mgalardini/roary.ipynb

## roary.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roary pangenome plots\n",
    "============="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h6><a href=\"javascript:toggle()\" target=\"_self\">Toggle source code</a></h6>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plotting imports\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Other imports\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from Bio import Phylo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "parSNP tree\n",
    "-------------\n",
    "\n",
    "Any other valid newick file is fine, if the tip labels is the same as in the gene_presence_absence matrix from roary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t = Phylo.read('parsnp.tree', 'newick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Max distance to create better plots\n",
    "mdist = max([t.distance(t.root, x) for x in t.get_terminals()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roary\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load roary\n",
    "roary = pd.read_table('gene_presence_absence.csv',\n",
    "                     sep=',',\n",
    "                     low_memory=False)\n",
    "# Set index (group name)\n",
    "roary.set_index('Gene', inplace=True)\n",
    "# Drop the other info columns\n",
    "roary.drop(list(roary.columns[:10]), axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Transform it in a presence/absence matrix (1/0)\n",
    "roary.replace('.{2,100}', 1, regex=True, inplace=True)\n",
    "roary.replace(np.nan, 0, regex=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sort the matrix by the sum of strains presence\n",
    "idx = roary.sum(axis=1).order(ascending=False).index\n",
    "roary_sorted = roary.ix[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0179b9bc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pangenome frequency plot\n",
    "plt.figure(figsize=(7, 5))\n",
    "\n",
    "plt.hist(roary.sum(axis=1), roary.shape[1],\n",
    "         histtype=\"stepfilled\", alpha=.7)\n",
    "\n",
    "plt.xlabel('Number of genomes')\n",
    "plt.ylabel('Number of genes')\n",
    "\n",
    "sns.despine(left=True,\n",
    "            bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Sort the matrix according to tip labels in the tree\n",
    "roary_sorted = roary_sorted[[x.name for x in t.get_terminals()]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017bff06d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLot presence/absence matrix against the tree\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig = plt.figure(figsize=(17, 10))\n",
    "\n",
    "    ax1=plt.subplot2grid((1,40), (0, 10), colspan=30)\n",
    "    a=ax1.imshow(roary_sorted.T, cmap=plt.cm.Blues,\n",
    "               vmin=0, vmax=1,\n",
    "               aspect='auto',\n",
    "               interpolation='none',\n",
    "                )\n",
    "    ax1.set_yticks([])\n",
    "    ax1.set_xticks([])\n",
    "    ax1.axis('off')\n",
    "\n",
    "    ax = fig.add_subplot(1,2,1)\n",
    "    ax=plt.subplot2grid((1,40), (0, 0), colspan=10, axisbg='white')\n",
    "\n",
    "    fig.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "    ax1.set_title('Roary matrix\\n(%d gene clusters)'%roary.shape[0])\n",
    "\n",
    "    Phylo.draw(t, axes=ax, \n",
    "               show_confidence=False,\n",
    "               label_func=lambda x: None,\n",
    "               xticks=([],), yticks=([],),\n",
    "               ylabel=('',), xlabel=('',),\n",
    "               xlim=(-0.01,mdist+0.01),\n",
    "               axis=('off',),\n",
    "               title=('parSNP tree\\n(%d strains)'%roary.shape[1],), \n",
    "              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "/IiIiEik/H+Z7d6juNrEQQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017b665bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the pangenome pie chart\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "core = roary[roary.sum(axis=1) == roary.shape[1]].shape[0]\n",
    "softcore = roary[(roary.sum(axis=1) < roary.shape[1]) &\n",
    "                 (roary.sum(axis=1) >= roary.shape[1]*0.95)].shape[0]\n",
    "shell = roary[(roary.sum(axis=1) < roary.shape[1]*0.95) &\n",
    "                 (roary.sum(axis=1) >= roary.shape[1]*0.15)].shape[0]\n",
    "cloud = roary[roary.sum(axis=1) < roary.shape[1]*0.15].shape[0]\n",
    "\n",
    "total = roary.shape[0]\n",
    "\n",
    "def my_autopct(pct):\n",
    "    val=int(pct*total/100.0)\n",
    "    return '{v:d}'.format(v=val)\n",
    "\n",
    "a=plt.pie([core, softcore, shell, cloud],\n",
    "      labels=['core\\n(%d strains)'%roary.shape[1],\n",
    "              'soft-core\\n(%d <= strains < %d)'%(roary.shape[1]*.95,\n",
    "                                                 roary.shape[1]),\n",
    "              'shell\\n(%d <= strains < %d)'%(roary.shape[1]*.15,\n",
    "                                             roary.shape[1]*.95),\n",
    "              'cloud\\n(strains < %d)'%(roary.shape[1]*.15)],\n",
    "      explode=[0.1, 0.05, 0.02, 0], radius=0.9,\n",
    "      colors=[(0, 0, 1, float(x)/total) for x in (core, softcore, shell, cloud)],\n",
    "      autopct=my_autopct)"
   ]
  }
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## roary.py
#!/usr/bin/env python
# Copyright (C) <2015> EMBL-European Bioinformatics Institute

# This program is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.

# Neither the institution name nor the name roary_plots
# can be used to endorse or promote products derived from
# this software without prior written permission.
# For written permission, please contact <marco@ebi.ac.uk>.

# Products derived from this software may not be called roary_plots
# nor may roary_plots appear in their names without prior written
# permission of the developers. You should have received a copy
# of the GNU General Public License along with this program.
# If not, see <http://www.gnu.org/licenses/>.

__author__ = "Marco Galardini"
__version__ = '0.1.0'

def get_options():
    import argparse

    # create the top-level parser
    description = "Create plots from roary outputs"
    parser = argparse.ArgumentParser(description = description,
                                     prog = 'roary_plots.py')

    parser.add_argument('tree', action='store',
                        help='Tree Newick file')
    parser.add_argument('roary', action='store',
                        help='Roary gene presence/absence table')

    parser.add_argument('--version', action='version',
                         version='%(prog)s '+__version__)

    return parser.parse_args()

if __name__ == "__main__":
    options = get_options()

    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.set_style('white')

    import os
    import pandas as pd
    import numpy as np
    from Bio import Phylo

    t = Phylo.read(options.tree, 'newick')

    # Max distance to create better plots
    mdist = max([t.distance(t.root, x) for x in t.get_terminals()])

    # Load roary
    roary = pd.read_table(options.roary,
                         sep=',',
                         low_memory=False)
    # Set index (group name)
    roary.set_index('Gene', inplace=True)
    # Drop the other info columns
    roary.drop(list(roary.columns[:10]), axis=1, inplace=True)

    # Transform it in a presence/absence matrix (1/0)
    roary.replace('.{2,100}', 1, regex=True, inplace=True)
    roary.replace(np.nan, 0, regex=True, inplace=True)

    # Sort the matrix by the sum of strains presence
    idx = roary.sum(axis=1).order(ascending=False).index
    roary_sorted = roary.ix[idx]

    # Pangenome frequency plot
    plt.figure(figsize=(7, 5))

    plt.hist(roary.sum(axis=1), roary.shape[1],
             histtype="stepfilled", alpha=.7)

    plt.xlabel('Number of genomes')
    plt.ylabel('Number of genes')

    sns.despine(left=True,
                bottom=True)
    plt.savefig('pangenome_frequency.png')
    plt.clf()

    # Sort the matrix according to tip labels in the tree
    roary_sorted = roary_sorted[[x.name for x in t.get_terminals()]]

    # Plot presence/absence matrix against the tree
    with sns.axes_style('whitegrid'):
        fig = plt.figure(figsize=(17, 10))

        ax1=plt.subplot2grid((1,40), (0, 10), colspan=30)
        a=ax1.matshow(roary_sorted.T, cmap=plt.cm.Blues,
                   vmin=0, vmax=1,
                   aspect='auto',
                   interpolation='none',
                    )
        ax1.set_yticks([])
        ax1.set_xticks([])
        ax1.axis('off')

        ax = fig.add_subplot(1,2,1)
        ax=plt.subplot2grid((1,40), (0, 0), colspan=10, axisbg='white')

        fig.subplots_adjust(wspace=0, hspace=0)

        ax1.set_title('Roary matrix\n(%d gene clusters)'%roary.shape[0])

        Phylo.draw(t, axes=ax,
                   show_confidence=False,
                   label_func=lambda x: None,
                   xticks=([],), yticks=([],),
                   ylabel=('',), xlabel=('',),
                   xlim=(-0.01,mdist+0.01),
                   axis=('off',),
                   title=('parSNP tree\n(%d strains)'%roary.shape[1],),
                   do_show=False,
                  )
        plt.savefig('pangenome_matrix.png')
        plt.clf()

    # Plot the pangenome pie chart
    plt.figure(figsize=(10, 10))

    core = roary[roary.sum(axis=1) == roary.shape[1]].shape[0]
    softcore = roary[(roary.sum(axis=1) < roary.shape[1]) &
                     (roary.sum(axis=1) >= roary.shape[1]*0.95)].shape[0]
    shell = roary[(roary.sum(axis=1) < roary.shape[1]*0.95) &
                     (roary.sum(axis=1) >= roary.shape[1]*0.15)].shape[0]
    cloud = roary[roary.sum(axis=1) < roary.shape[1]*0.15].shape[0]

    total = roary.shape[0]

    def my_autopct(pct):
        val=int(pct*total/100.0)
        return '{v:d}'.format(v=val)

    a=plt.pie([core, softcore, shell, cloud],
          labels=['core\n(%d strains)'%roary.shape[1],
                  'soft-core\n(%d <= strains < %d)'%(roary.shape[1]*.95,
                                                     roary.shape[1]),
                  'shell\n(%d <= strains < %d)'%(roary.shape[1]*.15,
                                                 roary.shape[1]*.95),
                  'cloud\n(strains < %d)'%(roary.shape[1]*.15)],
          explode=[0.1, 0.05, 0.02, 0], radius=0.9,
          colors=[(0, 0, 1, float(x)/total) for x in (core, softcore, shell, cloud)],
          autopct=my_autopct)
    plt.savefig('pangenome_pie.png')
    plt.clf()
	#!/usr/bin/env python
	# Copyright (C) <2015> EMBL-European Bioinformatics Institute

	# This program is free software: you can redistribute it and/or
	# modify it under the terms of the GNU General Public License as
	# published by the Free Software Foundation, either version 3 of
	# the License, or (at your option) any later version.

	# This program is distributed in the hope that it will be useful,
	# but WITHOUT ANY WARRANTY; without even the implied warranty of
	# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	# GNU General Public License for more details.

	# Neither the institution name nor the name roary_plots
	# can be used to endorse or promote products derived from
	# this software without prior written permission.
	# For written permission, please contact <marco@ebi.ac.uk>.

	# Products derived from this software may not be called roary_plots
	# nor may roary_plots appear in their names without prior written
	# permission of the developers. You should have received a copy
	# of the GNU General Public License along with this program.
	# If not, see <http://www.gnu.org/licenses/>.

	__author__ = "Marco Galardini"
	__version__ = '0.1.0'

	def get_options():
	import argparse

	# create the top-level parser
	description = "Create plots from roary outputs"
	parser = argparse.ArgumentParser(description = description,
	prog = 'roary_plots.py')

	parser.add_argument('tree', action='store',
	help='Tree Newick file')
	parser.add_argument('roary', action='store',
	help='Roary gene presence/absence table')

	parser.add_argument('--version', action='version',
	version='%(prog)s '+__version__)

	return parser.parse_args()

	if __name__ == "__main__":
	options = get_options()

	import matplotlib.pyplot as plt
	import seaborn as sns

	sns.set_style('white')

	import os
	import pandas as pd
	import numpy as np
	from Bio import Phylo

	t = Phylo.read(options.tree, 'newick')

	# Max distance to create better plots
	mdist = max([t.distance(t.root, x) for x in t.get_terminals()])

	# Load roary
	roary = pd.read_table(options.roary,
	sep=',',
	low_memory=False)
	# Set index (group name)
	roary.set_index('Gene', inplace=True)
	# Drop the other info columns
	roary.drop(list(roary.columns[:10]), axis=1, inplace=True)

	# Transform it in a presence/absence matrix (1/0)
	roary.replace('.{2,100}', 1, regex=True, inplace=True)
	roary.replace(np.nan, 0, regex=True, inplace=True)

	# Sort the matrix by the sum of strains presence
	idx = roary.sum(axis=1).order(ascending=False).index
	roary_sorted = roary.ix[idx]

	# Pangenome frequency plot
	plt.figure(figsize=(7, 5))

	plt.hist(roary.sum(axis=1), roary.shape[1],
	histtype="stepfilled", alpha=.7)

	plt.xlabel('Number of genomes')
	plt.ylabel('Number of genes')

	sns.despine(left=True,
	bottom=True)
	plt.savefig('pangenome_frequency.png')
	plt.clf()

	# Sort the matrix according to tip labels in the tree
	roary_sorted = roary_sorted[[x.name for x in t.get_terminals()]]

	# Plot presence/absence matrix against the tree
	with sns.axes_style('whitegrid'):
	fig = plt.figure(figsize=(17, 10))

	ax1=plt.subplot2grid((1,40), (0, 10), colspan=30)
	a=ax1.matshow(roary_sorted.T, cmap=plt.cm.Blues,
	vmin=0, vmax=1,
	aspect='auto',
	interpolation='none',
	)
	ax1.set_yticks([])
	ax1.set_xticks([])
	ax1.axis('off')

	ax = fig.add_subplot(1,2,1)
	ax=plt.subplot2grid((1,40), (0, 0), colspan=10, axisbg='white')

	fig.subplots_adjust(wspace=0, hspace=0)

	ax1.set_title('Roary matrix\n(%d gene clusters)'%roary.shape[0])

	Phylo.draw(t, axes=ax,
	show_confidence=False,
	label_func=lambda x: None,
	xticks=([],), yticks=([],),
	ylabel=('',), xlabel=('',),
	xlim=(-0.01,mdist+0.01),
	axis=('off',),
	title=('parSNP tree\n(%d strains)'%roary.shape[1],),
	do_show=False,
	)
	plt.savefig('pangenome_matrix.png')
	plt.clf()

	# Plot the pangenome pie chart
	plt.figure(figsize=(10, 10))

	core = roary[roary.sum(axis=1) == roary.shape[1]].shape[0]
	softcore = roary[(roary.sum(axis=1) < roary.shape[1]) &
	(roary.sum(axis=1) >= roary.shape[1]*0.95)].shape[0]
	shell = roary[(roary.sum(axis=1) < roary.shape[1]*0.95) &
	(roary.sum(axis=1) >= roary.shape[1]*0.15)].shape[0]
	cloud = roary[roary.sum(axis=1) < roary.shape[1]*0.15].shape[0]

	total = roary.shape[0]

	def my_autopct(pct):
	val=int(pct*total/100.0)
	return '{v:d}'.format(v=val)

	a=plt.pie([core, softcore, shell, cloud],
	labels=['core\n(%d strains)'%roary.shape[1],
	'soft-core\n(%d <= strains < %d)'%(roary.shape[1]*.95,
	roary.shape[1]),
	'shell\n(%d <= strains < %d)'%(roary.shape[1]*.15,
	roary.shape[1]*.95),
	'cloud\n(strains < %d)'%(roary.shape[1]*.15)],
	explode=[0.1, 0.05, 0.02, 0], radius=0.9,
	colors=[(0, 0, 1, float(x)/total) for x in (core, softcore, shell, cloud)],
	autopct=my_autopct)
	plt.savefig('pangenome_pie.png')
	plt.clf()