Ecomics COLOMBOS - Osmotic Stress.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Knowledge captured by COLOMBOS and Ecomics\n",
    "\n",
    "Based on an approach suggested by Ecomics authors, the following analysis wants to evaluate dataset quality looking for known genes associated with specific stresses starting from fold-changes.\n",
    "\n",
    "The third stress used is **osmotic stress**. Known genes have been collected from RegulonDB (https://www.ncbi.nlm.nih.gov/pubmed/26527724).\n",
    "\n",
    "  1. Find all conditions (i.e. unique combinations of strain, medium, stresses exposed, and genetic perturbations) exposed under the stress\n",
    "  2. Find the corresponding wild-type conditions (i.e. all condition features of strain, medium, genetic perturbations are identical except for no stress exposed).\n",
    "  3. Measure fold-changes of all genes for each pair (stress condition and its wild-type). \n",
    "  4. Rank genes by mean absolute fold-change.\n",
    "  5. Compare how many known genes are more present in top genes between the two datasets.\n",
    "  \n",
    "In COLOMBOS there's no need to calculate fold-changes since values are already ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read COLOMBOS contrast data\n",
    "colombos_ctr_data = pd.read_table('colombos_ecoli_exprdata_20151029.txt', sep='\\t', nrows=6)\n",
    "colombos_ctr_data = colombos_ctr_data[colombos_ctr_data.columns[2:]]\n",
    "colombos_ctr_data = colombos_ctr_data.set_index(colombos_ctr_data.columns[0])\n",
    "colombos_ctr_data.index = list(colombos_ctr_data.index[:-1]) + ['Contrast ID']\n",
    "\n",
    "### Read COLOMBOS gene data\n",
    "colombos_gene_data = pd.read_table('colombos_ecoli_exprdata_20151029.txt', sep='\\t', skiprows=6)\n",
    "colombos_gene_data = colombos_gene_data[colombos_gene_data.columns[:3]]\n",
    "colombos_gene_data.columns = list(colombos_gene_data.columns[:2]) + ['Gene ID']\n",
    "colombos_gene_data = colombos_gene_data.set_index('Gene ID')\n",
    "colombos_gene_data['LocusTag'] = ['m.' + lc for lc in colombos_gene_data['LocusTag'].tolist()]\n",
    "\n",
    "### Read COLOMBOS expression data\n",
    "colombos_exp_data = pd.read_table('colombos_ecoli_exprdata_20151029.txt', sep='\\t', skiprows=6)\n",
    "colombos_exp_data = colombos_exp_data[colombos_exp_data.columns[2:]]\n",
    "colombos_exp_data = colombos_exp_data.rename(columns = {'Geneid/Contrast_id':'Gene ID'})\n",
    "colombos_exp_data = colombos_exp_data.set_index('Gene ID')\n",
    "\n",
    "### Read COLOMBOS test sample annotation data\n",
    "colombos_test_annot = pd.read_table('colombos_ecoli_testannot_20151029.txt', sep='\\t')\n",
    "ctr_name = colombos_test_annot['ContrastName'].tolist()\n",
    "colombos_test_annot = pd.DataFrame(colombos_test_annot['TestAnnotation'].str.split(':').tolist(), \n",
    "             columns= ['TestAnnot', 'Value', 'None'])\n",
    "del colombos_test_annot['None']\n",
    "colombos_test_annot.insert(0, 'ContrastName', ctr_name)\n",
    "colombos_test_annot = colombos_test_annot.set_index('ContrastName')\n",
    "\n",
    "### Read COLOMBOS reference sample annotation data\n",
    "colombos_ref_annot = pd.read_table('colombos_ecoli_refannot_20151029.txt', sep='\\t')\n",
    "ctr_name = colombos_ref_annot['ContrastName'].tolist()\n",
    "colombos_ref_annot = pd.DataFrame(colombos_ref_annot['RefAnnotation'].str.split(':').tolist(), \n",
    "             columns= ['RefAnnot', 'Value', 'None'])\n",
    "del colombos_ref_annot['None']\n",
    "colombos_ref_annot.insert(0, 'ContrastName', ctr_name)\n",
    "colombos_ref_annot = colombos_ref_annot.set_index('ContrastName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read Ecomics annotation data\n",
    "ecomics_info = pd.read_csv('ncomms13090-s2_trans.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read Ecomics expression data\n",
    "ecomics_v8 = pd.read_csv('ecomics.transcriptome.no_avg.v8.txt', sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "osmotic_genes = ['m.b0014','m.b0161','m.b0313','m.b0314','m.b0435','m.b0440','m.b0463','m.b0464','m.b0812',\n",
    "                 'm.b0875','m.b0929','m.b0972','m.b0978','m.b0979','m.b1037','m.b1038','m.b1039','m.b1040',\n",
    "                 'm.b1041','m.b1042','m.b1048','m.b1049','m.b1197','m.b1283','m.b1323','m.b1482','m.b1739',\n",
    "                 'm.b1896','m.b2215','m.b2344','m.b2659','m.b2669','m.b2677','m.b2679','m.b2741','m.b3461',\n",
    "                 'm.b3556','m.b3687','m.b3700','m.b3701','m.b4000','m.b4032','m.b4033','m.b4034','m.b4035',\n",
    "                 'm.b4036','m.b4037','m.b4111','m.b4239','m.b4240','m.b4376','m.b4484']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Overlapping samples\n",
    "ecomics_unique_samples = set(ecomics_info['Sample ID'])\n",
    "colombos_unique_samples = set([y[:-4] for x in colombos_ctr_data.columns for y in x.split('-vs-') ])\n",
    "overlapping_samples = set.intersection(ecomics_unique_samples, colombos_unique_samples)\n",
    "colombos_overlapping_contrasts = [x for x in colombos_ctr_data.columns if '-vs-' in x and (x.split('-vs-')[0][:-4] in overlapping_samples or x.split('-vs-')[1][:-4] in overlapping_samples)]\n",
    "ecomics_overlapping_samples = list(overlapping_samples)\n",
    "\n",
    "ecomics_info = ecomics_info[ecomics_info['Sample ID'].isin(ecomics_overlapping_samples)]\n",
    "ecomics_v8 = ecomics_v8.loc[ecomics_info.index]\n",
    "\n",
    "colombos_ctr_data = colombos_ctr_data[colombos_overlapping_contrasts]\n",
    "colombos_exp_data = colombos_exp_data[colombos_ctr_data.loc['Contrast ID'].values]\n",
    "colombos_test_annot = colombos_test_annot.loc[colombos_ctr_data.columns]\n",
    "colombos_ref_annot = colombos_ref_annot.loc[colombos_ctr_data.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ecomics osmotic genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ea_idx = ecomics_v8.copy(deep=True)\n",
    "ea_idx.insert(0, 'Strain', ecomics_info['Strain Name'])\n",
    "ea_idx.insert(0, 'MediumID', ecomics_info['MD ID'])\n",
    "ea_idx.insert(0, 'Medium', ecomics_info['Base Medium'])\n",
    "ea_idx.insert(0, 'GP', ecomics_info['GP Type'])\n",
    "ea_idx.insert(0, 'Stress', ecomics_info['Stress'])\n",
    "del ea_idx['Cond']\n",
    "ea_idx_osmotic = ea_idx[ea_idx['Stress'] == 'osmotic'].set_index(['Strain', 'MediumID', 'Medium', 'GP', 'Stress'])\n",
    "ea_idx_osmotic = ea_idx_osmotic.groupby(level=['Stress', 'Strain', 'MediumID', 'Medium', 'GP'], axis=0).mean()\n",
    "fc_osmotic = {}\n",
    "for i,row in ea_idx_osmotic.iterrows():\n",
    "    _df = ea_idx[(ea_idx['Stress'] == 'none')&(ea_idx['Strain'] == i[1])&(ea_idx['MediumID'] == i[2])&\n",
    "                 (ea_idx['Medium'] == i[3])&(ea_idx['GP'] == i[4])].mean().to_frame()\n",
    "    _df.insert(0, 'osmotic', row)\n",
    "    _df.columns = ['osmotic', 'wt']\n",
    "    ratio = _df['osmotic'] / _df['wt']\n",
    "    ratio = ratio.replace(.0,  np.nextafter(0, 1))\n",
    "    fc_osmotic[i] = np.log2(ratio).abs()\n",
    "fc_osmotic = pd.DataFrame(fc_osmotic)\n",
    "fc_osmotic_ranked = list(fc_osmotic.mean(axis=1).sort_values(ascending=False).index)\n",
    "\n",
    "x_osmotic = np.arange(0,2000,100)\n",
    "y_osmotic_ecomics = [len(set(fc_osmotic_ranked[:k]).intersection(set(osmotic_genes))) / len(osmotic_genes) for k in x_osmotic]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COLOMBOS osmotic genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sucrose_ctrs_ref = colombos_ref_annot[colombos_ref_annot['RefAnnot'] == 'SUCROSE']['Value'].to_frame()\n",
    "sucrose_ctrs_test = colombos_test_annot[colombos_test_annot['TestAnnot'] == 'SUCROSE']['Value'].to_frame()\n",
    "sucrose_ctrs = pd.DataFrame(columns=['test', 'ref'], index=list(sucrose_ctrs_ref.index) + list(sucrose_ctrs_test.index))\n",
    "sucrose_ctrs['test'] = sucrose_ctrs_test\n",
    "sucrose_ctrs['ref'] = sucrose_ctrs_ref\n",
    "sucrose_ctrs = sucrose_ctrs[sucrose_ctrs['test'] != sucrose_ctrs['ref']]\n",
    "nacl_ctrs_ref = colombos_ref_annot[colombos_ref_annot['RefAnnot'] == 'NaCl']['Value'].to_frame()\n",
    "nacl_ctrs_test = colombos_test_annot[colombos_test_annot['TestAnnot'] == 'NaCl']['Value'].to_frame()\n",
    "nacl_ctrs = pd.DataFrame(columns=['test', 'ref'], index=list(nacl_ctrs_ref.index) + list(nacl_ctrs_test.index))\n",
    "nacl_ctrs['test'] = nacl_ctrs_test\n",
    "nacl_ctrs['ref'] = nacl_ctrs_ref\n",
    "nacl_ctrs = nacl_ctrs[nacl_ctrs['test'] != nacl_ctrs['ref']]\n",
    "osmotic_ctrs = list(nacl_ctrs.index) + list(sucrose_ctrs.index)\n",
    "\n",
    "colombos_osmotic_ranked = colombos_exp_data[colombos_ctr_data[osmotic_ctrs].loc['Contrast ID'].values].abs().mean(axis=1).sort_values(ascending=False).index\n",
    "colombos_osmotic_ranked = colombos_gene_data.loc[colombos_osmotic_ranked]['LocusTag']\n",
    "y_osmotic_colombos = [len(set(colombos_osmotic_ranked[:k]).intersection(set(osmotic_genes))) / len(osmotic_genes) for k in x_osmotic]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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MgEMPhc8+g3/8A445Jt2JJEnaYiz2JEl106JFsN9+8P338OyzcMAB6U4kSdIW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd04db0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sea\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.plot(x_osmotic, y_osmotic_ecomics, linestyle='--', marker='o', color='g', label='Ecomics v8')\n",
    "plt.plot(x_osmotic, y_osmotic_colombos, linestyle='--', marker='o', color='r', label='COLOMBOS')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Osmotic')\n",
    "plt.xlabel('Top ranked genes')\n",
    "plt.ylabel('Ratio of known genes in top ranked genes')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}