def plot_phewas_manhattan():
df = pd.read_csv(sys.argv[1], dtype={'phecode':str})
phewas_category = pd.read_table(sys.argv[2], dtype=str)
phewas_category = phewas_category.rename(columns={'phewas_code':'phecode'}).loc[:,["phecode","category"]].drop_duplicates()
merged = df.merge(phewas_category, how='left', on='phecode')
merged = merged.loc[merged.p>0,:]
manhattan(merged.p, merged.phecode.astype(float), merged.category, '', xlabel="category")
plt.title("Case vs Control: Chi-Squared")
plt.savefig(sys.argv[3])
plt.clf()
chrs = [str(i) for i in range(1, 23)]
chrs_names = np.array([str(i) for i in range(1, 23)])
chrs_names[1::2] = ''
colors = ['#1b9e77', "#d95f02", '#7570b3', '#e7298a']
# Converting from HEX into RGB
colors = [hex2color(colors[i]) for i in range(len(colors))]
# Load data
data = pd.read_fwf(datafile)
manhattan(data['P'], data["BP"], data["CHR"].apply(str), '', \
type='single', \
chrs_plot=[str(i) for i in range(1,23)], \
chrs_names=chrs_names, \
cut = 0, \
title='Eye color', \
xlabel='chromosome', \
ylabel='-log10(p-value)', \
lines= [], \
colors = colors, \
scaling = '-log10')
plt.savefig('%s_manhattan.png' % prefix, dpi=300)
############# QQ plot #############
from assocplots.qqplot import *
mpl.rcParams['figure.dpi'] = 100
mpl.rcParams['savefig.dpi'] = 100
mpl.rcParams['figure.figsize'] = 5.375, 5.375
qqplot([data["P"]], ['eyecolor'],
mpl.rcParams['figure.figsize'] = [12.375, 6.375]
chrs = [str(i) for i in range(1, 23)]
chrs_names = np.array([str(i) for i in range(1, 23)])
chrs_names[18::2] = ''
cmap = plt.get_cmap('Greys')
colors = [cmap(i) for i in [1.0, 0.6, 1.0, 0.6]]
plt.clf()
manhattan(datf['f2'],
datf['f21'],
datf['f20'],
'OCD-ADHD Female Meta',
p2=datm['f2'],
pos2=datm['f21'],
chr2=datm['f20'],
label2='OCD-ADHD Male Meta',
type='inverted',
chrs_plot=[str(i) for i in range(1, 23)],
chrs_names=chrs_names,
cut=0,
title='',
xlabel='chromosome',
ylabel='-log10(p-value)',
lines=[7.3],
top1=10,
top2=10,
colors=colors)
plt.savefig('OCD_ADHD_mal_fem_Manhattan.png', dpi=300)
import matplotlib.pyplot as plt
# makes sure can save pictures that a display is present!
plt.switch_backend('agg')
import pylab
import fastlmm.util.util as flutil
# better manhattan plot than MS Genomics one (or at least more robust)
# can be tweaked at will!
from assocplots.manhattan import *
from matplotlib.pyplot import figure
figure(num=None, figsize=(10, 7), dpi=80,)
manhattan(results_df["PValue"], results_df["ChrPos"], results_df["Chr"], OUTPUT_NAME,
lines=[5], colors=['r', 'b'], cut=1)
plt.savefig('Figures/' + OUTPUT_NAME + '_Manhattan_Plot.png')
plt.close()
# # QQ Plot
# In[11]:
from fastlmm.util.stats import plotp
plotp.qqplot(results_df["PValue"].values, xlim=[0,5], ylim=[0,5])
pylab.savefig('Figures/' + OUTPUT_NAME + 'QQ_Plot.png')
pylab.close()
mpl.rcParams['figure.dpi']=2000
mpl.rcParams['savefig.dpi']=2000
mpl.rcParams['figure.figsize']=17, 7
chrs = [str(i) for i in range(1,23)]
chrs_names = np.array([str(i) for i in range(1,23)])
chrs_names[1::2] = ''
cmap = plt.get_cmap('viridis')
colors = [cmap(i) for i in [0.0,0.33,0.67,0.90]]
manhattan( Baseline_PEER10_RegOut['f3'], Baseline_PEER10_RegOut['f1'], Baseline_PEER10_RegOut['f0'].astype(str), 'Baseline_RegOut_PEER10',
p2=Baseline_Log2_RegOut['f3'], pos2=Baseline_Log2_RegOut['f1'], chr2=Baseline_Log2_RegOut['f0'].astype(str), label2='Baseline_RegOut_Log2',
type='inverted',
chrs_plot=[str(i) for i in range(1,23)],
chrs_names=chrs_names,
cut = 0,
title='Baseline eQTLs of TRANSCRiBE 121 patients',
xlabel='chromosome',
ylabel='-log10(p-value)',
lines= [],
top1 = 50,
top2 = 50,
colors = colors)
plt.savefig('ChicagoPlot_BaselineRegOut+-PEER10.png', dpi=2000)
Li00=[Baseline_Log2_NoReg,Baseline_Log2_RegOut,Baseline_PEER10_NoReg,Baseline_PEER10_RegOut,Ischemia_Log2_NoReg,Ischemia_Log2_RegOut,Ischemia_PEER10_NoReg,Ischemia_PEER10_RegOut];
for x in Li00:
mpl.rcParams['figure.dpi']=300