work/6_ag1k_extraction/1_ag1k_within_species_distances.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run common.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPP = ['gambiae', 'coluzzii']\n",
    "METRIC = 'hamming'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single amplicon processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(654,), (283,)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample names per species\n",
    "sps = dict()\n",
    "for sp in SPP:\n",
    "    sps[sp] = samples.loc[samples.species==sp, 'ox_code']\n",
    "display([x.shape for x in sps.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read amplicon data\n",
    "ampl = '60'\n",
    "callset = zarr.open_group(AMPL_HAP_ZARR, 'r')\n",
    "gt = allel.GenotypeArray(callset[ampl]['calldata/genotype'])\n",
    "# extract samples - those differ between autosome and X\n",
    "gs = pd.Series(callset[ampl]['samples'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuber of variants\n",
    "nvar = gt.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(37, 573, 2), (37, 280, 2)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract genotypes per species\n",
    "gts = dict()\n",
    "for sp in SPP:\n",
    "    gts[sp] = gt[:, gs.isin(sps[sp]), :]\n",
    "display([x.shape for x in gts.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1146, 37), (560, 37)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert to haplotypes\n",
    "hts = dict()\n",
    "for sp in SPP:\n",
    "    hts[sp] = gts[sp].reshape(gts[sp].shape[0], 2 * gts[sp].shape[1]).transpose()\n",
    "display([x.shape for x in hts.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pairwise distances\n",
    "pds = dict()\n",
    "# within sp\n",
    "for sp in SPP:\n",
    "    pds[sp] = pdist(hts[sp], metric=METRIC)\n",
    "# between spp for pairwise combinations of species\n",
    "pds['between_spp'] = np.array([])\n",
    "for (sp1, sp2) in itertools.combinations(SPP, 2):\n",
    "    pds['between_spp'] = np.append(pds['between_spp'],\n",
    "                                   cdist(hts[sp1],hts[sp2], metric=METRIC))\n",
    "# combine multiple\n",
    "for (sp, dist) in pds.items():\n",
    "    plt.hist(dist * nvar, bins=range(8), alpha=0.5, label=sp);\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91"
     ]
    }
   ],
   "source": [
    "callset = zarr.open_group(AMPL_HAP_ZARR, 'r')\n",
    "# accumulate pairwise distances into dict\n",
    "pds = dict()\n",
    "\n",
    "for ampl in callset:\n",
    "    sys.stdout.write('\\r' + ampl)\n",
    "    pds[ampl] = dict()\n",
    "    # read genotypes\n",
    "    gt = allel.GenotypeArray(callset[ampl]['calldata/genotype'])\n",
    "    # extract samples - those differ between autosome and X amplicons\n",
    "    gs = pd.Series(callset[ampl]['samples'][:])\n",
    "    # nuber of variants\n",
    "    pds[ampl]['nvar'] = gt.shape[0]\n",
    "    # extract genotypes per species\n",
    "    gts = dict()\n",
    "    for sp in SPP:\n",
    "        gts[sp] = gt[:, gs.isin(sps[sp]), :]\n",
    "    # convert to haplotypes\n",
    "    hts = dict()\n",
    "    for sp in SPP:\n",
    "        hts[sp] = gts[sp].reshape(gts[sp].shape[0], 2 * gts[sp].shape[1]).transpose()\n",
    "    # pairwise distances\n",
    "    # within sp\n",
    "    for sp in SPP:\n",
    "        pds[ampl][sp] = pdist(hts[sp], metric=METRIC)\n",
    "    # between spp for pairwise combinations of species\n",
    "    pds[ampl]['between_spp'] = np.array([])\n",
    "    for (sp1, sp2) in itertools.combinations(SPP, 2):\n",
    "        pds[ampl]['between_spp'] = np.append(pds[ampl]['between_spp'],\n",
    "                                             cdist(hts[sp1],hts[sp2], metric=METRIC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(8,8, figsize=(15,15))\n",
    "for i, ampl in enumerate(AMPLS):\n",
    "    ax = axs.flatten()[i]\n",
    "    n = pds[ampl]['nvar']\n",
    "    for sp in SPP + ['between_spp']:\n",
    "        pd_var = pds[ampl][sp] * n\n",
    "        bins = np.arange(0, int(pd_var.max()) + 1.5) - 0.5\n",
    "        ax.hist(pd_var, \n",
    "                bins=bins, \n",
    "                alpha=0.4,\n",
    "                label=sp);\n",
    "        ax.set_xticks(bins + 0.5)\n",
    "        if i == 0:\n",
    "            ax.legend()\n",
    "    ax.set_title(ampl)\n",
    "#     ax.set_xscale('log')\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylim(0.9)\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Within-species distance thresholds\n",
    "\n",
    "Above debug histogram shows that in most cases the distributions of pairwise distances between haplotypes are contiguous, thus taking maximum observed within-species distances will work as a threshold. The only doubtful amplicon is 33, where gambiae is far much more variable than both coluzzii and between-species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_data = panel_mosquito.loc[:, ['start_insert','end_insert']].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sequence length in agam genome, both start and end positions taken into insert by subsetting code \n",
    "# (see previous notebook)\n",
    "var_data['len_insert'] = var_data.end_insert - var_data.start_insert + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find maximum within-species distances across both species\n",
    "def max_sp_pd_var(ampl):\n",
    "    max_sp_dist = [max(pds[ampl][sp]) for sp in SPP]\n",
    "    return max(max_sp_dist) * pds[ampl]['nvar']\n",
    "var_data['max_withinspecies_var'] = var_data.Primary_ID.apply(max_sp_pd_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Primary_ID</th>\n",
       "      <th>start_insert</th>\n",
       "      <th>end_insert</th>\n",
       "      <th>len_insert</th>\n",
       "      <th>max_withinspecies_var</th>\n",
       "      <th>max_withinspecies_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10356439</td>\n",
       "      <td>10356587</td>\n",
       "      <td>149</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.033557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10357476</td>\n",
       "      <td>10357629</td>\n",
       "      <td>154</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12084517</td>\n",
       "      <td>12084667</td>\n",
       "      <td>151</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.039735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13862890</td>\n",
       "      <td>13863031</td>\n",
       "      <td>142</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.028169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>18693503</td>\n",
       "      <td>18693693</td>\n",
       "      <td>191</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.020942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Primary_ID  start_insert  end_insert  len_insert  max_withinspecies_var  \\\n",
       "0          0      10356439    10356587         149                    5.0   \n",
       "1          1      10357476    10357629         154                    2.0   \n",
       "2          2      12084517    12084667         151                    6.0   \n",
       "3          3      13862890    13863031         142                    4.0   \n",
       "4          4      18693503    18693693         191                    4.0   \n",
       "\n",
       "   max_withinspecies_dist  \n",
       "0                0.033557  \n",
       "1                0.012987  \n",
       "2                0.039735  \n",
       "3                0.028169  \n",
       "4                0.020942  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalise by sequence length\n",
    "var_data['max_withinspecies_dist'] = var_data.max_withinspecies_var / var_data.len_insert\n",
    "var_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primary_ID,start_insert,end_insert,len_insert,max_withinspecies_var,max_withinspecies_dist\r\n",
      "0,10356439,10356587,149,5.0,0.03355704697986577\r\n",
      "1,10357476,10357629,154,2.0,0.012987012987012988\r\n",
      "2,12084517,12084667,151,6.0,0.039735099337748346\r\n",
      "3,13862890,13863031,142,4.0,0.028169014084507043\r\n",
      "4,18693503,18693693,191,4.0,0.020942408376963352\r\n",
      "5,19065193,19065344,152,7.0,0.046052631578947366\r\n",
      "6,20840408,20840599,192,5.0,0.026041666666666668\r\n",
      "7,26760718,26760865,148,7.0,0.0472972972972973\r\n",
      "8,27674665,27674814,150,6.0,0.04\r\n"
     ]
    }
   ],
   "source": [
    "# write\n",
    "var_data.to_csv(WSP_VAR_FILE, index=False)\n",
    "! head {WSP_VAR_FILE}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare to previous estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>target</th>\n",
       "      <th>dist_threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.088000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.039216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.064039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target  dist_threshold\n",
       "0       0        0.088000\n",
       "1       1        0.039216\n",
       "2       2        0.064039\n",
       "3       3        0.043478\n",
       "4       4        0.052632"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate based on extended sample set\n",
    "seq_wsp_file = '../../../data/phylo_ampl_dada2/comb1_5/3_thresholds.tsv'\n",
    "seq_wsp = pd.read_csv(seq_wsp_file, sep='\\t')\n",
    "seq_wsp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to dict\n",
    "seq_t = seq_wsp.set_index('target')['dist_threshold'].to_dict()\n",
    "var_data['seq_threshold'] = var_data.Primary_ID.astype(int).replace(seq_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(10,4))\n",
    "ax.scatter(var_data.Primary_ID.astype(int), var_data.seq_threshold, c='b', alpha=0.5, label='sequencing')\n",
    "ax.scatter(var_data.Primary_ID.astype(int), var_data.max_withinspecies_dist, c='r', alpha=0.5, label='Ag1000g')\n",
    "ticks = range(62)\n",
    "ax.set_xticks(ticks)\n",
    "plt.xticks(rotation=45)\n",
    "for t in ticks:\n",
    "    ax.axvline(t - 0.5, c='grey')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(4,4))\n",
    "ax.scatter(var_data.max_withinspecies_dist, \n",
    "           var_data.seq_threshold)\n",
    "ax.set_xlabel('Ag1000g max dist')\n",
    "ax.set_ylabel('Sequencing data threshold')\n",
    "ax.plot([0, 0.1], [0, 0.1], c='grey', ls='--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thresholds are underestimated for the most variable compared to sequencing data - potentially because of lack of indel data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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