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riboflask_diff.py
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riboflask_diff.py
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import sys
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
from matplotlib import gridspec
from matplotlib.font_manager import FontProperties
import os
import re
import subprocess
import shelve
import mpld4
from mpld4 import plugins,utils
import collections
from mpld4.utils import get_id
import pandas as pd
from math import log
from bokeh.plotting import figure, show, output_file
from bokeh.embed import file_html, components
from bokeh.resources import CDN
from bokeh.palettes import YlOrRd9 as palette
from bokeh.layouts import column
from bokeh.models.widgets import TextInput
from scipy.stats.stats import spearmanr,pearsonr
from bokeh.io import show
from bokeh.models import (
TapTool,
CustomJS,
OpenURL,
Label,
LogTicker,
ColumnDataSource,
HoverTool,
LinearColorMapper,
LogColorMapper,
BasicTicker,
PrintfTickFormatter,
ColorBar
)
#import seaborn
#seaborn.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})
#seaborn.set_style("whitegrid", {'axes.grid' : False})
#Defaults
#lite = "y"
#min_read = 25
#max_read = 35
#ribo = "a"
#rna = "a"
#subcodon = "d"
#tran = ""
#ambig = "u"
#user_ribo_files = []
#user_rna_files = []
#offset_dict = {}
# Define some CSS to control our custom labels
point_tooltip_css = """
table
{
border-collapse: collapse;
}
th
{
color: #000000;
background-color: #d2d4d8;
}
td
{
background-color: #ffffff;
}
table, th, td
{
font-family:Arial, Helvetica, sans-serif;
border: 0px solid black;
text-align: left;
}
"""
def merge_dict(dict1,dict2):
master_dict = dict1
for key in dict2:
if key in master_dict:
master_dict[key] += dict2[key]
else:
master_dict[key] = dict2[key]
return master_dict
def set_axis_color(axis, color, alpha=None):
"""Sets the spine color of all sides of an axis (top, right, bottom, left)."""
for side in ('top', 'right', 'bottom', 'left'):
spine = axis.spines[side]
spine.set_color(color)
if alpha is not None:
spine.set_alpha(alpha)
def get_color_palette(scheme):
"""Return colors for a given scheme. Default colors are returned for an item
if undefined in scheme.
"""
color_schemes = {
'default': {
'frames': ['#FF4A45', '#64FC44', '#5687F9'], 'background': '#ffffff',
'color': '#616161', 'ticks': '#757575', 'start': '#ffffff', 'stop': '#909090',
'rna': '#BFBFBF', 'axis': '#e0e0e0', 'grey': '#bdbdbd'
},
'colorbrewer': {
'frames': ['#fc8d62', '#66c2a5', '#8da0cb']
},
'rgb': {
'frames': ['red', 'green', 'blue']
},
'greyorfs': {}
}
colors = {}
for k, v in color_schemes['default'].items():
try:
vals = color_schemes[scheme][k]
except KeyError:
vals = v
colors[k] = vals
return colors
def get_near_cog_starts(seq):
near_cog_starts = {0:[],1:[],2:[]}
for i in range(0,len(seq)):
codon = seq[i:i+3]
if codon == "CTG":
near_cog_starts[(i+1)%3].append(i+1)
return near_cog_starts
def generate_plot(sorted_min_exp_list,bin_list,organism, label,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_color, short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list):
#Convert gene_list from string to list
#print "sorted min exp list", sorted_min_exp_list
print "bin list", bin_list
if gene_list != "":
gene_list = gene_list.replace(","," ").replace("\t"," ")
split_list = gene_list.split(" ")
gene_list = []
for item in split_list:
gene_list.append(item.strip(" ").upper())
posallxvals = []
posallyvals = []
posalllabels = []
posallgenes = []
poszscores = []
negallxvals = []
negallyvals = []
negalllabels = []
negallgenes = []
negzscores = []
nonde_xvals = []
nonde_yvals = []
nonde_labels = []
nonde_allgenes = []
nonde_zscores = []
#print "bin list", bin_list
upper_thresholds_y = [bin_list[0][2]]
upper_thresholds_x = [(sorted_min_exp_list[0][1])*0.99]
lower_thresholds_y = [bin_list[0][3]]
lower_thresholds_x = [(sorted_min_exp_list[0][1])*0.99]
# Easiest way to plot threshold lines is to just plot the threshold and min_exp for each bin, but this will result in slanted lines, i.ie if my first bin threshold is 8 and my second is 4
# there would be a slanted line between 8 and 4, but this makes no sense as the threshold is the same across an entire bin, so instead we plot two y values at every step (except first and last)
#d((transcript, log(min_exp,2), fold_change,gene))
cur_count = 0
bin_count = 0
for i in range(0,len(sorted_min_exp_list)):
cur_count+=1
if cur_count == 300:
#print "bin count {} threshold {} standard deviation {} mean {}".format(bin_count, bin_list[bin_count][2], bin_list[bin_count][1],bin_list[bin_count][0])
#To x we add the log2(min exp) of the 300th (or multiple of) item in min exp list
bin_count += 1
try:
tst = bin_list[bin_count]
except:
bin_count -= 1
cur_count = 0
upper_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
upper_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
upper_thresholds_y.append(bin_list[bin_count-1][2])
upper_thresholds_y.append(bin_list[bin_count][2])
lower_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
lower_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
lower_thresholds_y.append(bin_list[bin_count-1][3])
lower_thresholds_y.append(bin_list[bin_count][3])
#print i
#print "smel", sorted_min_exp_list[i]
#print "bin count", bin_list[bin_count]
z_score = (sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1])
#print bin_list[bin_count], sorted_min_exp_list[i][1], z_score
#print bin_list[bin_count][2]
#print "count, mean, std dev, z_score",sorted_min_exp_list[i][1], bin_list[bin_count][0], bin_list[bin_count][1], z_score
if gene_list == "":
if sorted_min_exp_list[i][2] <= bin_list[bin_count][2] and sorted_min_exp_list[i][2] >= bin_list[bin_count][3]:
nonde_xvals.append(sorted_min_exp_list[i][1])
nonde_yvals.append(sorted_min_exp_list[i][2])
nonde_labels.append(sorted_min_exp_list[i][0])
nonde_allgenes.append(sorted_min_exp_list[i][3])
nonde_zscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][2] > bin_list[bin_count][2]:
posallxvals.append(sorted_min_exp_list[i][1])
posallyvals.append(sorted_min_exp_list[i][2])
posalllabels.append(sorted_min_exp_list[i][0])
posallgenes.append(sorted_min_exp_list[i][3])
poszscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][2] < (bin_list[bin_count][3]):
negallxvals.append(sorted_min_exp_list[i][1])
negallyvals.append(sorted_min_exp_list[i][2])
negalllabels.append(sorted_min_exp_list[i][0])
negallgenes.append(sorted_min_exp_list[i][3])
negzscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
else:
#If the user passes a gene list only highlight those genes red/green depending on if fold changes is >/< 0
if sorted_min_exp_list[i][3] not in gene_list:
nonde_xvals.append(sorted_min_exp_list[i][1])
nonde_yvals.append(sorted_min_exp_list[i][2])
nonde_labels.append(sorted_min_exp_list[i][0])
nonde_allgenes.append(sorted_min_exp_list[i][3])
nonde_zscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][3] in gene_list and sorted_min_exp_list[i][2] > 0:
posallxvals.append(sorted_min_exp_list[i][1])
posallyvals.append(sorted_min_exp_list[i][2])
posalllabels.append(sorted_min_exp_list[i][0])
posallgenes.append(sorted_min_exp_list[i][3])
poszscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][3] in gene_list and sorted_min_exp_list[i][2] < 0:
negallxvals.append(sorted_min_exp_list[i][1])
negallyvals.append(sorted_min_exp_list[i][2])
negalllabels.append(sorted_min_exp_list[i][0])
negallgenes.append(sorted_min_exp_list[i][3])
negzscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
#print upper_thresholds_x,upper_thresholds_y
# Add z score thresholds for the last bin, which wouldn't have been covered in the above loop
upper_thresholds_x.append((sorted_min_exp_list[-1][1])*0.99)
upper_thresholds_y.append(bin_list[bin_count][2])
lower_thresholds_x.append((sorted_min_exp_list[-1][1])*0.99)
lower_thresholds_y.append(bin_list[bin_count][3])
full_title = "Differential translation ({})".format(short_code)
if no_groups == 1:
x_lab = 'Geometric mean (log2)'
y_lab = 'Fold change (log2)'
else:
x_lab = 'Average Geometric mean (log2)'
y_lab = 'Average Fold change (log2)'
p = figure(plot_width=1800, plot_height=750,x_axis_label=x_lab, y_axis_label=y_lab,title= full_title,toolbar_location="below",
tools = "reset,pan,box_zoom,hover,tap",logo=None)
p.title.align="center"
p.title.text_font_size = title_size
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = marker_size
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = marker_size
p.background_fill_color = background_color
p.xgrid.grid_line_color = "white"
p.ygrid.grid_line_color = "white"
if gene_list == "":
p.line(upper_thresholds_x, upper_thresholds_y, color="yellow",line_width=3)
p.line(lower_thresholds_x, lower_thresholds_y, color="yellow",line_width=3)
source = ColumnDataSource({'x': nonde_xvals,'y':nonde_yvals,'labels':nonde_labels, 'genes':nonde_allgenes})
sct = p.scatter('x','y',source=source, alpha=1,color="grey",size=5)
#source = ColumnDataSource({'x': posallxvals,'y':posallyvals,'labels':posalllabels, 'genes':posallgenes,"zscores":poszscores})
source = ColumnDataSource({'x': posallxvals,'y':posallyvals,'labels':posalllabels, 'genes':posallgenes})
p.scatter('x','y',source=source, alpha=1,color="green",size=10)
hover = p.select(dict(type=HoverTool))
#hover.tooltips = [("Fold change", "@y"),("Geometric mean","@x"),("Transcript","@labels"),("Genes","@genes"), ("Z score","@zscores")]
hover.tooltips = [("Fold change", "@y"),("Geometric mean","@x"),("Transcript","@labels"),("Genes","@genes")]
#source = ColumnDataSource({'x': negallxvals,'y':negallyvals,'labels':negalllabels, 'genes':negallgenes,"zscores":negzscores})
source = ColumnDataSource({'x': negallxvals,'y':negallyvals,'labels':negalllabels, 'genes':negallgenes})
p.scatter('x','y',source=source, alpha=1,color="red",size=10)
#source = ColumnDataSource({'x': nonde_xvals,'y':nonde_yvals,'labels':nonde_labels, 'genes':nonde_allgenes,"zscores":nonde_zscores})
output_file("scatter10k.html", title="Differential translation")
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,%23ff1f00_228,%233BFF00_231,%23ffffff_232,%23000000_&transcript=YLR162W&normalize=F&cov=T&ambig=F&minread=25&maxread=100
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,228,229,%23ff1f00_230,%233BFF00&transcript=YDR003W&normalize=T&cov=T&ambig=T&minread=18&maxread=45
#url = "http://143.239.109.139/tripsviz/{}/{}/interactive_plot/?transcript=@labels".format(organism, transcriptome)
file_string = ""
label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
if riboseq1:
if riboseq1[0] != "":
for file_id in riboseq1:
file_string += ("{},".format(file_id))
file_string += ("%23007a02_")
if riboseq2:
if riboseq2[0] != "":
for file_id in riboseq2:
file_string += ("{},".format(file_id))
file_string += ("%23960000_")
if rnaseq1:
if rnaseq1[0] != "":
print "\n\n\n\n\nsuccess", rnaseq1
for file_id in rnaseq1:
file_string += ("{},".format(file_id))
file_string += ("%2374ed76_")
if rnaseq2:
if rnaseq2[0] != "":
print "\n\n\n\n\nsuccess", rnaseq2
for file_id in rnaseq2:
file_string += ("{},".format(file_id))
file_string += ("%23ff6d6d_")
# remove the trailing _ in file_string if it's been populated
if file_string:
file_string = file_string[:len(file_string)-1]
if ambiguous == True:
ambig = "T"
else:
ambig = "F"
url = "http://trips.ucc.ie/{}/{}/comparison/?files={}{}&transcript=@labels&normalize={}&cov=T&ambig={}&minread=25&maxread=150".format(organism, transcriptome,file_string,label_string,str(normalized)[0],ambig)
'''
hili_gene = CustomJS(args=dict(sct=sct,source=source), code="""
console.log("Called hili gene");
var f = cb_obj['value']
var x = source["genes"][f]
console.log("x is" + x);
console.log("F is "+f);
sct.glyph.fill_color = '#4286f4';
""")
'''
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
#text_input = TextInput(value="B2M",title="Gene",callback=hili_gene)
#TODO FIX HARDCODED TMP FILE LINK
graph = "<div style='padding-left: 55px;padding-top: 22px;'><a href='https://trips.ucc.ie/short/{0}' target='_blank' ><button class='button centerbutton' type='submit'><b>Direct link to this plot</b></button></a><br><a href='https://trips.ucc.ie/static/tmp/{1}' target='_blank' ><button class='button centerbutton' type='submit'><b>Download results as csv file</b></button></a> </div>".format(short_code,filename)
#graph = "<div style='padding-left: 55px;padding-top: 22px;'><a href='https://trips.ucc.ie/short/{0}' target='_blank' ><button class='button centerbutton' type='submit'><b>Direct link to this plot</b></button></a><br> </div>".format(short_code)
#layout = column(text_input, p)
graph += file_html(p,CDN)
return graph
def ribo_vs_rna(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label):
#Convert gene_list from string to list
if gene_list != "":
gene_list = gene_list.replace(","," ").replace("\t"," ")
split_list = gene_list.split(" ")
gene_list = []
for item in split_list:
gene_list.append(item.strip(" ").upper())
x_values = []
y_values = []
genes = []
trans = []
hili_x_values = []
hili_y_values = []
hili_genes = []
hili_trans = []
for gene in ribo_rna_dict:
if label == "TE":
if gene not in gene_list:
y_values.append(log(ribo_rna_dict[gene]["ribo2"]/ribo_rna_dict[gene]["ribo1"],2))
x_values.append(log(ribo_rna_dict[gene]["rna2"]/ribo_rna_dict[gene]["rna1"],2))
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
hili_y_values.append(log(ribo_rna_dict[gene]["ribo2"]/ribo_rna_dict[gene]["ribo1"],2))
hili_x_values.append(log(ribo_rna_dict[gene]["rna2"]/ribo_rna_dict[gene]["rna1"],2))
hili_genes.append(gene)
hili_trans.append(ribo_rna_dict[gene]["tran"])
elif label == "Riboseq":
if gene not in gene_list:
y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
hili_y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
hili_x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
hili_genes.append(gene)
hili_trans.append(ribo_rna_dict[gene]["tran"])
elif label == "Rnaseq":
if gene not in gene_list:
y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
hili_y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
hili_x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
hili_genes.append(gene)
hili_trans.append(ribo_rna_dict[gene]["tran"])
source = ColumnDataSource({'x': x_values,'y':y_values,'trans':trans, 'genes':genes})
if label == "TE":
p = figure(plot_width=1800, plot_height=1800,x_axis_label="RNA-Seq FC (log2)", y_axis_label='Ribo-Seq FC (log2)',title="Ribo-Seq FC vs RNA-Seq FC ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Riboseq":
p = figure(plot_width=1800, plot_height=1800,x_axis_label="Ribo-Seq Cond2 count (log2)", y_axis_label='Ribo-Seq Cond1 count (log2)',title="Ribo-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Rnaseq":
p = figure(plot_width=1800, plot_height=1800,x_axis_label="RNA-Seq Cond2 count (log2)", y_axis_label='RNA-Seq Cond1 count (log2)',title="RNA-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
p.title.align="center"
p.title.text_font_size = title_size
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = marker_size
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = marker_size
p.background_fill_color = background_col
p.xgrid.grid_line_color = "#cccccc"
p.ygrid.grid_line_color = "#cccccc"
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='grey')
source = ColumnDataSource({'x':hili_x_values, 'y':hili_y_values, 'trans':hili_trans,'genes':hili_genes})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#4286f4')
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
if label == "TE":
p.line([-8,8], [-8,8], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo fc", "@y"),("RNA fc","@x"),("Genes","@genes"),("Transcript","@trans")]
elif label == "Riboseq":
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans")]
#corr = spearmanr(x_values, y_values)
pearson_corr = pearsonr(x_values, y_values)
mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
p.add_layout(mytext)
else:
hover.tooltips = [("Rna-seq Cond 1 count (log2)", "@y"),("Rna-seq Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans")]
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans")]
#corr = spearmanr(x_values, y_values)
pearson_corr = pearsonr(x_values, y_values)
mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
p.add_layout(mytext)
file_string = ""
label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
if riboseq1:
if riboseq1[0] != "":
for file_id in riboseq1:
file_string += ("{},".format(file_id))
file_string += ("%23007a02_")
if riboseq2:
if riboseq2[0] != "":
for file_id in riboseq2:
file_string += ("{},".format(file_id))
file_string += ("%23960000_")
if rnaseq1:
if rnaseq1[0] != "":
for file_id in rnaseq1:
file_string += ("{},".format(file_id))
file_string += ("%2374ed76_")
if rnaseq2:
if rnaseq2[0] != "":
for file_id in rnaseq2:
file_string += ("{},".format(file_id))
file_string += ("%23ff6d6d_")
# remove the trailing _ in file_string if it's been populated
if file_string:
file_string = file_string[:len(file_string)-1]
if ambiguous == True:
ambig = "T"
else:
ambig = "F"
url = "http://trips.ucc.ie/{}/{}/comparison/?files={}{}&transcript=@trans&normalize={}&cov=T&ambig={}&minread=25&maxread=150".format(organism, transcriptome,file_string,label_string,str(normalized)[0],ambig)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
graph = "<div style='padding-left: 55px;padding-top: 22px;'><a href='https://trips.ucc.ie/short/{0}' target='_blank' ><button class='button centerbutton' type='submit'><b>Direct link to this plot</b></button></a><br><a href='https://trips.ucc.ie/static/tmp/{1}' target='_blank' ><button class='button centerbutton' type='submit'><b>Download results as csv file</b></button></a> </div>".format(short_code,filename)
#graph = "<div style='padding-left: 55px;padding-top: 22px;'><a href='https://trips.ucc.ie/short/{0}' target='_blank' ><button class='button centerbutton' type='submit'><b>Direct link to this plot</b></button></a><br> </div>".format(short_code)
#layout = column(text_input, p)
graph += file_html(p,CDN)
return graph