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# # Compare Meta Data for Tryptase Expressing vs Non-Tryptase Expressing
#
# 1) tryptase-expressing v. non-expressing – you would likely have to set some somewhat arbitrary RPKM threshold based upon controls/confidence
#
# 2) among tryptase-positive: alphaDUP-expressing v. all alpha-expressing v. non-alpha-expressing
#
# 3) if anything comes out of 2, it might make sense to iterate this back through step 1 (comparing alphaDUP v. non-expressing for instance)

import pandas as pd

df = pd.read_csv('../data_files/mbase_normed/aml.meta.mbase_norm.csv', index_col=0)

df.head()


# +
def has_dup(df):
    """
    Does the sample have any reads covering the snps that characterise the dup locus?
    
    """

    if df['norm_alpha_dup_read_covers_snps_count_exact'] == 0:
        
        return False
    else:
        
        return True
    
def has_tryptase()


# -

df['has_dup'] = df.apply(has_dup, axis=1)

df['norm_alignment_count'].value_counts()



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