb2        | Klf2        | -0.2550632  | 0.00000000  | -1.3862944  | -0.07876607 | black       |\n",
       "| 248 | MHb2        | Dusp2       | -0.3787879  | 0.00000000  |  0.0000000  | -0.71270519 | black       |\n",
       "| 249 | MHb2        | Wee1        |  0.2512563  | 0.00000000  |  0.0000000  |  1.31322083 | black       |\n",
       "| 250 | MHb2        | Thbs1       | -0.1275316  | 0.00000000  | -1.7917595  | -0.10140492 | black       |\n",
       "| 251 | MHb2        | Sik1        |  3.5842089  | 0.00000000  | 10.9877902  |  0.22567133 | red         |\n",
       "| 252 | MHb2        | Gdf15       |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 253 | MHb2        | Ier5        | -1.9396224  | 0.00000000  |  2.2759443  |  0.07786422 | black       |\n",
       "| 254 | MHb2        | Rgs1        |  0.0000000  | 0.00000000  |  0.0000000  |  0.00000000 | black       |\n",
       "| 255 | MHb2        | Id2         | -0.1275316  | 0.00000000  | -1.7917595  |  0.49235530 | black       |\n",
       "| 256 | MHb2        | Apold1      | -0.1275316  | 0.00000000  | -1.7917595  | -0.87187387 | black       |\n",
       "\n"
      ],
      "text/plain": [
       "    type gene     P_change    P_p        E_p        E_change    color\n",
       "129 MHb2 Fos       2.49733516 0.0000000   5.1734978  0.34056693 red  \n",
       "130 MHb2 Fosb     -0.38640171 0.0000000  -0.3923930 -0.14054065 black\n",
       "131 MHb2 Fosl1     0.25125628 0.0000000   0.0000000  1.47563658 black\n",
       "132 MHb2 Fosl2    -0.25506319 0.0000000  -1.3862944  0.22734248 black\n",
       "133 MHb2 Jun       4.38366073 2.5460034  -0.2483728  0.03645498 black\n",
       "134 MHb2 Junb      1.75118014 0.0000000  -1.4398750 -0.05633340 black\n",
       "135 MHb2 Jund     -8.37330592 1.6554300   5.8722091  0.07432942 black\n",
       "136 MHb2 Egr1      0.73473428 0.0000000   0.6279802  0.13035000 blue \n",
       "137 MHb2 Egr2      0.00000000 0.0000000   0.0000000  0.00000000 black\n",
       "138 MHb2 Egr4      0.25125628 0.0000000   0.0000000  1.30103111 black\n",
       "139 MHb2 Nr4a1    -0.01142074 0.0000000   0.9212983  0.17008440 black\n",
       "140 MHb2 Nr4a2     1.27150906 0.0000000   0.7291733  0.06268799 black\n",
       "141 MHb2 Nr4a3     1.99862951 0.0000000  -0.6499995 -0.12320910 black\n",
       "142 MHb2 Arc       4.61968936 1.3064519   4.3905063  0.11803902 black\n",
       "143 MHb2 Homer1   -1.05070809 0.0000000   4.2643059  0.14556440 black\n",
       "144 MHb2 Rheb      3.31582153 0.0000000   5.3797227  0.09615673 black\n",
       "145 MHb2 Rgs2      2.29366530 0.0000000   8.0302289  0.16396755 red  \n",
       "146 MHb2 Plk2     -2.73907416 0.0000000   7.9424006  0.15010880 red  \n",
       "147 MHb2 Ptgs2     0.00000000 0.0000000   0.0000000  0.00000000 black\n",
       "148 MHb2 Bdnf     -0.25506319 0.0000000  -0.6931472 -0.23507667 black\n",
       "149 MHb2 Inhba     0.50251256 0.0000000   0.0000000  1.39488577 black\n",
       "150 MHb2 Nptx2     1.94533272 0.0000000   5.9771775  0.09919529 black\n",
       "151 MHb2 Plat      0.37498097 0.0000000  -1.0986123  0.18716109 black\n",
       "152 MHb2 Nrn1     -3.86972743 0.9149738   0.7055547  0.06766979 black\n",
       "153 MHb2 Myc      -0.37878788 0.0000000   0.0000000 -1.05989943 black\n",
       "154 MHb2 Dusp1     0.49870565 0.0000000  -0.1335314  0.19385054 black\n",
       "155 MHb2 Dusp5    -3.69080250 0.5990911   3.5855677  0.11833803 black\n",
       "156 MHb2 Dusp6     0.62623725 0.0000000  -1.7917595  0.04901783 black\n",
       "157 MHb2 Pcdh8    -0.52535404 0.0000000   3.8051726  0.15753838 red  \n",
       "158 MHb2 Cyr61    -0.37878788 0.0000000   0.0000000 -2.13979048 black\n",
       "... ...  ...      ...         ...        ...        ...         ...  \n",
       "227 MHb2 Cxcl10    0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "228 MHb2 Gbp2      0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "229 MHb2 Clec4e    0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "230 MHb2 Mmp13     0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "231 MHb2 Il23a    -0.3787879  0.00000000  0.0000000 -1.11556185 black\n",
       "232 MHb2 Arhgef3   0.2512563  0.00000000  0.0000000  1.15110820 black\n",
       "233 MHb2 Serpine1  0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "234 MHb2 Traf1    -0.1275316  0.00000000 -1.7917595  0.21906415 black\n",
       "235 MHb2 Vcam1    -0.7575758  0.04915679  0.0000000 -1.03094962 black\n",
       "236 MHb2 Marcksl1 -3.9725141  0.04947343 -0.2373420  0.03743812 black\n",
       "237 MHb2 Nfkbid   -0.6376580  0.00000000  1.0986123  0.19165546 black\n",
       "238 MHb2 Ikbke    -0.3787879  0.00000000  0.0000000 -1.09238463 black\n",
       "239 MHb2 Ccl12     0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "240 MHb2 Ifit3    -0.3787879  0.00000000  0.0000000 -0.71270519 black\n",
       "241 MHb2 Cebpb    -6.2147861  3.47565898  1.4152217  0.08808224 black\n",
       "242 MHb2 Zfp36l1  -0.8851074  0.00000000 -1.7917595 -0.07588597 black\n",
       "243 MHb2 Txnip     0.3749810  0.00000000 -1.0986123 -0.15334003 black\n",
       "244 MHb2 Nfib     -4.2961017  0.00000000  3.6198873  0.06658002 black\n",
       "245 MHb2 Hes1     -0.2550632  0.00000000 -0.6931472  0.25236867 black\n",
       "246 MHb2 Pias1     3.8659205  0.73348945  8.5045981  0.19131350 red  \n",
       "247 MHb2 Klf2     -0.2550632  0.00000000 -1.3862944 -0.07876607 black\n",
       "248 MHb2 Dusp2    -0.3787879  0.00000000  0.0000000 -0.71270519 black\n",
       "249 MHb2 Wee1      0.2512563  0.00000000  0.0000000  1.31322083 black\n",
       "250 MHb2 Thbs1    -0.1275316  0.00000000 -1.7917595 -0.10140492 black\n",
       "251 MHb2 Sik1      3.5842089  0.00000000 10.9877902  0.22567133 red  \n",
       "252 MHb2 Gdf15     0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "253 MHb2 Ier5     -1.9396224  0.00000000  2.2759443  0.07786422 black\n",
       "254 MHb2 Rgs1      0.0000000  0.00000000  0.0000000  0.00000000 black\n",
       "255 MHb2 Id2      -0.1275316  0.00000000 -1.7917595  0.49235530 black\n",
       "256 MHb2 Apold1   -0.1275316  0.00000000 -1.7917595 -0.87187387 black"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MHb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 15 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nN7ACcIxGAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#MHb2, expression, repeat for others\n",
    "color<- vector(mode=\"numeric\", length=0)\n",
    "MHb<- subset(Cell_number,type==\"MHb2\")\n",
    "for(i in 1:dim(MHb)[1]){\n",
    "    \n",
    "    if(MHb$gene[i]==\"Fos\"){color[i]<-\"yellow\"}\n",
    "\n",
    "    else if(MHb$gene[i]==\"Egr1\"){color[i]<-\"blue\"}      \n",
    "     else if (MHb$E_p[i] > -log(0.05) & MHb$E_change[i]>0.15 ){color[i]<- \"red\"}\n",
    "    else{color[i]<-\"black\"}\n",
    "}\n",
    "MHb$color<-color\n",
    "\n",
    "ggplot(MHb,aes(x=E_change,y=E_p,color=color))+geom_point(size=4)+ylab(\"- log p-vlaue\")+xlab(\"change (%)\")+scale_color_manual(values=c(\"black\",\"blue\",\"red\",\"yellow\"))+theme(axis.text.x=element_text(size=10,angle = 50, hjust = 1))+ggtitle(\"IEG proportion MHb2\")+\n",
    "theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=12),axis.text.x=element_text(size=15,angle = 50, hjust = 1),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+xlim(-0.5,1)+ylim(0,18)+geom_hline(yintercept=-log(0.05), linetype=\"dashed\", color = \"black\")+ geom_vline(xintercept =c(-0.15,0.15), linetype=\"dashed\",color = \"black\")\n",
    "#ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/MHb2_Expression_level_supple6_0.15_testaxis.pdf\",width=10,height=10) # may use +scale_fill_manual(values=c(\"grey\", \"black\"))\n",
    "#write.xlsx2(MHb,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/MHb2_Expression_level_supple6.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LHb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "IEG<-read.xlsx(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/IEG.xlsx\",sheetIndex=1)\n",
    "IEG<-as.character(IEG[[1]])\n",
    "IEG<-intersect(IEG,rownames(LHb.integrated@assays$RNA@data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(temp_c, temp_s, alternative = \"two.sided\"):\n",
      "“cannot compute exact p-value with ties”"
     ]
    }
   ],
   "source": [
    "#create df, p value is -log\n",
    "Cell_type<-c(\"LHb1\",\"LHb2\",\"LHb3\",\"LHb4\",\"LHb5\",\"LHb6\")\n",
    "# make a data frame for cell number (this data frame is a simple version giving single value for each cluster) \n",
    "Cell_number<- data.frame(\"type\" =c(rep(\"LHb1\",length(IEG)),rep(\"LHb2\",length(IEG)),rep(\"LHb3\",length(IEG)),rep(\"LHb4\",length(IEG)),rep(\"LHb5\",length(IEG)),rep(\"LHb6\",length(IEG))), \"gene\" = rep(IEG,6))\n",
    "\n",
    "\n",
    "E_p<-vector(mode=\"numeric\", length=0) # expression level p value\n",
    "E_change<-vector(mode=\"numeric\", length=0) # Expression level change\n",
    "\n",
    "    \n",
    "for (i in 1:dim(Cell_number)[1]){\n",
    "    c_sum<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)\n",
    "    s_sum<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)\n",
    "    temp_c<-LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))][LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0]\n",
    "    temp_s<-LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))][LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0]\n",
    "   if(c_sum>0 & s_sum>0){\n",
    "        res <-wilcox.test(temp_c, temp_s, alternative = \"two.sided\")\n",
    "        if(length(Cell_type)*res$p.value>1){E_p[i]<- -log(1)}\n",
    "        else{E_p[i]<- -log(length(Cell_type)*res$p.value)}\n",
    "    }else if(c_sum==0 | s_sum==0){E_p[i]<- -log(1)}\n",
    "}\n",
    "Cell_number$E_p<-E_p\n",
    "for (i in 1:dim(Cell_number)[1]){if(sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)==0 & sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)==0)\n",
    "{E_change[i]<-0}else if(sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)==0){E_change[i]<-0-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)}\n",
    "   else if(sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)==0){E_change[i]<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)}\n",
    "    else{E_change[i]<-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_stim\",sep=\"\")))]>0)-sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))])/sum(LHb.integrated@assays$RNA@data[Cell_number$gene[i],eval(parse(text = paste(Cell_number$type[i],\"_barcode_cntl\",sep=\"\")))]>0)}}\n",
    "Cell_number$E_change<-E_change\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "LHb<-subset(Cell_number,type==\"LHb1\"|type==\"LHb2\"|type==\"LHb3\"|type==\"LHb4\"|type==\"LHb5\"|type==\"LHb6\")\n",
    "LHb$type<-factor(LHb$type,levels=rev(c( \"LHb1\",\"LHb2\",\"LHb3\",\"LHb4\",\"LHb5\",\"LHb6\")))\n",
    "E_change_p<-numeric()\n",
    "for(i in 1:dim(LHb)[1]){\n",
    "    if(LHb$E_p[i]> -log(0.05)){E_change_p[i]<-LHb$E_change[i]}else{E_change_p[i]<- 0}\n",
    "}\n",
    "LHb$E_change_p<-E_change_p\n",
    "LHb<-subset(LHb,gene==\"Arc\"|gene==\"Fosb\"|gene==\"Fos\"|gene==\"Junb\"|gene==\"Jund\"|gene==\"Egr1\"|gene==\"Homer1\"|gene==\"Nr4a2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <-ggplot(LHb, aes(gene,type, fill= E_change_p)) + \n",
    " \n",
    "  geom_tile() + scale_fill_gradient(limits=c(0.1, 0.6), breaks=seq(0.1,0.6,by=0.2),low = \"white\",high = \"red\", oob=squish)\n",
    "pp<-p+ylab(\"Cell type\")+xlab(\"IEGs\")+theme(axis.title=element_text(size=20,face=\"bold\"),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\", size = 0.1),axis.ticks.y = element_blank())+ggtitle(\"IEGs\")\n",
    "pp\n",
    "#ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/IEG heatmap LHb2.pdf\",width=10,height=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 11 rows containing missing values (geom_point).”Warning message:\n",
      "“Removed 11 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LHb1, expression\n",
    "color<- vector(mode=\"numeric\", length=0)\n",
    "MHb<- subset(Cell_number,type==\"LHb1\")\n",
    "for(i in 1:dim(MHb)[1]){\n",
    "    \n",
    "    if(MHb$gene[i]==\"Fos\"){color[i]<-\"yellow\"}\n",
    "\n",
    "    else if(MHb$gene[i]==\"Egr1\"){color[i]<-\"blue\"}      \n",
    "     else if (MHb$E_p[i] > -log(0.05) & MHb$E_change[i]>0.15 ){color[i]<- \"red\"}\n",
    "    else{color[i]<-\"black\"}\n",
    "}\n",
    "MHb$color<-color\n",
    "\n",
    "ggplot(MHb,aes(x=E_change,y=E_p,color=color))+geom_point(size=4)+ylab(\"- log p-vlaue\")+xlab(\"change (%)\")+scale_color_manual(values=c(\"black\",\"blue\",\"red\",\"yellow\"))+theme(axis.text.x=element_text(size=10,angle = 50, hjust = 1))+ggtitle(\"IEG proportion LHb1\")+\n",
    "theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=12),axis.text.x=element_text(size=15,angle = 50, hjust = 1),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+xlim(-0.5,1)+ylim(0,11)+ geom_hline(yintercept=-log(0.05), linetype=\"dashed\", color = \"black\")+ geom_vline(xintercept =c(-0.15,0.15), linetype=\"dashed\",color = \"black\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/LHb1_Expression_level_supple60.15_testaxis.pdf\",width=10,height=10) # may use +scale_fill_manual(values=c(\"grey\", \"black\"))\n",
    "write.xlsx2(MHb,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/LHb1_Expression_level_supple6.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 30 rows containing missing values (geom_point).”Warning message:\n",
      "“Removed 30 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LHb2, expression, re[peat for others\n",
    "color<- vector(mode=\"numeric\", length=0)\n",
    "MHb<- subset(Cell_number,type==\"LHb2\")\n",
    "for(i in 1:dim(MHb)[1]){\n",
    "    \n",
    "    if(MHb$gene[i]==\"Fos\"){color[i]<-\"yellow\"}\n",
    "    else if(MHb$gene[i]==\"Egr1\"){color[i]<-\"blue\"}      \n",
    "     else if (MHb$E_p[i] > -log(0.05) & MHb$E_change[i]>0.15 ){color[i]<- \"red\"}\n",
    "    else{color[i]<-\"black\"}\n",
    "}\n",
    "MHb$color<-color\n",
    "\n",
    "ggplot(MHb,aes(x=E_change,y=E_p,color=color))+geom_point(size=4)+ylab(\"- log p-vlaue\")+xlab(\"change (%)\")+scale_color_manual(values=c(\"black\",\"blue\",\"red\",\"yellow\"))+theme(axis.text.x=element_text(size=10,angle = 50, hjust = 1))+ggtitle(\"IEG proportion LHb2\")+\n",
    "theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=12),axis.text.x=element_text(size=15,angle = 50, hjust = 1),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+xlim(-0.5,1)+ylim(0,11)+ geom_hline(yintercept=-log(0.05), linetype=\"dashed\", color = \"black\")+ geom_vline(xintercept =c(-0.15,0.15), linetype=\"dashed\",color = \"black\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/LHb2_Expression_level_supple60.15_test_axis.pdf\",width=10,height=10) # may use +scale_fill_manual(values=c(\"grey\", \"black\"))\n",
    "write.xlsx2(MHb,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/LHb2_Expression_level_supple6.xlsx\")"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}