Mercurial > repos > dereeper > roary_plots

comparison Roary/contrib/roary_plots/roary_plots.py @ 0:c47a5f61bc9f draft

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author dereeper
date Fri, 14 May 2021 20:27:06 +0000
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1 #!/usr/bin/env python
2 # Copyright (C) <2015> EMBL-European Bioinformatics Institute
3
4 # This program is free software: you can redistribute it and/or
5 # modify it under the terms of the GNU General Public License as
6 # published by the Free Software Foundation, either version 3 of
7 # the License, or (at your option) any later version.
8
9 # This program is distributed in the hope that it will be useful,
10 # but WITHOUT ANY WARRANTY; without even the implied warranty of
11 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 # GNU General Public License for more details.
13
14 # Neither the institution name nor the name roary_plots
15 # can be used to endorse or promote products derived from
16 # this software without prior written permission.
17 # For written permission, please contact <marco@ebi.ac.uk>.
18
19 # Products derived from this software may not be called roary_plots
20 # nor may roary_plots appear in their names without prior written
21 # permission of the developers. You should have received a copy
22 # of the GNU General Public License along with this program.
23 # If not, see <http://www.gnu.org/licenses/>.
24
25 __author__ = "Marco Galardini"
26 __version__ = '0.1.0'
27
28 def get_options():
29 import argparse
30
31 # create the top-level parser
32 description = "Create plots from roary outputs"
33 parser = argparse.ArgumentParser(description = description,
34 prog = 'roary_plots.py')
35
36 parser.add_argument('tree', action='store',
37 help='Newick Tree file', default='accessory_binary_genes.fa.newick')
38 parser.add_argument('spreadsheet', action='store',
39 help='Roary gene presence/absence spreadsheet', default='gene_presence_absence.csv')
40
41 parser.add_argument('--labels', action='store_true',
42 default=False,
43 help='Add node labels to the tree (up to 10 chars)')
44 parser.add_argument('--format',
45 choices=('png',
46 'tiff',
47 'pdf',
48 'svg'),
49 default='png',
50 help='Output format [Default: png]')
51 parser.add_argument('-N', '--skipped-columns', action='store',
52 type=int,
53 default=14,
54 help='First N columns of Roary\'s output to exclude [Default: 14]')
55
56 parser.add_argument('--version', action='version',
57 version='%(prog)s '+__version__)
58
59 return parser.parse_args()
60
61 if __name__ == "__main__":
62 options = get_options()
63
64 import matplotlib
65 matplotlib.use('Agg')
66
67 import matplotlib.pyplot as plt
68 import seaborn as sns
69
70 sns.set_style('white')
71
72 import os
73 import pandas as pd
74 import numpy as np
75 from Bio import Phylo
76
77 t = Phylo.read(options.tree, 'newick')
78
79 # Max distance to create better plots
80 mdist = max([t.distance(t.root, x) for x in t.get_terminals()])
81
82 # Load roary
83 roary = pd.read_csv(options.spreadsheet, low_memory=False)
84 # Set index (group name)
85 roary.set_index('Gene', inplace=True)
86 # Drop the other info columns
87 roary.drop(list(roary.columns[:options.skipped_columns-1]), axis=1, inplace=True)
88
89 # Transform it in a presence/absence matrix (1/0)
90 roary.replace('.{2,100}', 1, regex=True, inplace=True)
91 roary.replace(np.nan, 0, regex=True, inplace=True)
92
93 # Sort the matrix by the sum of strains presence
94 idx = roary.sum(axis=1).sort_values(ascending=False).index
95 roary_sorted = roary.loc[idx]
96
97 # Pangenome frequency plot
98 plt.figure(figsize=(7, 5))
99
100 plt.hist(roary.sum(axis=1), roary.shape[1],
101 histtype="stepfilled", alpha=.7)
102
103 plt.xlabel('No. of genomes')
104 plt.ylabel('No. of genes')
105
106 sns.despine(left=True,
107 bottom=True)
108 plt.savefig('pangenome_frequency.%s'%options.format, dpi=300)
109 plt.clf()
110
111 # Sort the matrix according to tip labels in the tree
112 roary_sorted = roary_sorted[[x.name for x in t.get_terminals()]]
113
114 # Plot presence/absence matrix against the tree
115 with sns.axes_style('whitegrid'):
116 fig = plt.figure(figsize=(17, 10))
117
118 ax1=plt.subplot2grid((1,40), (0, 10), colspan=30)
119 a=ax1.matshow(roary_sorted.T, cmap=plt.cm.Blues,
120 vmin=0, vmax=1,
121 aspect='auto',
122 interpolation='none',
123 )
124 ax1.set_yticks([])
125 ax1.set_xticks([])
126 ax1.axis('off')
127
128 ax = fig.add_subplot(1,2,1)
129 # matplotlib v1/2 workaround
130 try:
131 ax=plt.subplot2grid((1,40), (0, 0), colspan=10, facecolor='white')
132 except AttributeError:
133 ax=plt.subplot2grid((1,40), (0, 0), colspan=10, axisbg='white')
134
135 fig.subplots_adjust(wspace=0, hspace=0)
136
137 ax1.set_title('Roary matrix\n(%d gene clusters)'%roary.shape[0])
138
139 if options.labels:
140 fsize = 12 - 0.1*roary.shape[1]
141 if fsize < 7:
142 fsize = 7
143 with plt.rc_context({'font.size': fsize}):
144 Phylo.draw(t, axes=ax,
145 show_confidence=False,
146 label_func=lambda x: str(x)[:10],
147 xticks=([],), yticks=([],),
148 ylabel=('',), xlabel=('',),
149 xlim=(-mdist*0.1,mdist+mdist*0.45-mdist*roary.shape[1]*0.001),
150 axis=('off',),
151 title=('Tree\n(%d strains)'%roary.shape[1],),
152 do_show=False,
153 )
154 else:
155 Phylo.draw(t, axes=ax,
156 show_confidence=False,
157 label_func=lambda x: None,
158 xticks=([],), yticks=([],),
159 ylabel=('',), xlabel=('',),
160 xlim=(-mdist*0.1,mdist+mdist*0.1),
161 axis=('off',),
162 title=('Tree\n(%d strains)'%roary.shape[1],),
163 do_show=False,
164 )
165 plt.savefig('pangenome_matrix.%s'%options.format, dpi=300)
166 plt.clf()
167
168 # Plot the pangenome pie chart
169 plt.figure(figsize=(10, 10))
170
171 core = roary[(roary.sum(axis=1) >= roary.shape[1]*0.99) & (roary.sum(axis=1) <= roary.shape[1] )].shape[0]
172 softcore = roary[(roary.sum(axis=1) >= roary.shape[1]*0.95) & (roary.sum(axis=1) < roary.shape[1]*0.99)].shape[0]
173 shell = roary[(roary.sum(axis=1) >= roary.shape[1]*0.15) & (roary.sum(axis=1) < roary.shape[1]*0.95)].shape[0]
174 cloud = roary[roary.sum(axis=1) < roary.shape[1]*0.15].shape[0]
175
176 total = roary.shape[0]
177
178 def my_autopct(pct):
179 val=int(round(pct*total/100.0))
180 return '{v:d}'.format(v=val)
181
182 a=plt.pie([core, softcore, shell, cloud],
183 labels=['core\n(%d <= strains <= %d)'%(roary.shape[1]*.99,roary.shape[1]),
184 'soft-core\n(%d <= strains < %d)'%(roary.shape[1]*.95,roary.shape[1]*.99),
185 'shell\n(%d <= strains < %d)'%(roary.shape[1]*.15,roary.shape[1]*.95),
186 'cloud\n(strains < %d)'%(roary.shape[1]*.15)],
187 explode=[0.1, 0.05, 0.02, 0], radius=0.9,
188 colors=[(0, 0, 1, float(x)/total) for x in (core, softcore, shell, cloud)],
189 autopct=my_autopct)
190 plt.savefig('pangenome_pie.%s'%options.format, dpi=300)
191 plt.clf()