Figure. mCNV eQTL Effect Sizes



In [1]:

    
import copy
import cPickle
import os
import subprocess

import cdpybio as cpb
import matplotlib as mpl
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None  # default='warn'
import pybedtools as pbt
import scipy.stats as stats
import seaborn as sns

import ciepy
import cardipspy as cpy

%matplotlib inline
%load_ext rpy2.ipython

dy_name = 'figure_mcnv_eqtl_effect_sizes'
    
outdir = os.path.join(ciepy.root, 'output', dy_name)
cpy.makedir(outdir)

private_outdir = os.path.join(ciepy.root, 'private_output', dy_name)
cpy.makedir(private_outdir)

import socket
if socket.gethostname() == 'fl-hn1' or socket.gethostname() == 'fl-hn2':
    dy = os.path.join(ciepy.root, 'sandbox', 'tmp', dy_name)
    cpy.makedir(dy)
    pbt.set_tempdir(dy)

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In [2]:

    
sns.set_style('whitegrid')



In [3]:

    
fn = os.path.join(ciepy.root, 'output', 'mcnv_analysis', 'sig.tsv')
sig = pd.read_table(fn, index_col=0)



In [4]:

    
tdf = sig.sort_values(by=['overlap_gene_cons', 'pvalue']).drop_duplicates(subset='gene')



In [7]:

    
fig = plt.figure(figsize=(6.85, 2.25), dpi=300)

gs = gridspec.GridSpec(1, 1)
ax = fig.add_subplot(gs[0, 0])
ax.text(0, 1, 'Figure S6',
        size=16, va='top')
ciepy.clean_axis(ax)
ax.set_xticks([])
ax.set_yticks([])
gs.tight_layout(fig, rect=[0, 0.85, 0.5, 1])

gs = gridspec.GridSpec(1, 2)

# Genic, lead CNV
ax = fig.add_subplot(gs[0, 0])
ax.set_ylabel('Number of genes', fontsize=8)
ax.set_xlabel('$\\beta$', fontsize=8)
tdf[tdf.overlap_gene_cons].beta.hist(bins=np.arange(-1, 1.1, 0.1), ax=ax)
ax.grid(axis='x')
for t in ax.get_xticklabels() + ax.get_yticklabels():
    t.set_fontsize(8)
print('{:,} genic mCNV eGenes.'.format(tdf[tdf.overlap_gene_cons].shape[0]))
p = stats.binom_test((tdf[tdf.overlap_gene_cons].beta > 0).value_counts())
print('Effect sizes for genic lead mCNVs are biased '
      '(p={:.3e}, binomial test).'.format(p))

# Intergenic, lead CNV
ax = fig.add_subplot(gs[0, 1])
tdf[tdf.overlap_gene_cons == False].beta.hist(bins=np.arange(-1, 1.1, 0.1), ax=ax)
ax.set_ylabel('Number of genes', fontsize=8)
ax.set_xlabel('$\\beta$', fontsize=8)
ax.grid(axis='x')
for t in ax.get_xticklabels() + ax.get_yticklabels():
    t.set_fontsize(8)
p = stats.binom_test((tdf[tdf.overlap_gene_cons == False].beta > 0).value_counts())
print('{:,} lead intergenic mCNV eGenes.'.format(tdf[tdf.overlap_gene_cons == False].shape[0]))
print('Effect sizes for intergenic lead mCNVs are biased '
      '(p={:.3e}, binomial test).'.format(p))
    
gs.tight_layout(fig, rect=[0, 0, 1, 0.85])

t = fig.text(0.005, 0.77, 'A', weight='bold', 
             size=12)
t = fig.text(0.5, 0.77, 'B', weight='bold', 
             size=12)

fig.savefig(os.path.join(outdir, 'mcnv_eqtl_effect_sizes.pdf'))
fig.savefig(os.path.join(outdir, 'mcnv_eqtl_effect_sizes.png'), dpi=300)









    



33 genic mCNV eGenes.
Effect sizes for genic lead mCNVs are biased (p=1.309e-07, binomial test).
56 lead intergenic mCNV eGenes.
Effect sizes for intergenic lead mCNVs are biased (p=1.842e-03, binomial test).