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mod_lefse-plot_res.py
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#!/usr/bin/env python
import os
import sys
sys.path.append('/home/admin1/miniconda3/envs/lefse/share/lefse-1.0.8.post1-1')
sys.path.append('/home/cheng/softwares/miniconda2/share/lefse-1.0.8.post1-1')
# sys.path.append('/home/admin1/github/Bayegy')
import matplotlib
matplotlib.use('Agg')
from pylab import *
from collections import defaultdict
from lefse import *
import argparse
from Bayegy.getColors import get_lefse_colors
def read_params(args):
parser = argparse.ArgumentParser(description='Plot results')
parser.add_argument('input_file', metavar='INPUT_FILE', type=str, help="tab delimited input file")
parser.add_argument('output_file', metavar='OUTPUT_FILE', type=str, help="the file for the output image")
parser.add_argument('--map', dest="map", type=str, default=False)
parser.add_argument('--category', dest="category", type=str, default=False)
parser.add_argument('--colors', dest="colors", type=str, default=False)
parser.add_argument('--feature_font_size', dest="feature_font_size",
type=int, default=7, help="the file for the output image")
parser.add_argument('--format', dest="format",
choices=["png", "svg", "pdf"], default='png', type=str, help="the format for the output file")
parser.add_argument('--dpi', dest="dpi", type=int, default=300)
parser.add_argument('--title', dest="title", type=str, default="")
parser.add_argument('--title_font_size', dest="title_font_size", type=str, default="12")
parser.add_argument('--class_legend_font_size', dest="class_legend_font_size", type=str, default="10")
parser.add_argument('--width', dest="width", type=float, default=12.0)
parser.add_argument('--height', dest="height", type=float, default=4.0, help="only for vertical histograms")
parser.add_argument('--left_space', dest="ls", type=float, default=0.26)
parser.add_argument('--right_space', dest="rs", type=float, default=0.1)
parser.add_argument('--orientation', dest="orientation", type=str, choices=["h", "v"], default="h")
parser.add_argument('--autoscale', dest="autoscale", type=int, choices=[0, 1], default=1)
parser.add_argument('--background_color', dest="back_color", type=str,
choices=["k", "w"], default="w", help="set the color of the background")
parser.add_argument('--subclades', dest="n_scl", type=int, default=1,
help="number of label levels to be dislayed (starting from the leaves, -1 means all the levels, 1 is default )")
parser.add_argument('--max_feature_len', dest="max_feature_len", type=int,
default=60, help="Maximum length of feature strings (def 60)")
parser.add_argument('--all_feats', dest="all_feats", type=str, default="")
parser.add_argument('--otu_only', dest="otu_only", default=False, action='store_true',
help="Plot only species resolved OTUs (as opposed to all levels)")
parser.add_argument('--report_features', dest="report_features", default=False,
action='store_true', help="Report important features to STDOUT")
args = parser.parse_args()
return vars(args)
# colors = ['r', 'g', 'b', 'm', 'c', 'y', 'k', 'w']
def read_data(input_file, output_file, otu_only):
with open(input_file, 'r') as inp:
if not otu_only:
rows = [line.strip().split()[:-1] for line in inp.readlines() if len(line.strip().split()) > 3]
else:
rows = [line.strip().split()[:-1] for line in inp.readlines() if len(line.strip().split()) > 3 and len(
line.strip().split()[0].split('.')) == 8] # a feature with length 8 will have an OTU id associated with it
classes = list(set([v[2] for v in rows if len(v) > 2]))
if len(classes) < 1:
print "No differentially abundant features found in " + input_file
os.system("touch " + output_file)
sys.exit()
data = {}
data['rows'] = rows
data['cls'] = classes
return data
def plot_histo_hor(path, params, data, bcl, report_features):
cls2 = []
if params['all_feats'] != "":
cls2 = sorted(params['all_feats'].split(":"))
cls = sorted(data['cls'])
if bcl:
data['rows'].sort(key=lambda ab: fabs(float(ab[3])) * (cls.index(ab[2]) * 2 - 1))
else:
mmax = max([fabs(float(a)) for a in zip(*data['rows'])[3]])
data['rows'].sort(key=lambda ab: fabs(float(ab[3])) / mmax + (cls.index(ab[2]) + 1))
pos = arange(len(data['rows']))
head = 0.75
tail = 0.5
ht = head + tail
ints = max(len(pos) * 0.2, 1.5)
fig = plt.figure(figsize=(params['width'], ints + ht),
edgecolor=params['back_color'], facecolor=params['back_color'])
ax = fig.add_subplot(111, frame_on=False, axis_bgcolor=params['back_color'])
ls, rs = params['ls'], 1.0 - params['rs']
plt.subplots_adjust(left=ls, right=rs - 0.1, top=1 - head * (1.0 - ints / (ints + ht)) - 0.07,
bottom=tail * (1.0 - ints / (ints + ht)))
fig.canvas.set_window_title('LDA results')
l_align = {'horizontalalignment': 'left', 'verticalalignment': 'baseline'}
r_align = {'horizontalalignment': 'right', 'verticalalignment': 'baseline'}
added = []
m = 1 if data['rows'][0][2] == cls[0] else -1
out_data = defaultdict(list) # keep track of which OTUs result in the plot
for i, v in enumerate(data['rows']):
if report_features:
otu = v[0].split('.')[7].replace('_', '.') # string replace retains format New.ReferenceOTUxxx
score = v[3]
otu_class = v[2]
out_data[otu] = [score, otu_class]
indcl = cls.index(v[2])
lab = str(v[2]) if str(v[2]) not in added else ""
added.append(str(v[2]))
col = colors[indcl % len(colors)]
if len(cls2) > 0:
col = colors[cls2.index(v[2]) % len(colors)]
vv = fabs(float(v[3])) * (m * (indcl * 2 - 1)) if bcl else fabs(float(v[3]))
ax.barh(pos[i], vv, align='center', color=col, label=lab, height=0.8, edgecolor=params['fore_color'])
mv = max([abs(float(v[3])) for v in data['rows']])
if report_features:
print 'OTU\tLDA_score\tCLass'
for i in out_data:
print '%s\t%s\t%s' % (i, out_data[i][0], out_data[i][1])
for i, r in enumerate(data['rows']):
indcl = cls.index(data['rows'][i][2])
if params['n_scl'] < 0:
rr = r[0]
else:
rr = ".".join(r[0].split(".")[-params['n_scl']:])
if len(rr) > params['max_feature_len']:
rr = rr[:params['max_feature_len'] / 2 - 2] + " [..]" + rr[-params['max_feature_len'] / 2 + 2:]
if m * (indcl * 2 - 1) < 0 and bcl:
ax.text(mv / 40.0, float(i) - 0.3, rr, l_align,
size=params['feature_font_size'], color=params['fore_color'])
else:
ax.text(-mv / 40.0, float(i) - 0.3, rr, r_align,
size=params['feature_font_size'], color=params['fore_color'])
ax.set_title(params['title'], size=params['title_font_size'], y=1.0 + head *
(1.0 - ints / (ints + ht)) * 0.8, color=params['fore_color'])
ax.set_yticks([])
ax.set_xlabel("LDA SCORE (log 10)")
ax.xaxis.grid(True)
xlim = ax.get_xlim()
if params['autoscale']:
ran = arange(0.0001, round(round((abs(xlim[0]) + abs(xlim[1])) / 10, 4) * 100, 0) / 100)
if len(ran) > 1 and len(ran) < 100:
ax.set_xticks(arange(xlim[0], xlim[1] + 0.0001, min(xlim[1] + 0.0001,
round(round((abs(xlim[0]) + abs(xlim[1])) / 10, 4) * 100, 0) / 100)))
ax.set_ylim((pos[0] - 1, pos[-1] + 1))
leg = ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=5, borderaxespad=0.,
frameon=False, prop={'size': params['class_legend_font_size']})
def get_col_attr(x):
return hasattr(x, 'set_color') and not hasattr(x, 'set_facecolor')
for o in leg.findobj(get_col_attr):
o.set_color(params['fore_color'])
for o in ax.findobj(get_col_attr):
o.set_color(params['fore_color'])
plt.savefig(path, format=params['format'], facecolor=params['back_color'],
edgecolor=params['fore_color'], dpi=params['dpi'])
plt.close()
def plot_histo_ver(path, params, data, report_features):
cls = data['cls']
mmax = max([fabs(float(a)) for a in zip(*data['rows'])[1]])
data['rows'].sort(key=lambda ab: fabs(float(ab[3])) / mmax + (cls.index(ab[2]) + 1))
pos = arange(len(data['rows']))
if params['n_scl'] < 0:
nam = [d[0] for d in data['rows']]
else:
nam = [d[0].split(".")[-min(d[0].count("."), params['n_scl'])] for d in data['rows']]
fig = plt.figure(edgecolor=params['back_color'], facecolor=params['back_color'],
figsize=(params['width'], params['height']))
ax = fig.add_subplot(111, axis_bgcolor=params['back_color'])
plt.subplots_adjust(top=0.9, left=params['ls'], right=params['rs'], bottom=0.3)
fig.canvas.set_window_title('LDA results')
l_align = {'horizontalalignment': 'left', 'verticalalignment': 'baseline'}
r_align = {'horizontalalignment': 'right', 'verticalalignment': 'baseline'}
added = []
out_data = defaultdict(list) # keep track of which OTUs result in the plot
for i, v in enumerate(data['rows']):
if report_features:
otu = v[0].split('.')[7].replace('_', '.') # string replace retains format New.ReferenceOTUxxx
score = v[3]
otu_class = v[2]
out_data[otu] = [score, otu_class]
indcl = data['cls'].index(v[2])
lab = str(v[2]) if str(v[2]) not in added else ""
added.append(str(v[2]))
col = colors[indcl % len(colors)]
vv = fabs(float(v[3]))
ax.bar(pos[i], vv, align='center', color=col, label=lab)
if report_features:
print 'OTU\tLDA_score\tCLass'
for i in out_data:
print '%s\t%s\t%s' % (i, out_data[i][0], out_data[i][1])
xticks(pos, nam, rotation=-20, ha='left', size=params['feature_font_size'])
ax.set_title(params['title'], size=params['title_font_size'])
ax.set_ylabel("LDA SCORE (log 10)")
ax.yaxis.grid(True)
a, b = ax.get_xlim()
dx = float(len(pos)) / float((b - a))
ax.set_xlim((0 - dx, max(pos) + dx))
plt.savefig(path, format=params['format'], facecolor=params['back_color'],
edgecolor=params['fore_color'], dpi=params['dpi'])
plt.close()
if __name__ == '__main__':
params = read_params(sys.argv)
if params['category'] and params['map']:
category_colors = get_lefse_colors(params['category'], params['map'], params['input_file'])
else:
category_colors = False
colors = get_lefse_colors(params['category'], params['map'], params['input_file'],
colors=params['colors']) if params['colors'] else False
colors = colors or category_colors or ['r', 'g', 'b', 'm', 'c', [1.0, 0.5, 0.0],
[0.0, 1.0, 0.0], [0.33, 0.125, 0.0], [0.75, 0.75, 0.75], 'k']
params['fore_color'] = 'w' if params['back_color'] == 'k' else 'k'
data = read_data(params['input_file'], params['output_file'], params['otu_only'])
if params['orientation'] == 'v':
plot_histo_ver(params['output_file'], params, data, params['report_features'])
else:
plot_histo_hor(params['output_file'], params, data, len(data['cls']) == 2, params['report_features'])