plot-polymerase-activity-assay-preliminary-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot PB2 polymerase activity assay preliminary data \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary modules\n",
    "import sys, subprocess, glob, os, shutil, re, importlib, Bio, csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rpy2\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lmoncla/anaconda/envs/LHM-basics/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# read in data as dataframe \n",
    "df = pd.DataFrame.from_csv(\"/Users/lmoncla/Documents/Fellowships/K99/resubmission/polymerase-activity-assay-data/flow-cytometry-results/raw-data.txt\",sep=\"\\t\")\n",
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>293T|Count</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Median</th>\n",
       "      <th>Geometric Mean</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Proportion_positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BioT</td>\n",
       "      <td>7850</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GFP</td>\n",
       "      <td>7781</td>\n",
       "      <td>52.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.013</td>\n",
       "      <td>47.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No trans control</td>\n",
       "      <td>8253</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>627K</td>\n",
       "      <td>6671</td>\n",
       "      <td>0.900</td>\n",
       "      <td>54.800</td>\n",
       "      <td>10.100</td>\n",
       "      <td>34.2</td>\n",
       "      <td>11854.0</td>\n",
       "      <td>10121.0</td>\n",
       "      <td>17865.0</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>627E</td>\n",
       "      <td>6831</td>\n",
       "      <td>0.015</td>\n",
       "      <td>21.200</td>\n",
       "      <td>42.200</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>3372.0</td>\n",
       "      <td>0.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>8184</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60.600</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sample  293T|Count      Q1      Q2      Q3     Q4   Median  \\\n",
       "0              BioT        7850   0.013   0.013   0.000  100.0    332.0   \n",
       "1               GFP        7781  52.400   0.000   0.013   47.6      NaN   \n",
       "2  No trans control        8253   0.000   0.000   0.012  100.0      NaN   \n",
       "3              627K        6671   0.900  54.800  10.100   34.2  11854.0   \n",
       "4              627E        6831   0.015  21.200  42.200   36.5   1964.0   \n",
       "5           mCherry        8184   0.000   0.000  60.600   39.4      NaN   \n",
       "\n",
       "   Geometric Mean     Mean  Proportion_positive  \n",
       "0           332.0    332.0                0.000  \n",
       "1             NaN      NaN                0.000  \n",
       "2             NaN      NaN                0.000  \n",
       "3         10121.0  17865.0                0.844  \n",
       "4          1991.0   3372.0                0.334  \n",
       "5             NaN      NaN                0.000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure_name = \"percent-positive.pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TableGrob (1 x 2) \"arrange\": 2 grobs\n",
       "  z     cells    name           grob\n",
       "1 1 (1-1,1-1) arrange gtable[layout]\n",
       "2 2 (1-1,2-2) arrange gtable[layout]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -h 300 -u px -i df,figure_name  # this sets the size of the plot...otherwise, it will go off the page\n",
    "require(ggplot2)\n",
    "library(ggplot2)\n",
    "require(grid)\n",
    "library(grid)\n",
    "require(gridExtra)\n",
    "\n",
    "df$Sample = gsub(\"No trans control\",\"no trans\\ncontrol\", df$Sample)\n",
    "df$Sample = factor(df$Sample, levels=c(\"627E\",\"627K\", \"GFP\", \"mCherry\",\"BioT\",\"no trans\\ncontrol\"))\n",
    "\n",
    "p1 <- ggplot(data=df, aes(x=Sample, y=Proportion_positive * 100, fill=Sample)) + \n",
    "    geom_col() +\n",
    "#     scale_x_continuous(limits=c(2008, 2018), breaks=c(2008,2010,2012,2014,2016,2018))+ \n",
    "#     scale_y_continuous(limits=c(0,0.3))+\n",
    "    scale_fill_manual(values=c(\"627K\" = \"#E89129\", \"627E\" = \"#2876B4\", \"GFP\"=\"#8B8B8C\", \"mCherry\"=\"#8B8B8C\",\"BioT\"=\"#8B8B8C\",\"no trans\\ncontrol\"=\"#8B8B8C\"), guide=FALSE)+\n",
    "    labs(x=\"\",y=\"% positive\")+\n",
    "    theme(plot.title = element_text(size=20, hjust=0.5))+\n",
    "    theme(panel.grid.major.y=element_line(colour=NA))+\n",
    "    theme(panel.grid.minor=element_line(colour=NA,size=NA))+    \n",
    "    theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
    "    theme(axis.line.x=element_line(colour=\"black\"))+\n",
    "    theme(axis.line.y=element_line(colour=\"black\"))+\n",
    "    theme(strip.text.x=element_text(size=116))+\n",
    "    theme(axis.title.y=element_text(size=16, vjust=4))+\n",
    "    theme(axis.title.x=element_text(size=16, vjust=-3))+\n",
    "    theme(axis.text=element_text(size=14, colour=\"black\"))+\n",
    "    #theme(axis.text.x=element_text(size=12))+\n",
    "    theme(legend.text=element_text(size=16))+\n",
    "    theme(legend.title=element_text(size=16, face=\"plain\"))+\n",
    "    theme(panel.margin=unit(1, \"lines\"))+theme(plot.margin=unit(c(0.1,0.1,0.01,1),\"cm\"))+\n",
    "    theme(legend.key.size=unit(0.7, \"cm\"))+\n",
    "    theme(panel.background=element_rect(fill=NA))+\n",
    "    theme(legend.key=element_rect(fill=NA))\n",
    "\n",
    "\n",
    "p2 <- ggplot(data=df, aes(x=Sample, y=Median, fill=Sample)) + \n",
    "    geom_col() +\n",
    "    scale_y_continuous(limits=c(0,12000), breaks=seq(0,12000,4000))+\n",
    "    scale_fill_manual(values=c(\"627K\" = \"#E89129\", \"627E\" = \"#2876B4\", \"GFP\"=\"#8B8B8C\", \"mCherry\"=\"#8B8B8C\",\"BioT\"=\"#8B8B8C\",\"no trans\\ncontrol\"=\"#8B8B8C\"), guide=FALSE)+\n",
    "    labs(x=\"\",y=\"intensity\")+\n",
    "    theme(plot.title = element_text(size=20, hjust=0.5))+\n",
    "    theme(panel.grid.major.y=element_line(colour=NA))+\n",
    "    theme(panel.grid.minor=element_line(colour=NA,size=NA))+    \n",
    "    theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
    "    theme(axis.line.x=element_line(colour=\"black\"))+\n",
    "    theme(axis.line.y=element_line(colour=\"black\"))+\n",
    "    theme(strip.text.x=element_text(size=116))+\n",
    "    theme(axis.title.y=element_text(size=16, vjust=4))+\n",
    "    theme(axis.title.x=element_text(size=16, vjust=-3))+\n",
    "    theme(axis.text=element_text(size=14, colour=\"black\"))+\n",
    "    #theme(axis.text.x=element_text(size=12))+\n",
    "    theme(legend.text=element_text(size=16))+\n",
    "    theme(legend.title=element_text(size=16, face=\"plain\"))+\n",
    "    theme(panel.margin=unit(1, \"lines\"))+\n",
    "    theme(plot.margin=unit(c(0.1,0.1,0.1,1),\"cm\"))+\n",
    "    theme(legend.key.size=unit(0.7, \"cm\"))+\n",
    "    theme(panel.background=element_rect(fill=NA))+\n",
    "    theme(legend.key=element_rect(fill=NA))\n",
    "\n",
    "\n",
    "p <- grid.arrange(p1, p2, ncol=2)\n",
    "ggsave(figure_name, p, width = 10, height = 2.5, path=\"/Users/lmoncla/Documents/Fellowships/K99/resubmission/figures/\")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LHM-basics (python3)",
   "language": "python",
   "name": "lhm-basics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}