VanTineEtAl_Fig5_Catplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## To make catplots in Figure 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "from matplotlib import colors\n",
    "import os\n",
    "from numpy import ma\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from glob import glob\n",
    "import glob\n",
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set working directory\n",
    "\n",
    "os.chdir('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/vanTine_001_FinalDataWithControls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load this data file from the GitHub page\n",
    "\n",
    "dfSarcoma = pd.read_excel('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/vanTine_001_FinalDataWithControls/MQ/vanTine_001_90_samples/Protein/Model/Protein_pVals_Final_WithMetaData.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter and reshape the df\n",
    "\n",
    "df = dfSarcoma\n",
    "\n",
    "df = df.loc[:,~df.columns.str.contains('CON_')] # remove contaminants\n",
    "df = df.query(\"dataType == 'NormalizedIntensity_Log2'\")\n",
    "df = df.query(\"timing != 'Control'\")\n",
    "\n",
    "totalCols = df.columns\n",
    "numCols = df._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "df = pd.melt(df,\n",
    "    id_vars = catCols,\n",
    "    value_vars = numCols,\n",
    "    var_name = 'protAcc',\n",
    "    value_name=\"area\",\n",
    "    ignore_index=False,\n",
    "    )\n",
    "\n",
    "# sort, to make plots order correctly\n",
    "df.sort_values(by=['timing'], inplace=True, ascending = False)\n",
    "\n",
    "#remove rows without areas & duplicates\n",
    "df.dropna(subset=['area'], inplace=True)\n",
    "df.drop_duplicates(inplace = True)\n",
    "\n",
    "# add a column specifying the dataset\n",
    "dataset = 'sarcoma'\n",
    "df.insert(loc=1, column='dataset', value=dataset)\n",
    "\n",
    "dfProtein = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "# make dfUniprot for annotation, this excel file is on the associated GitHub page\n",
    "\n",
    "dfUniprot = pd.read_excel('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/Figure2_Data_CancerVsNormal/uniprot-download_true_fields_accession_2Creviewed_2Cid_2Cprotein_nam-2023.03.17-19.33.16.44.xlsx')\n",
    "dfUniprot = dfUniprot.rename(columns={'Entry' : 'protAcc'})\n",
    "dfUniprot = dfUniprot.rename(columns={'Gene Names (primary)' : 'geneNamePrimary'})\n",
    "dfUniprot = dfUniprot.drop(columns=['Length','Entry Name','Organism','Gene Names'])\n",
    "dfUniprot['protAcc'] = dfUniprot['protAcc'].astype(str)\n",
    "dfUniprot.drop_duplicates(subset=['protAcc'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# annotate uniprot info\n",
    "dfMerged = dfProtein.merge(dfUniprot, on='protAcc', how='left')\n",
    "\n",
    "totalCols = dfMerged.columns\n",
    "numCols = dfMerged._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "dfMerged['area'] = dfMerged['area'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13e420790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1266.12x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "\n",
    "df = dfMerged\n",
    "\n",
    "tgtList = ['FBLN1', 'SPP2', 'PHKA2', 'DEGS1']\n",
    "            \n",
    "mask = df['geneNamePrimary'].isin(tgtList) #select target genes\n",
    "\n",
    "# connected pre-post plot\n",
    "sns.set(rc={\"lines.linewidth\": 0.4})\n",
    "sns.set_style(\"white\")\n",
    "g = sns.FacetGrid(df.loc[mask], col=\"geneNamePrimary\", sharey=False)\n",
    "\n",
    "g.map_dataframe(sns.stripplot, x = 'timing', y ='area', hue = 'sensitivity', jitter = True, palette=['blue', 'red'])\n",
    "g.map_dataframe(sns.pointplot, x ='timing', y ='area', hue = 'sensitivity', dodge = True, capsize=.05, palette=['blue', 'red'])\n",
    "g.set_axis_labels('timing', \"Area\")\n",
    "g.add_legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}