sei-framework/utils.py at main · FunctionLab/sei-framework · GitHub

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importos

importh5py
importnumpyasnp
importpandasaspd


defget_targets(filename):
"""
 Load the names of all the chromatin profiles predicted by Sei
 """
targets= []
withopen(filename, 'r') asfile_handle:
forlineinfile_handle:
targets.append(line.strip())
returntargets


defget_data(filename):
"""
 Load HDF5 file of predictions into memory
 """
fh=h5py.File(filename, 'r')
data=fh["data"][()]
fh.close()
returndata


defsc_projection(chromatin_profile_preds, clustervfeat):
return (np.dot(chromatin_profile_preds, clustervfeat.T) /
np.linalg.norm(clustervfeat, axis=1))


defsc_hnorm_varianteffect(chromatin_profile_ref, chromatin_profile_alt, clustervfeat, histone_inds):
chromatin_profile_ref_adjust=chromatin_profile_ref.copy()
chromatin_profile_ref_adjust[:, histone_inds] = \
chromatin_profile_ref_adjust[:, histone_inds] * (
 (np.sum(chromatin_profile_ref[:, histone_inds], axis=1)*0.5+
np.sum(chromatin_profile_alt[:, histone_inds], axis=1)*0.5) /
np.sum(chromatin_profile_ref[:, histone_inds], axis=1))[:, None]

chromatin_profile_alt_adjust=chromatin_profile_alt.copy()
chromatin_profile_alt_adjust[:, histone_inds] = \
chromatin_profile_alt_adjust[:, histone_inds] * (
 (np.sum(chromatin_profile_ref[:, histone_inds], axis=1)*0.5+
np.sum(chromatin_profile_alt[:, histone_inds], axis=1)*0.5) /
np.sum(chromatin_profile_alt[:, histone_inds], axis=1))[:, None]

refproj=sc_projection(chromatin_profile_ref_adjust, clustervfeat)
altproj=sc_projection(chromatin_profile_alt_adjust, clustervfeat)
diffproj=altproj[:,:40] -refproj[:,:40]
returndiffproj


defget_filename_prefix(filename):
"""Filename must follow Selene output file conventions.
 """
prefix=None
if'.alt_predictions'infilename:
prefix=filename.split('.alt_predictions')[0]
elif'.ref_predictions'infilename:
prefix=filename.split('.ref_predictions')[0]
else:
prefix=filename.split('_predictions')[0]
returnprefix


defwrite_to_tsv(max_abs_diff,
chromatin_profile_diffs,
sequence_class_projscores,
chromatin_profiles,
seqclass_names,
rowlabels,
output_chromatin_profile_file,
output_sequence_class_file):
sorted_sc_abs_diff=np.sort(max_abs_diff)[::-1]
sorted_maxsc_df=pd.DataFrame(sorted_sc_abs_diff, # dataframe
columns=['seqclass_max_absdiff'])

sorted_ixs=np.argsort(max_abs_diff)[::-1]
assertlen(sorted_ixs) ==chromatin_profile_diffs.shape[0]
sorted_rowlabels=rowlabels.iloc[sorted_ixs] # dataframe
sorted_rowlabels.reset_index(inplace=True)
rowlabel_columns=sorted_rowlabels.columns.tolist()

# sorted now
chromatin_profile_diffs=chromatin_profile_diffs[sorted_ixs, :]
sequence_class_projscores=sequence_class_projscores[sorted_ixs,:]

# dataframes
sorted_profiles_df=pd.DataFrame(chromatin_profile_diffs, columns=chromatin_profiles)
sorted_sc_df=pd.DataFrame(sequence_class_projscores, columns=seqclass_names)
delchromatin_profile_diffs

sei_df=pd.concat([sorted_maxsc_df, sorted_rowlabels, sorted_profiles_df],
axis=1)
sc_df=pd.concat([sorted_maxsc_df, sorted_rowlabels, sorted_sc_df],
axis=1)
sc_df[['seqclass_max_absdiff'] +rowlabel_columns+seqclass_names].to_csv(
output_sequence_class_file, sep='\t', index=False)

iflen(sei_df) >10000:
sei_df[['seqclass_max_absdiff'] +rowlabel_columns+chromatin_profiles].to_csv(
output_chromatin_profile_file, sep='\t', index=False, compression='gzip')
else:
sei_df[['seqclass_max_absdiff'] +rowlabel_columns+chromatin_profiles].to_csv(
output_chromatin_profile_file, sep='\t', index=False)