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sv_classifier.py crash at calc_params if no del in training data. #321

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sv_classifier.py crash at calc_params if no del in training data. #321

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63f433c
updated sv_classifier.py to fit into sv-pipeline
jasonbhn May 2, 2022
b0f39a5
Merge pull request #1 from jasonbhn/jasonbhn-naive-bayes-debug
jasonbhn May 2, 2022
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37 changes: 20 additions & 17 deletions svtools/sv_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,22 +231,26 @@ def calc_params(vcf_path, sex_chrom_names):
df=df[(df.log2r>=df.q_low) & (df.log2r<=df.q_high)]
#df.to_csv('./train.csv')
#adjust copy number for small deletions (<1kb), no strong relationship b/w cn and size for dups evident so far
small_het_dels = df[(df.svtype=="DEL") & (df.GT=="0/1") & (df.svlen<1000) & (df.svlen>=50)].copy()
small_hom_dels = df[(df.svtype=="DEL") & (df.GT=="1/1") & (df.svlen<1000) & (df.svlen>=50)].copy()
het_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="0/1") & (df.svtype=="DEL")]['log2r'])
hom_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="1/1") & (df.svtype=="DEL")]['log2r'])
small_het_dels.loc[:,'offset']=small_het_dels.loc[:,'log2r']-het_del_mean
small_hom_dels.loc[:,'offset']=small_hom_dels.loc[:,'log2r']-hom_del_mean
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
hom_del_fit=smf.ols('offset~log_len',small_hom_dels).fit()
het_del_fit=smf.ols('offset~log_len',small_het_dels).fit()
#print hom_del_fit.summary()
#print het_del_fit.summary()
small_hom_dels.loc[:,'log2r_adj'] = small_hom_dels.loc[:,'log2r'] - hom_del_fit.predict(small_hom_dels)
small_het_dels.loc[:,'log2r_adj'] = small_het_dels.loc[:,'log2r'] - het_del_fit.predict(small_het_dels)
small_dels=small_hom_dels.append(small_het_dels)
small_dels=small_dels[['var_id', 'sample', 'svtype', 'svlen', 'AF', 'GT', 'CN', 'log_len', 'log2r', 'q_low', 'q_high', 'log2r_adj']]
if "DEL" in df.svtype.unique().tolist():
small_het_dels = df[(df.svtype=="DEL") & (df.GT=="0/1") & (df.svlen<1000) & (df.svlen>=50)].copy()
small_hom_dels = df[(df.svtype=="DEL") & (df.GT=="1/1") & (df.svlen<1000) & (df.svlen>=50)].copy()
het_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="0/1") & (df.svtype=="DEL")]['log2r'])
hom_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="1/1") & (df.svtype=="DEL")]['log2r'])
small_het_dels.loc[:,'offset']=small_het_dels.loc[:,'log2r']-het_del_mean
small_hom_dels.loc[:,'offset']=small_hom_dels.loc[:,'log2r']-hom_del_mean
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
hom_del_fit=smf.ols('offset~log_len',small_hom_dels).fit()
het_del_fit=smf.ols('offset~log_len',small_het_dels).fit()
small_hom_dels.loc[:,'log2r_adj'] = small_hom_dels.loc[:,'log2r'] - hom_del_fit.predict(small_hom_dels)
small_het_dels.loc[:,'log2r_adj'] = small_het_dels.loc[:,'log2r'] - het_del_fit.predict(small_het_dels)
small_dels=small_hom_dels.append(small_het_dels)
small_dels=small_dels[['var_id', 'sample', 'svtype', 'svlen', 'AF', 'GT', 'CN', 'log_len', 'log2r', 'q_low', 'q_high', 'log2r_adj']]
else:
het_del_fit = None
hom_del_fit = None
small_dels = pd.DataFrame(columns=['var_id', 'sample', 'svtype', 'svlen', 'AF', 'GT', 'CN',\
'log_len', 'log2r', 'q_low', 'q_high', 'log2r_adj'])
# dels of length<100 bp are excluded here
df1=df.loc[(df.svtype!="DEL") | (df.GT=="0/0") | (df.svlen>=1000), :].copy()
df1.loc[:,'log2r_adj']=df1.loc[:,'log2r']
Expand All @@ -255,7 +259,6 @@ def calc_params(vcf_path, sex_chrom_names):
params=pd.pivot_table(params, index=['sample', 'svtype'], columns='GT', values=['mean', 'var', 'len']).reset_index()
params.columns=['sample', 'svtype', 'mean0', 'mean1', 'mean2', 'var0', 'var1', 'var2', 'len0', 'len1', 'len2']
params['std_pooled'] = np.sqrt((params['var0']*params['len0']+params['var1']*params['len1']+params['var2']*params['len2'])/(params['len0']+params['len1']+params['len2']))
#params.to_csv('./params.csv')
return (params, het_del_fit, hom_del_fit)


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