In this notebook, I will identify genes that do not conform to the canonical epistasis relationships expected for the hypoxia pathway in C. elegans.
In [1]:
# important stuff:
import os
import pandas as pd
import numpy as np
# TEA and morgan
import genpy
import gvars
import morgan as morgan
import tissue_enrichment_analysis as tea
# Graphics
import matplotlib as mpl
import matplotlib.ticker as plticker
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.patheffects as path_effects
from matplotlib import rc
# rc('text', usetex=True)
rc('text', usetex=True)
rc('text.latex', preamble=r'\usepackage{cmbright}')
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# Magic function to make matplotlib inline;
%matplotlib inline
# This enables SVG graphics inline.
# There is a bug, so uncomment if it works.
%config InlineBackend.figure_formats = {'png', 'retina'}
# JB's favorite Seaborn settings for notebooks
rc = {'lines.linewidth': 2,
'axes.labelsize': 18,
'axes.titlesize': 18,
'axes.facecolor': 'DFDFE5'}
sns.set_context('notebook', rc=rc)
sns.set_style("dark")
ft = 35 #title fontsize
mpl.rcParams['xtick.labelsize'] = 18
mpl.rcParams['ytick.labelsize'] = 18
mpl.rcParams['legend.fontsize'] = 14
genvar = gvars.genvars()
q=0.1
tidy_data = pd.read_csv('../output/temp_files/DE_genes.csv')
tidy_data.sort_values('target_id', inplace=True)
tidy_data.dropna(subset=['ens_gene'], inplace=True)
tidy_data = tidy_data[tidy_data.genotype != 'fog-2']
tidy_data['fancy genotype'] = tidy_data.code.map(genvar.fancy_mapping)
Genes that display non-canonical epistasis:¶
To identify genes that display non-canonical epistasis, I will fuse some columns to the dataframe containing the rhy-1 transcriptome. Using these columns, we will find genes that have inverse expression changes between vhl-1(lf) mutants and egl-9(lf) or rhy-1(lf) mutants.
In [2]:
# Specify the genotypes to refer to:
single_mutants = ['b', 'c', 'd', 'e', 'g']
# Specify which letters are double mutants and their genotype
double_mutants = {'a' : 'bd', 'f':'bc'}
# initialize the morgan.hunt object:
thomas = morgan.hunt('target_id', 'b', 'tpm', 'qval')
# input the genmap file:
thomas.add_genmap('../input/library_genotype_mapping.txt', comment='#')
# add the names of the single mutants
thomas.add_single_mutant(single_mutants)
# add the names of the double mutants
thomas.add_double_mutants(['a', 'f'], ['bd', 'bc'])
# set the q-value threshold for significance to its default value, 0.1
thomas.set_qval()
# Add the tpm files:
kallisto_loc = '../input/kallisto_all/'
sleuth_loc = '../sleuth/kallisto/'
thomas.add_tpm(kallisto_loc, '/kallisto/abundance.tsv', '')
# load all the beta dataframes:
for file in os.listdir("../sleuth/kallisto"):
if file[:4] == 'beta':
letter = file[-5:-4].lower()
thomas.add_beta(sleuth_loc + file, letter)
thomas.beta[letter].sort_values('target_id', inplace=True)
thomas.beta[letter].reset_index(inplace=True)
thomas.filter_data()
# place all
df1 = thomas.beta['e'].copy()
df2 = thomas.beta['b']
df3 = thomas.beta['d']
df1['b_b'] = df2.b
df1['b_d'] = df3.b
df1['q_b'] = df2.qval
df1['q_d'] = df3.qval
In [3]:
# use least strict conditions:
lowestrhy = (df1.b*df1.b_d < 0) # egl anti vhl
lowestsigrhy = ((df1.qval < q) & # egl sig
(df1.q_d < q)) # vhl sig
lowestegl = (df1.b_b*df1.b_d < 0) # egl anti vhl
lowestsigegl = ((df1.q_b < q) & # egl sig
(df1.q_d < q)) # vhl sig
Now that we have coded up the conditions, let's see what we get!
In [4]:
df1.sort_values('qval', ascending=True)
hifoh = df1[
(lowestegl & lowestsigegl) |
(lowestrhy & lowestsigrhy)].target_id.unique()
print('{0} candidates found for HIF-1-OH regulation'.format(len(hifoh)))
df1[(lowestegl & lowestsigegl) |
(lowestrhy & lowestsigrhy)].to_csv('../output/temp_files/hifoh_candidates.csv', index=False)
In [5]:
hypoxia = pd.read_csv('../output/temp_files/hypoxia_response.csv')
In [6]:
len(hypoxia[hypoxia.target_id.isin(hifoh)].ens_gene.unique())
Out[6]:
Plotting genes that display non-canonical changes:¶
So far, all I have done is find the genes that have different expression between vhl-1 and egl-9. It would be very interesting if genes that have these different behaviors still conform to the same epistatic rules (egl-9 = egl-9;vhl-1 and hif-1 = egl-9 hif-1). We can make a qPCR plot to see if that is the case:
In [7]:
tidy = tidy_data[tidy_data.target_id.isin(hifoh)].copy()
x_sort = {}
for i, xi in enumerate(tidy.target_id.unique()):
x_sort[xi] = i + 1
tidy['order'] = tidy.target_id.map(x_sort)
tidy.sort_values('order', inplace=True)
tidy.reset_index(inplace=True)
Initially, let's just look at the first five genes in our set:
In [8]:
ind = tidy.target_id.isin(tidy.target_id.unique()[0:5])
genpy.qPCR_plot(tidy[ind], genvar.plot_order, genvar.plot_color,
clustering='fancy genotype', plotting_group='target_id', rotation=90)
Wow! All of them obey the epistatic rules! This is cool.
Figure 7A¶
Next, i will generate figure 7A and 7B in the paper.
In [9]:
new_order = {r'\emph{egl-9}': 0,
r'\emph{vhl-1}': 1,
}
In [10]:
genpy.qPCR_plot(tidy[(tidy.target_id.isin(hifoh[0:15])) & (tidy.code.isin(['b', 'd']))],
new_order, genvar.plot_color, clustering='fancy genotype',
plotting_group='target_id', rotation=90)
plt.savefig('../output/vhl1_noncanonical.svg')
7B¶
In [11]:
to_focus_on = ['nlp-31', 'ftn-1', 'ftn-2']
gene_order = {'nlp-31': 1, 'ftn-1': 2, 'ftn-2': 3}
temp = tidy[(tidy.ext_gene.isin(to_focus_on))].copy()
del temp['order']
temp['order'] = temp.ext_gene.map(gene_order)
genpy.qPCR_plot(temp, genvar.plot_order, genvar.plot_color,
clustering='fancy genotype', plotting_group='ext_gene')
plt.savefig('../output/hif1oh_qPCR.svg', bbox_inches='tight')
