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ribo_plot.py
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ribo_plot.py
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'''
Script to plot ribosome profiling analysis
Copyright (C) 2019 Fuad Mohammad, [email protected]
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.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
import pandas as pd
import numpy as np
import math
from sklearn.linear_model import LinearRegression
import itertools
import os, time
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import plot
import cPickle as pickle
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import display
from scipy import stats
import ribo_util
def size_dist(inputs, paths_in, paths_out):
files = inputs['files']
path_figure = paths_out['path_figures']
plot_num = 0
sns.set_style("white")
plt.figure(figsize=(5,3))
naming = ''
for fname in files:
naming += fname + '_'
path_analysis = paths_out['path_analysis'] + fname + '/readQC/'
data = ribo_util.unPickle(path_analysis + 'read_distribution')
df = ribo_util.dict_to_df(data, 'Length', 'fraction of total')
plt.plot(df, label = fname)
plt.title('Size Distribution')
plt.xlabel("Read Length")
plt.ylabel("Percent of Reads")
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
path_comparison = path_figure + 'Comparison/size_dist/'
if not os.path.exists(path_comparison):
os.makedirs(path_comparison)
plt.savefig(path_comparison + 'sizedist_'+naming+'.pdf', dpi=400, bbox_inches="tight")
plt.show()
for fname in files:
sns.set_style("white")
plt.figure(figsize=(5,3))
path_analysis = paths_out['path_analysis'] + fname + '/readQC/'
data = ribo_util.unPickle(path_analysis + 'read_distribution')
df = ribo_util.dict_to_df(data, 'Length', 'fraction of total')
plt.plot(df, label = fname)
plt.title('Size Distribution')
plt.xlabel("Read Length")
plt.ylabel("Percent of Reads")
plt.legend(loc='upper right')
plt.savefig(path_figure + fname + '/sizedist.pdf', dpi=400, bbox_inches="tight")
size_plot_csv = pd.DataFrame(df)
size_plot_csv.to_csv(path_analysis + 'size_plot_values.csv')
plt.gcf().clear()
def read_comp(inputs, paths_in, paths_out):
files = inputs['files']
path_figure = paths_out['path_figures']
plot_num = 0
for fname in files:
plot_num = 0
plt.figure(figsize=(20,2.5))
for nucleotide in ['G', 'C', 'A', 'U']:
plot_num += 1
path_analysis = paths_out['path_analysis'] + fname + '/readQC/'
data = ribo_util.unPickle(path_analysis + 'comp_' + nucleotide)
df = ribo_util.heatmapdict_to_df(data, 'Length', 'Position', 'composition')
plt.subplot(1,4,plot_num)
plot = sns.heatmap(df, cmap = "RdBu_r", vmin = 0, vmax = 50)
plt.setp(plot.get_xticklabels(), visible=False)
plt.setp(plot.get_xticklabels()[0::5], visible=True)
plt.setp(plot.get_yticklabels(), visible=False)
plt.setp(plot.get_yticklabels()[0::4], visible=True)
plt.title(fname + ' ' + nucleotide)
plt.savefig(path_figure + fname + '/read_composition.pdf', dpi=400, bbox_inches="tight")
plt.show()
def read_comp_context(inputs, paths_in, paths_out):
files = inputs['files']
path_figure = paths_out['path_figures']
plot_num = 0
for fname in files:
plot_num = 0
plt.figure(figsize=(20,2.5))
for nucleotide in ['G', 'C', 'A', 'U']:
plot_num += 1
path_analysis = paths_out['path_analysis'] + fname + '/reads_position/'
data = ribo_util.unPickle(path_analysis + 'comp_genome_' + nucleotide)
df = ribo_util.heatmapdict_to_df(data, 'Length', 'Position', 'composition')
plt.subplot(1,4,plot_num)
plot = sns.heatmap(df, cmap = "RdBu_r", vmin = 0, vmax = 50)
#plot.set_xlim([0,10])
plt.setp(plot.get_xticklabels(), visible=False)
plt.setp(plot.get_xticklabels()[0::5], visible=True)
plt.setp(plot.get_yticklabels(), visible=False)
plt.setp(plot.get_yticklabels()[0::4], visible=True)
plt.title(fname + ' ' + nucleotide)
plt.savefig(path_figure + fname + '/read_comp_context.pdf', dpi=400, bbox_inches="tight")
plt.show()
'''def read_comp_gff(inputs, paths_in, paths_out):
files = inputs['files']
plot_num = 0
for fname in files:
plot_num = 0
plt.figure(figsize=(20,2.5))
for nucleotide in ['G', 'C', 'A', 'U']:
plot_num += 1
path_analysis = paths_out['path_analysis'] + fname + '/'
data = ribo_util.unPickle(path_analysis + 'comp_genome_' + nucleotide)
df = ribo_util.heatmapdict_to_df(data, 'Length', 'Position', 'composition')
plt.subplot(1,4,plot_num)
plot = sns.heatmap(df, cmap = "RdBu_r", vmin = 0, vmax = 50)
plt.setp(plot.get_xticklabels(), visible=False)
plt.setp(plot.get_xticklabels()[0::5], visible=True)
plt.setp(plot.get_yticklabels(), visible=False)
plt.setp(plot.get_yticklabels()[0::4], visible=True)
plt.title(fname + ' ' + nucleotide)
plt.show()'''
def plot_alignment_allocation(inputs, paths_in, paths_out):
files = inputs['files']
analysis_path = paths_out['path_analysis_log']
path_figure = paths_out['path_figures']
data = {}
fnames = []
samples = 0
samples_list = []
for fname in files:
fnames.append(fname)
samples_list.append(samples)
samples += 1
analysis_log = analysis_path + fname + '/' + fname
analysis_log = ribo_util.unPickle(analysis_log)
raw_data = analysis_log['ribo_density']['analysis_breakdown']
total_reads = raw_data.pop('Total Reads', None)
#total_reads = raw_data.pop('Reads Filtered', None)
for key in raw_data.keys():
if not key in data:
data[key] = []
value = raw_data[key]
data[key].append(value)
legend = raw_data.keys()
legend = sorted(legend, key=str.lower)
colors = ['#b2182b','#fddbc7','#e0e0e0','#bababa','#878787','#4d4d4d']
bottom = [0]*samples
sns.set_style("white")
plt.figure(figsize=(.8 * samples,4))
for key in legend:
i = legend.index(key)
color = colors[i]
plt.bar(samples_list, data[key], bottom = bottom, color = color, edgecolor='white', width=.6)
bottom = [x + y for x, y in zip(bottom, data[key])]
plt.xticks(samples_list, fnames, fontweight='bold')
plt.title('Read Mapping')
plt.xlabel("Sample")
plt.ylabel("Reads")
plt.legend(legend, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig(path_figure + fname + '/read_allocation.pdf', dpi=400, bbox_inches="tight")
plt.show()
def plot_avggene(inputs, paths_in, paths_out, settings, settings_plot):
files = inputs['files']
shift = settings_plot['shift']
hmmax = settings_plot['HM_max']
ymax = settings_plot['ymax']
path_figure = paths_out['path_figures']
minlength = settings['minlength']
maxlength = settings['maxlength']
length_in_ORF = settings['length_in_ORF']
length_out_ORF = settings['length_out_ORF']
density_type = settings['density_type']
next_gene = settings['next_gene']
equal_weight = settings['equal_weight']
threshold = settings['threshold']
minlength_1 = str(minlength) +'_'
maxlength_1 = str(maxlength) +'_'
length_in_ORF_1 = str(length_in_ORF) +'_'
length_out_ORF_1 = str(length_out_ORF)+'_'
density_type_1 = density_type +'_'
next_gene_1 = str(next_gene) +'_'
equal_weight_1 = equal_weight +'_'
threshold_1 = str(threshold) +'_'
name_settings = length_in_ORF_1+length_out_ORF_1+next_gene_1+threshold_1
name_settings += density_type_1+equal_weight_1+minlength_1+maxlength_1
for fname in files:
plot_num = 0
sns.set_style("white")
sns.set_style("ticks")
plt.figure(figsize=(20,5))
path_analysis = paths_out['path_analysis'] + fname + '/avggenes/'
data_start = ribo_util.unPickle(path_analysis + 'avg_start_'+ name_settings + '_all')
data_stop = ribo_util.unPickle(path_analysis + 'avg_stop_' + name_settings + '_all')
data_startHM = ribo_util.unPickle(path_analysis + 'avg_start_'+ name_settings + '_HM')
data_stopHM = ribo_util.unPickle(path_analysis + 'avg_stop_' + name_settings + '_HM')
xmax = len(data_start.keys())
data_start = ribo_util.dict_to_df(data_start, 'Position', 'Reads')
data_stop = ribo_util.dict_to_df(data_stop, 'Position', 'Reads')
data_startHM = ribo_util.heatmapdict_to_df(data_startHM, 'Length', 'Position', 'composition')
data_stopHM = ribo_util.heatmapdict_to_df(data_stopHM, 'Length', 'Position', 'composition')
X = range(-50, 151)
data_startHM = data_startHM.reindex(index=data_startHM.index[::-1])
data_stopHM = data_stopHM.reindex(index=data_stopHM.index[::-1])
data_start.to_csv(path_analysis + 'start_all.csv')
data_stop.to_csv(path_analysis + 'stop_all.csv')
max_start = data_start["Reads"].max()
max_stop = data_stop["Reads"].max()
if ymax == 0:
if max_start > max_stop:
ymax = max_start
else:
ymax = max_stop
for graph in ['Start', 'Stop']:
plot_num += 1
if graph == 'Start':
data = data_start
elif graph == 'Stop':
data = data_stop
plt.subplot(2,2,plot_num)
plt.plot(data, sns.xkcd_rgb["dark grey"], )
plt.title(fname + ' ' + graph)
plt.ylabel("Reads")
plt.ylim(0, ymax)
plt.xlim(0, xmax)
sns.despine()
for graph in ['startHM', 'stopHM']:
plot_num += 1
if graph == 'startHM':
dataHM = data_startHM
elif graph == 'stopHM':
dataHM = data_stopHM
plt.subplot(2,2,plot_num)
plot = sns.heatmap(dataHM, cmap = "ocean_r", vmin = 0, vmax = hmmax, cbar=False)
plt.setp(plot.get_xticklabels(), visible=False)
plt.setp(plot.get_xticklabels()[0::10], visible=True)
plt.setp(plot.get_yticklabels(), visible=False)
plt.setp(plot.get_yticklabels()[0::4], visible=True)
plt.savefig(path_figure + fname + '/avggene_' + name_settings + '.pdf', dpi=400)
plt.show()
def plot_frame(inputs, paths_in, paths_out, settings, settings_plot):
files = inputs['files']
path_figure = paths_out['path_figures']
minlength = settings['minlength']
maxlength = settings['maxlength']
threshold = settings['threshold']
minlength_1 = str(minlength) +'_'
maxlength_1 = str(maxlength) +'_'
threshold_1 = str(threshold) +'_'
name_settings = minlength_1+maxlength_1+threshold_1
for fname in files:
sns.set_style("white")
plt.figure(figsize=(50,3))
path_analysis = paths_out['path_analysis'] + fname + '/frame/'
frame_alias = ribo_util.unPickle(path_analysis + name_settings + 'frame_alias')
alias_F = []
alias_V = []
alias_A = []
alias_R = []
for alias in frame_alias.keys():
for frame in range(0, 3):
alias_F.append(frame)
alias_V.append(frame_alias[alias][frame])
alias_A.append(alias)
alias_df = pd.DataFrame({'Frame': alias_F, 'Fraction': alias_V, 'Alias': alias_A})
frame_length = ribo_util.unPickle(path_analysis + name_settings + 'frame_length')
length_F = []
length_V = []
length_L = []
for length in frame_length.keys():
for frame in range(0, 3):
length_F.append(frame)
length_V.append(frame_length[length][frame])
length_L.append(length)
length_df = pd.DataFrame({'Frame': length_F, 'Fraction': length_V, 'Length': length_L})
frame_genome = ribo_util.unPickle(path_analysis + name_settings + 'frame_genome')
x_genome = [0, 1, 2]
plt.subplot(1,5,1)
plot1 = sns.barplot(x = x_genome, y = frame_genome, color = ".2")
plt.ylabel("Fraction")
plt.xlabel("Frame")
total = float(len(frame_genome))
for p in plot1.patches:
height = p.get_height()
plot1.annotate("%.1f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-10),
ha='center', va='center', fontsize=10, color='.9', xytext=(0, 20),
textcoords='offset points')
plt.subplot(1,5,3)
plot2 = sns.stripplot(x = 'Length', y = 'Fraction', hue = 'Frame', data = length_df,
jitter=False, size=6, color="0", linewidth=0)
plot2.legend_.remove()
#plot2.axhline(y=45, xmin=0, xmax=1, dashes=[2, 1, 2, 1])
frame_0 = length_df.loc[length_df.Frame == 0]
x = frame_0.Length.values
y = frame_0.Fraction.values
plot2.plot(y, sns.xkcd_rgb["pale red"], marker = 'o' )
frame_1 = length_df.loc[length_df.Frame == 1]
x = frame_1.Length.values
y = frame_1.Fraction.values
plot2.plot(y, c = '0.3', marker = 'o' )
plt.ylim(0, 100)
plt.title(fname + ' Frame for each Readlength')
plt.subplot(1,5,5)
plot3 = sns.boxplot(x = 'Frame', y = 'Fraction', data = alias_df,
color="c")
plt.title(fname + ' Frame for each Alias')
plot3_1 = sns.stripplot(x = 'Frame', y = 'Fraction', data = alias_df,
jitter=True, size=3, color=".2", linewidth=0)
plt.savefig(path_figure + fname + '/frame_' + name_settings + '.pdf', dpi=400)
plt.show()
def plot_frame_comp(inputs, paths_in, paths_out, settings, settings_plot):
files = inputs['files']
minlength = settings['minlength']
maxlength = settings['maxlength']
threshold = settings['threshold']
minlength_1 = str(minlength) +'_'
maxlength_1 = str(maxlength) +'_'
threshold_1 = str(threshold) +'_'
name_settings = minlength_1+maxlength_1+threshold_1
framescore = []
for fname in files:
path_analysis = paths_out['path_analysis'] + fname + '/frame/'
frame_genome = ribo_util.unPickle(path_analysis + name_settings + 'frame_genome')
x_genome = [0, 1, 2]
frame_score = abs(frame_genome[0] - 40.3)
frame_score = frame_score / 59.7
framescore.append(frame_score)
print frame_score
print files, framescore
plt.figure(figsize=(30,5))
plot1 = sns.barplot(x = files, y = framescore, color = ".2")
plt.ylabel("Libraries")
plt.xlabel("Frame Score")
for p in plot1.patches:
height = p.get_height()
plot1.annotate("%.1f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-50),
ha='center', va='center', fontsize=10, color='0', xytext=(0, 20),
textcoords='offset points')
plt.show()
def plot_pausescore(inputs, paths_in, paths_out, settings, settings_plot):
files = inputs['files']
aa_codon = settings_plot['aa_or_codon']
aminoacids = settings_plot['amino_acid']
codons = settings_plot['codon']
ymax_dot = settings_plot['ymax_dot']
ymax_line = settings_plot['ymax_line']
vmax_HM = settings_plot['vmax_HM']
path_figure = paths_out['path_figures']
aa_plots = len(aminoacids)
codon_plots = len(codons)
aa_code, codon_code = ribo_util.get_genetic_code()
for fname in files:
minlength = settings['minlength']
maxlength = settings['maxlength']
plot_upstream = settings['plot_upstream'] / 3 * 3 #change window to interval of 3
plot_downstream = settings['plot_downstream'] / 3 * 3
start_trim = settings['start_trim'] / 3 * 3
stop_trim = settings['stop_trim'] / 3 * 3
frameshift = settings['frameshift']
A_site = settings['A_site shift']
minlength_1 = str(minlength) +'_'
maxlength_1 = str(maxlength) +'_'
plot_upstream_1 = str(plot_upstream) +'_'
plot_downstream_1 = str(plot_downstream)+'_'
start_trim_1 = str(start_trim) +'_'
stop_trim_1 = str(stop_trim) +'_'
frameshift_1 = str(frameshift) +'_'
a_site_1 = str(A_site) +'_'
name_settings = minlength_1+maxlength_1+plot_upstream_1+plot_downstream_1
name_settings += start_trim_1+stop_trim_1+frameshift_1
path_pausescore = paths_out['path_analysis'] + fname + '/pause_score/'
### For aa_analysis ###
if aa_codon == 'aa':
aa_score = ribo_util.unPickle(path_pausescore + 'aa_scores' + name_settings)
aa_HM = ribo_util.unPickle(path_pausescore + 'aa_HM_data'+ name_settings )
aa_plot = ribo_util.unPickle(path_pausescore + 'aa_plot_data'+ name_settings )
aa_df = pd.DataFrame(aa_score)
aa_df = aa_df.sort_values(by=['Amino Acid'])
sns.set_style("white")
sns.set_context("talk")
plt.figure(figsize=(8 + 4 * aa_plots,5))
plt.subplot2grid((2,2 + aa_plots), (0,0), rowspan=2, colspan=2)
plot = sns.stripplot(x="Amino Acid", y="A_site", data=aa_df, size = 13, color= 'black')
plot = sns.stripplot(x="Amino Acid", y="P_site", data=aa_df, size = 6, color = 'black')
plot = sns.stripplot(x="Amino Acid", y="E_site", data=aa_df, size = 6, color = 'grey')
#sns.despine(offset=5, trim = True )
plt.ylim(0, ymax_dot)
plot.axhline(y=1, xmin=0, xmax=1, dashes=[2, 2, 2, 2], color = 'grey')
plt.title(fname + ' Amino Acid Pause Scores')
plt.xlabel("Amino Acid")
plt.ylabel("Pause Score")
aa_HM_dict = {}
aa_plot_dict = {}
for aa in aminoacids:
df_HM = pd.DataFrame(aa_HM[aa])
df_plot = pd.DataFrame(aa_plot[aa])
aa_HM_dict[aa] = df_HM
aa_plot_dict[aa] = df_plot
xlim_lower = aa_plot_dict[aa].index[0]
xlim_upper = aa_plot_dict[aa].index[-1]
sns.set_style("white")
sns.set_style("ticks")
plotnum = 0
for aa in aminoacids:
plt.subplot2grid((2,2 + aa_plots), (0,2 + plotnum), rowspan=1, colspan=1)
plot_2 = plt.plot(aa_plot_dict[aa], sns.xkcd_rgb["dark grey"])
plt.title(aa + ' Average Plot')
plt.ylabel("Pause Score")
plt.ylim(0, ymax_line)
plt.xlim(xlim_lower, xlim_upper)
sns.despine(offset=5)
plt.subplot2grid((2,2 + aa_plots), (1,2 + plotnum), rowspan=1, colspan=1)
plot_3 = sns.heatmap(aa_HM_dict[aa], cmap = "ocean_r", vmin = 0, vmax = vmax_HM,
cbar=False, xticklabels=15, yticklabels=6)
sns.despine(offset=5)
plotnum +=1
plt.tight_layout()
plt.savefig(path_figure + fname + '/aa_pausescore' + name_settings + '_aa_pause_scores.pdf', dpi=400)
plt.show()
aa_plot_csv = pd.DataFrame(aa_plot)
aa_plot_csv.to_csv(path_pausescore + 'aa_plot_values.csv')
### For codon_analysis ###
if aa_codon == 'codon':
codon_score = ribo_util.unPickle(path_pausescore + 'codon_scores' + name_settings )
codon_HM = ribo_util.unPickle(path_pausescore + 'codon_HM_data' + name_settings )
codon_plot = ribo_util.unPickle(path_pausescore + 'codon_plot_data' + name_settings )
aa_list = []
for codon in codon_score['Codon']:
aa = codon_code[codon]
aa_list.append(aa)
codon_score['Amino_Acid'] = aa_list
codon_df = pd.DataFrame(codon_score)
codon_df = codon_df.sort_values(by=['Amino_Acid', 'Codon'])
sns.set_style("white")
sns.set_context("talk")
plt.figure(figsize=(28 + 4 * codon_plots,5))
plt.subplot2grid((2,7 + codon_plots), (0,0), rowspan=2, colspan=7)
plot = sns.stripplot(x="Codon", y="A_site", data=codon_df, size = 10, color = 'black')
plot = sns.stripplot(x="Codon", y="P_site", data=codon_df, size = 2, color = 'black')
plot = sns.stripplot(x="Codon", y="E_site", data=codon_df, size = 2, color = 'grey')
#sns.despine(offset=5, trim = True )
plt.ylim(0, ymax_dot)
plot.axhline(y=1, xmin=0, xmax=1, dashes=[2, 2, 2, 2], color = 'grey')
plot.axvline(x=3.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(1, 4,'A', fontsize=30) #add text
plot.axvline(x=5.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(4, 4,'C', fontsize=30) #add text
plot.axvline(x=7.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(6, 4,'D', fontsize=30) #add text
plot.axvline(x=9.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(8, 4,'E', fontsize=30) #add text
plot.axvline(x=11.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(10, 4,'F', fontsize=30) #add text
plot.axvline(x=15.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(13, 4,'G', fontsize=30) #add text
plot.axvline(x=17.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(16, 4,'H', fontsize=30) #add text
plot.axvline(x=20.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(18.8, 4,'I', fontsize=30) #add text
plot.axvline(x=22.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(21, 4,'K', fontsize=30) #add text
plot.axvline(x=28.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(25, 4,'L', fontsize=30) #add text
plot.axvline(x=29.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(28.6, 4,'M', fontsize=30) #add text
plot.axvline(x=31.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(30, 4,'N', fontsize=30) #add text
plot.axvline(x=35.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(33, 4,'P', fontsize=30) #add text
plot.axvline(x=37.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(36, 4,'Q', fontsize=30) #add text
plot.axvline(x=43.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(40, 4,'R', fontsize=30) #add text
plot.axvline(x=49.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(46, 4,'S', fontsize=30) #add text
plot.axvline(x=53.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(51, 4,'T', fontsize=30) #add text
plot.axvline(x=57.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(55, 4,'V', fontsize=30) #add text
plot.axvline(x=60.5, ymin=0, ymax=ymax_dot, color = 'grey', lw = .8)
plt.text(58.4, 4,'W', fontsize=30) #add text
plt.text(61.5, 4,'_', fontsize=30) #add text'''
plt.title(fname + ' Codon Pause Scores')
plt.xlabel("Codon")
plt.ylabel("Pause Score")
codon_HM_dict = {}
codon_plot_dict = {}
for codon in codons:
df_HM = pd.DataFrame(codon_HM[codon])
df_plot = pd.DataFrame(codon_plot[codon])
codon_HM_dict[codon] = df_HM
codon_plot_dict[codon] = df_plot
xlim_lower = codon_plot_dict[codon].index[0]
xlim_upper = codon_plot_dict[codon].index[-1]
sns.set_style("white")
sns.set_style("ticks")
plotnum = 0
for codon in codons:
plt.subplot2grid((2,7 + codon_plots), (0,7 + plotnum), rowspan=1, colspan=1)
plot_2 = plt.plot(codon_plot_dict[codon], sns.xkcd_rgb["dark grey"])
plt.title(codon + ' Average Plot')
plt.ylabel("Pause Score")
plt.ylim(0, ymax_line)
plt.xlim(xlim_lower, xlim_upper)
sns.despine(offset=5)
plt.subplot2grid((2,7 + codon_plots), (1,7 + plotnum), rowspan=1, colspan=1)
plot_3 = sns.heatmap(codon_HM_dict[codon], cmap = "ocean_r", vmin = 0, vmax = vmax_HM,
cbar=False, xticklabels=15, yticklabels=6)
sns.despine(offset=5)
plotnum +=1
plt.tight_layout()
plt.savefig(path_figure + fname + '/codon_pausescore' + name_settings + '.pdf', dpi=400)
plt.show()
def plot_asymmetry(inputs, paths_in, paths_out, settings):
files = inputs['files']
path_figure = paths_out['path_figures']
minlength = settings['minlength']
maxlength = settings['maxlength']
minlength_1 = str(minlength) +'_'
maxlength_1 = str(maxlength) +'_'
name_settings = minlength_1+maxlength_1
for fname in files:
path_analysis = paths_out['path_analysis'] + fname + '/'
asymmetry_dict = ribo_util.unPickle(path_analysis + 'asymmetry')
df = pd.DataFrame(asymmetry_dict)
#display(df)
sub = df.loc[df.Subgroup == "Subgroup"]
other = df.loc[df.Subgroup == "Other"]
plot2 = sns.jointplot(x="Score", y="GeneLength", data=df, size = 4,
color="black", marker='.', alpha=0, xlim = (-3, 3))
plot2.x = other.Score
plot2.y = other.GeneLength
plot2.plot_joint(plt.scatter, marker='.', c='.6')
plot2.plot_joint(sns.kdeplot, n_levels=6, cmap="Greys")
plot2.x = sub.Score
plot2.y = sub.GeneLength
plot2.plot_joint(plt.scatter, marker='.', c='orangered', alpha=.6)
plot2.plot_joint(sns.kdeplot, n_levels=4, cmap="Reds")
plt.savefig(path_figure + fname + '/asymmetry' + name_settings + '.pdf', dpi=400, bbox_inches="tight")
plt.show()
def plot_asymmetry_comp(inputs, paths_in, paths_out, settings):
#sns.boxplot(data=iris, orient="h", palette="Set2")
files = inputs['files']
path_figure = paths_out['path_figures'] + 'Comparison/'
minlength = settings['minlength']
maxlength = settings['maxlength']
minlength_1 = str(minlength) +'_'
maxlength_1 = str(maxlength) +'_'
name_settings = minlength_1+maxlength_1
data = {}
score_list = []
fname_list = []
plot_count = 0
fnames = ''
cm_treated = ['CW63', 'OL38', 'OL34', 'OL35', 'OL18', 'OL19', 'OL20', 'OL21', 'OL1', 'OL2']
cm_untreated = ['OL' + str(i) for i in range(0, 40)]
treated_mean = []
untreated_mean = []
for fname in files:
fnames += fname
plot_count += 1
path_analysis = paths_out['path_analysis'] + fname + '/'
asymmetry_dict = ribo_util.unPickle(path_analysis + 'asymmetry')
df = pd.DataFrame(asymmetry_dict)
compare_path = paths_out['path_analysis'] + 'OL17' + '/'
compare_dict = ribo_util.unPickle(compare_path + 'asymmetry')
compare_df = pd.DataFrame(compare_dict)
score = df['Score'].values.tolist()
compare_score = compare_df['Score'].values.tolist()
ttest = stats.ttest_ind(score, compare_score )
ttest2 = stats.ttest_1samp(score, 0 )
mean = np.mean(score)
if fname in cm_treated:
treated_mean.append(mean)
elif fname in cm_untreated:
untreated_mean.append(mean)
for value in score:
fname_list.append(fname)
score_list.append(value)
data['file'] = fname_list
data['score'] = score_list
data = pd.DataFrame(data)
sns.set_style("white")
plot = plt.figure(figsize=(3,12))
plot = sns.boxplot(x="score", y='file', data=data, orient="h", color = "grey", fliersize = 0)
plot.axvline(linewidth=2, color='r')
plot.set(xlim=(-3, 3))
sns.despine(offset=5)
plt.savefig(path_figure + 'asymmetry/' + fnames + name_settings + '.pdf', dpi=400, bbox_inches="tight")
plt.show()
plot2 = sns.distplot(treated_mean, bins=5)
plot2 = sns.distplot(untreated_mean, bins=5)
plt.show()
ttest = stats.ttest_ind(treated_mean, untreated_mean )
print ttest
print 'number of genes in treated = ' + str(len(treated_mean))
print 'number of genes in untreated = ' + str(len(untreated_mean))
print
print 'treated = ' , treated_mean
print
print 'untreated = ' , untreated_mean
def plot_genelist(fnamelist, paths_in, paths_out, settings):
'''Takes 2 files and compares gene RPKM values'''
path_figure = paths_out['path_figures'] + 'Comparison/'
'''Load Settings'''
file1 = fnamelist[0]
file2 = fnamelist[1]
foldchange = settings['foldchange']
highlight_genes = settings['interesting']
data_transform = settings['data_transform'] # has to be 'None', 'log10', or 'log2'
highlight_genes = settings['annotation']
'''Load Data'''
#genelist file from analysis
path_analysis1 = paths_out['path_analysis'] + fnamelist[0] + '/'
path_analysis2 = paths_out['path_analysis'] + fnamelist[1] + '/'
genelist_f1 = ribo_util.unPickle(path_analysis1 + 'genelist')
genelist_f2 = ribo_util.unPickle(path_analysis2 + 'genelist')
#define alias and RPKM as arrays
df1 = pd.DataFrame(genelist_f1)
alias = df1.Alias
RPKM_f1 = df1.RPKM
annotation_f1 = df1.Annotation
df2 = pd.DataFrame(genelist_f2)
RPKM_f2 = df2.RPKM
annotation_f2 = df2.Annotation
'''data arrays to plot'''
xydict = {}
x_list = []
y_list = []
folddict = {}
x_foldchange = []
y_foldchange = []
interestingdict = {}
x_interesting = []
y_interesting = []
annotateddict = {}
x_annotated = []
y_annotated = []
'''iterate through gene data for manipulation (log transform and fold change calc)'''
for alias, xval, yval, annotation in itertools.izip(alias.values, RPKM_f1.values, RPKM_f2.values, annotation_f1.values):
# remove genes with RPKM = 0
if xval == 0 or yval == 0:
continue
# calculate foldchange prior to data transformation
foldchange_val = (yval / xval)
# transform data
if data_transform == 'None':
xval = xval
yval = yval
if data_transform == 'log2':
xval = math.log(xval, 2)
yval = math.log(yval, 2)
if data_transform == 'log10':
xval = math.log10(xval)
yval = math.log10(yval)
# append data to lists
x_list.append(xval)
y_list.append(yval)
if foldchange_val > foldchange or foldchange_val < (1 / foldchange):
x_foldchange.append(xval)
y_foldchange.append(yval)
if alias in settings['interesting']:
x_interesting.append(xval)
y_interesting.append(yval)
print alias, xval, yval
if annotation == highlight_genes:
x_annotated.append(xval)
y_annotated.append(yval)
print alias, xval, yval
# convert lists to pd.dataframe
xydict[file1] = x_list
xydict[file2] = y_list
xy_df = pd.DataFrame(xydict)
folddict[file1] = x_foldchange
folddict[file2] = y_foldchange
fold_df = pd.DataFrame(folddict)
interestingdict[file1] = x_interesting
interestingdict[file2] = y_interesting
interesting_df = pd.DataFrame(interestingdict)
annotateddict[file1] = x_annotated
annotateddict[file2] = y_annotated
annotated_df = pd.DataFrame(annotateddict)
display(annotated_df)
sns.set_style("white")
plt.figure(figsize=(6,6))
plot1 = sns.regplot(x = file1, y = file2, data = xy_df,
color=".5", marker='.', fit_reg = False)
#plot1.set_axis_labels(fnamelist[0] + ' log2(RPKM)', fnamelist[1] + ' log2(RPKM)')
plot1.x = fold_df[file1]
plot1.y = fold_df[file2]
#plot1.plot_joint(plt.scatter, marker='.', c='r', alpha = .4)
x = interesting_df[file1]
y = interesting_df[file2]
plot1 = sns.regplot(x = x, y = y, color="blue", marker='.', fit_reg = False)
x = annotated_df[file1]
y = annotated_df[file2]
plot1 = sns.regplot(x = x, y = y, color="red", marker='.', fit_reg = False)
x = xy_df[file1]
y = xy_df[file1]
plot1 = sns.regplot(x = x, y = y, color="black", marker="None")
plot2 = sns.jointplot(x = file1, y = file2, data = xy_df,
color=".5", marker='.', kind="reg")
plot2 = plot2.annotate(stats.pearsonr)
plt.savefig(path_figure + file1 + '_' + file2 + '.pdf', dpi=400, bbox_inches="tight")
plt.show()
def plot_UTRvsCDS(inputs, paths_in, paths_out, settings, settings_plot):
files = inputs['files']
path_figure = paths_out['path_figures']
minlength = settings['minlength']
maxlength = settings['maxlength']
length_in_ORF = settings['length_in_ORF']
length_out_ORF = settings['length_out_ORF']
density_type = settings['density_type']
next_gene = settings['next_gene']
equal_weight = settings['equal_weight']