def is_one_gene(p):
'''Test to see if most mutations are due to single gene'''
counts = hit_matrix.ix[run.gene_sets[p]].sum(1).dropna().order()
with_top = hit_matrix.ix[run.gene_sets[p]].sum().clip_upper(1).sum()
without = hit_matrix.ix[run.gene_sets[p] -
{counts.idxmax()}].sum().clip_upper(1).sum()
return ((with_top - without) / without) > .5
meta_matrix = meta_matrix[size_filter(meta_matrix)]
s = Series({p: is_one_gene(p) for p in meta_matrix.index})
meta_matrix = meta_matrix.ix[s==False]
hit_matrix = hit_matrix[size_filter(hit_matrix)]
'''Add passing features to the Data Object'''
mut.features = meta_matrix.append(hit_matrix)
mut.compress()
mut.uncompress()
'''Save updated Data Object (with additional features field'''
mut.save()
mut.uncompress()
'''Draw pathway_plots for pathway level features'''
meta_features = [f for f in mut.features.index if f in run.gene_sets]
pathway_plot_folder = mut.path + '/Figures/PathwayPlots/'
if not os.path.isdir(pathway_plot_folder):
os.makedirs(pathway_plot_folder)
for i,p in enumerate(meta_features):
df = mut.df.ix[run.gene_sets[p]]
from Processing.Helpers import merge_redundant
report_path = sys.argv[1]
cancer_type = sys.argv[2]
data_type = sys.argv[3]
data_type = data_type[3:]
'''Load in run and CN data'''
run = pickle.load(open(report_path + '/RunObject.p', 'rb'))
cancer = run.load_cancer(cancer_type)
if data_type == 'broad':
data = Dataset(cancer, run, 'CN_broad')
data.features = data.df
data.save()
sys.exit(0)
data = Dataset(cancer, run, 'CN')
data.path = '_'.join([data.path, data_type])
if data_type == 'deletion':
data.hit_val = -2
elif data_type == 'amplification':
data.hit_val = 2
elif data_type == 'amplification_low':
data.df = data.df.replace(1,2)
data.hit_val = 2
hit_matrix = (data.df==data.hit_val).astype(float)
