STEP 14: Python Code
class step14_preparation:
@staticmethod
def execute(*kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
print('load_combined_data')
comb_dict = step14_preparation.load_combined_data(5, folder = folder, output_folder = output_folder)
print('load_sex_data')
sex_dict = step14_preparation.load_sex_data(5, folder = folder, output_folder = output_folder)
print('load_diet_data')
diet_dict = step14_preparation.load_diet_data(5, folder = folder, output_folder = output_folder)
print('export_considered_pathways')
step14_preparation.export_considered_pathways(folder = folder)
@staticmethod
def load_multivariate_module_specific_data(dataset, module_type, datatype, covariate, **kwargs):
n_subunit = kwargs.get('n_subunit',0)
folder = kwargs.get('folder','PATH')
joint_name = '_'.join([dataset, module_type, datatype,covariate])
filename = joint_name + '_data_all_multi_covariate_moduleSpecific_empiricalFDR.tsv.gz'
data = DataFrameAnalyzer.open_in_chunks(folder, filename)
data = data[data['n.subunits']>=n_subunit]
return data
@staticmethod
def load_multivariate_combined_module_specific_data(dataset, module_type, datatype, **kwargs):
n_subunit = kwargs.get('n_subunit',0)
folder = kwargs.get('folder','PATH')
joint_name = '_'.join([dataset, module_type, datatype])
filename = joint_name + '_data_all_multi_covariate_moduleSpecific_COMBINEDEFFECT.tsv.gz'
data = DataFrameAnalyzer.open_in_chunks(folder, filename)
data = data[data['n.subunits']>=n_subunit]
return data
@staticmethod
def load_combined_data(su, **kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
fname = 'protein_variation.tsv.gz'
data = DataFrameAnalyzer.open_in_chunks(folder, fname)
comb_all_quant = data[data.covariates=='sex_diet']
comb_mquant_complex = figure6a.load_multivariate_combined_module_specific_data('gygi3',
'complex','quant', n_subunit = su)
comb_mquant_corum = figure6a.load_multivariate_combined_module_specific_data('gygi3',
'corum','quant', n_subunit = su)
comb_mstoch_complex = figure6a.load_multivariate_combined_module_specific_data('gygi3',
'complex','stoichiometry', n_subunit = su)
comb_mstoch_corum = figure6a.load_multivariate_combined_module_specific_data('gygi3',
'corum','stoichiometry', n_subunit = su)
comb_dict = {'complex':{'quant':comb_mquant_complex,'stoichiometry':comb_mstoch_complex},
'all':{'quant':comb_all_quant}}
DataFrameAnalyzer.to_pickle(comb_dict, output_folder + 'combined_dictionary.pkl')
return comb_dict
@staticmethod
def load_sex_data(su, **kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
fname = 'protein_variation.tsv.gz'
data = DataFrameAnalyzer.open_in_chunks(folder, fname)
sex_all_quant = data[data.covariates=='sex']
mquant_complex = figure6a.load_multivariate_module_specific_data('gygi3',
'complex','quant','sex', n_subunit = su)
mstoch_complex = figure6a.load_multivariate_module_specific_data('gygi3',
'complex','stoichiometry','sex', n_subunit = su)
mquant_corum = figure6a.load_multivariate_module_specific_data('gygi3',
'corum','quant','sex', n_subunit = su)
mstoch_corum = figure6a.load_multivariate_module_specific_data('gygi3',
'corum','stoichiometry','sex', n_subunit = su)
sex_dict = {'complex':{'quant':mquant_complex,'stoichiometry':mstoch_complex},
'all':{'quant':sex_all_quant}}
DataFrameAnalyzer.to_pickle(sex_dict, output_folder + 'sex_effect_dictionary.pkl')
return sex_dict
@staticmethod
def load_diet_data(su, **kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
fname = 'protein_variation.tsv.gz'
data = DataFrameAnalyzer.open_in_chunks(folder, fname)
diet_all_quant = data[data.covariates=='diet']
mquant_complex = figure6a.load_multivariate_module_specific_data('gygi3',
'complex','quant','diet', n_subunit = su)
mstoch_complex = figure6a.load_multivariate_module_specific_data('gygi3',
'complex','stoichiometry','diet', n_subunit = su)
mquant_corum = figure6a.load_multivariate_module_specific_data('gygi3',
'corum','quant','diet', n_subunit = su)
mstoch_corum = figure6a.load_multivariate_module_specific_data('gygi3',
'corum','stoichiometry','diet', n_subunit = su)
diet_dict = {'complex':{'quant':mquant_complex,'stoichiometry':mstoch_complex},
'all':{'quant':diet_all_quant}}
DataFrameAnalyzer.to_pickle(diet_dict, output_folder + 'diet_effect_dictionary.pkl')
return diet_dict
@staticmethod
def export_considered_pathways(**kwargs):
folder = kwargs.get('folder','PATH')
#pathways are considered when not different from random, but different from complex (<0.5)
df = DataFrameAnalyzer.getFile(folder,'corr_classification_pathways.tsv')
sub = df[df.pval1>0.1]
sub = sub[sub.pval1_complex < 0.1]
considered_pathways = list(sub.index)
df.to_csv(folder + 'corr_classification_pathways.tsv.gz', sep = '\t', compression = 'gzip')
return considered_pathways
class step14_figure:
@staticmethod
def execute(**kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
print('FIGURE6A: get_considered_pathways')
considered_pathways = step14_figure.get_considered_pathways(folder = folder)
print('FIGURE6A: plot')
step14_figure.plot(considered_pathways, folder = folder)
@staticmethod
def get_considered_pathways(**kwargs):
folder = kwargs.get('folder','PATH')
df = DataFrameAnalyzer.getFile(folder,'corr_classification_pathways.tsv.gz')
sub = df[df.pval1 > 0.1]
sub = sub[sub.pval1_complex < 0.1]
considered_pathways = list(sub.index)
return considered_pathways
@staticmethod
def plot(considered_pathways, **kwargs):
folder = kwargs.get('folder','PATH')
comb_dict = DataFrameAnalyzer.read_pickle(folder + 'combined_dictionary.pkl')
sex_dict = DataFrameAnalyzer.read_pickle(folder + 'sex_effect_dictionary.pkl')
diet_dict = DataFrameAnalyzer.read_pickle(folder + 'diet_effect_dictionary.pkl')
for key in ['all','complex','pathway']:
if key == 'all':
quant_sex_all = list(set(sex_dict[key]['quant']['r2.all.module']))
quant_diet_all = list(set(diet_dict[key]['quant']['r2.all.module']))
quant_comb_all = list(set(comb_dict[key]['quant']['r2.all.module']))
elif key == 'complex':
quant_sex_complex = list(set(sex_dict[key]['quant']['r2.all.module']))
stoch_sex_complex = list(set(sex_dict[key]['stoichiometry']['r2.all.module']))
quant_diet_complex = list(set(diet_dict[key]['quant']['r2.all.module']))
stoch_diet_complex = list(set(diet_dict[key]['stoichiometry']['r2.all.module']))
quant_comb_complex = list(set(comb_dict[key]['quant']['r2.all.module']))
stoch_comb_complex = list(set(comb_dict[key]['stoichiometry']['r2.all.module']))
elif key == 'pathway':
mquant_sex = sex_dict[key]['quant']
mquant_sex = mquant_sex[~mquant_sex['complex.name'].isin(considered_pathways)]
quant_sex_pathway = list(set(mquant_sex['r2.all.module']))
mstoch_sex = sex_dict[key]['stoichiometry']
mstoch_sex = mstoch_sex[~mstoch_sex['complex.name'].isin(considered_pathways)]
stoch_sex_pathway = list(set(mstoch_sex['r2.all.module']))
mquant_diet = diet_dict[key]['quant']
mquant_diet = mquant_diet[~mquant_diet['complex.name'].isin(considered_pathways)]
quant_diet_pathway = list(set(mquant_diet['r2.all.module']))
mstoch_diet = diet_dict[key]['stoichiometry']
mstoch_diet = mstoch_diet[~mstoch_diet['complex.name'].isin(considered_pathways)]
stoch_diet_pathway = list(set(mstoch_diet['r2.all.module']))
mquant_comb = comb_dict[key]['quant']
mquant_comb = mquant_comb[~mquant_comb['complex.name'].isin(considered_pathways)]
quant_comb_pathway = list(set(mquant_comb['r2.all.module']))
mstoch_comb = comb_dict[key]['stoichiometry']
mstoch_comb = mstoch_comb[~mstoch_comb['complex.name'].isin(considered_pathways)]
stoch_comb_pathway = list(set(mstoch_comb['r2.all.module']))
r2_quant_sex_complex = quant_sex_complex + quant_sex_pathway
r2_stoch_sex_complex = stoch_sex_complex + stoch_sex_pathway
r2_quant_diet_complex = quant_diet_complex + quant_diet_pathway
r2_stoch_diet_complex = stoch_diet_complex + stoch_diet_pathway
r2_quant_comb_complex = quant_comb_complex + quant_comb_pathway
r2_stoch_comb_complex = stoch_comb_complex + stoch_comb_pathway
r2_list = [r2_stoch_comb_complex, r2_quant_comb_complex, quant_comb_all,
r2_stoch_diet_complex, r2_quant_diet_complex, quant_diet_all,
r2_stoch_sex_complex, r2_quant_sex_complex, quant_sex_all]
label_list = ['scomb_complex','qcomb_complex','qcomb_all',
'sdiet_complex','qdiet_complex','qdiet_all',
'ssex_complex','qsex_complex','qsex_all']
color_list = ['lightblue','blue','lightgreen','green','grey','black']
big_color_list = ['black','grey','grey','green','lightgreen','lightgreen',
'blue','lightblue','lightblue']
small_color_list = ['lightblue','lightgreen','grey']
sns.set(context='notebook', style='white',
palette='deep', font='Liberation Sans', font_scale=1,
color_codes=False, rc=None)
plt.rcParams["axes.grid"] = False
plt.clf()
fig = plt.figure(figsize = (10,10))
gs = gridspec.GridSpec(9,9)
ax = plt.subplot(gs[0:6,0:])
ax.set_xlim(-0.01,0.4)
bp = ax.boxplot(r2_list,notch=0,sym="",vert=0,patch_artist=True, widths=[0.8]*len(r2_list))
plt.setp(bp['medians'], color="black")
plt.setp(bp['whiskers'], color="black",linestyle="--",alpha=0.8)
for i,patch in enumerate(bp['boxes']):
patch.set_edgecolor("black")
patch.set_alpha(0.6)
patch.set_color(big_color_list[i])
plt.yticks(list(xrange(len(label_list))))
ax.set_yticklabels(label_list)
print(scipy.stats.f_oneway(r2_list[0]+r2_list[1],r2_list[2])[1])
print(scipy.stats.f_oneway(r2_list[3]+r2_list[4],r2_list[5])[1])
print(scipy.stats.f_oneway(r2_list[6]+r2_list[7],r2_list[8])[1])
ax.axvline(0.1, color = 'black', linestyle = '--')
ax.axvline(0.2, color = 'black', linestyle = '--')
ax.axvline(0.3, color = 'black', linestyle = '--')
ax = plt.subplot(gs[6:9,0:])
ax.axis('off')
plt.savefig(folder + 'fig6a_combinedEffect_analysis.pdf', bbox_inches = 'tight', dpi = 400)
if __name__ == "__main__":
## EXECUTE STEP14
step14_preparation.execute(folder = sys.argv[1], output_folder = sys.argv[2])
step14_figure.execute(folder = sys.argv[1], output_folder = sys.argv[2])
All scripts were developed by Natalie Romanov (Bork group, EMBL). The source code used in the analysis of protein complex variability across individuals is released under the GNU General Public License v3.0. All scripts on this website/web resource is Copyright (C) 2019 Natalie Romanov, Michael Kuhn, Ruedi Aebersold, Alessandro Ori, Martin Beck, Peer Bork and EMBL.