def ancom(output_dir: str,
table: pd.DataFrame,
metadata: qiime2.CategoricalMetadataColumn,
transform_function: str = 'clr',
difference_function: str = None) -> None:
index_fp = os.path.join(output_dir, 'index.html')
metadata = metadata.filter_ids(table.index)
if metadata.has_missing_values():
missing_data_sids = metadata.get_ids(where_values_missing=True)
missing_data_sids = ', '.join(sorted(missing_data_sids))
raise ValueError('Metadata column is missing values for the '
'following samples. Values need to be added for '
'these samples, or the samples need to be removed '
'from the table: %s' % missing_data_sids)
ancom_results = skbio_ancom(table,
metadata.to_series(),
significance_test=f_oneway)
# scikit-bio 0.4.2 returns a single tuple from ancom, and scikit-bio 0.5.0
# returns two tuples. We want to support both scikit-bio versions, so we
# tuplize ancom_result to support both. Similarly, the "reject" column
# was renamed in scikit-bio 0.5.0, so we apply a rename here (which does
# nothing if a column called "reject" isn't found).
ancom_results = qiime2.core.util.tuplize(ancom_results)
ancom_results[0].sort_values(by='W', ascending=False)
ancom_results[0].rename(columns={'reject': 'Reject null hypothesis'},
inplace=True)
ancom_results[0].to_csv(os.path.join(output_dir, 'ancom.csv'),
header=True,
index=True)
html = _volcanoplot(output_dir, table, metadata, ancom_results[0],
transform_function, difference_function)
significant_features = ancom_results[0][ancom_results[0]
['Reject null hypothesis']]
significant_features_present = not significant_features.empty
insignificant_div = ('<div>No significant features identified!</div>')
with open(index_fp, 'w') as index_f:
index_f.write('<html>\n')
if html is not None:
index_f.write('<head>\n')
index_f.write(INLINE.render())
index_f.write('</head>\n')
index_f.write('<body>\n')
index_f.write('<h1>ANCOM statistical results</h1>\n')
index_f.write('<a href="ancom.csv">Download complete table as CSV</a>'
'<br>\n')
if significant_features_present:
index_f.write(
q2templates.df_to_html(significant_features['W'].to_frame(),
border=None,
classes=None))
else:
index_f.write(insignificant_div)
if len(ancom_results) == 2:
ancom_results[1].to_csv(os.path.join(output_dir,
'percent-abundances.csv'),
header=True,
index=True)
index_f.write(('<h1>Percentile abundances of features '
'by group</h1>\n'))
index_f.write(('<a href="percent-abundances.csv">'
'Download complete table as CSV</a><br>\n'))
if significant_features_present:
index_f.write(
q2templates.df_to_html(
ancom_results[1].loc[significant_features.index],
border=None,
classes=None))
else:
index_f.write(insignificant_div)
if html is not None:
index_f.write(html[1])
index_f.write(html[0])
else:
index_f.write('<p>Unable to generate volcano plot, please check '
'the ANCOM statistical results (above).</p>\n')
index_f.write('</body></html>\n')
def ancom(output_dir: str,
table: pd.DataFrame,
metadata: qiime2.CategoricalMetadataColumn,
transform_function: str = 'clr',
difference_function: str = None) -> None:
metadata = metadata.filter_ids(table.index)
if metadata.has_missing_values():
missing_data_sids = metadata.get_ids(where_values_missing=True)
missing_data_sids = ', '.join(sorted(missing_data_sids))
raise ValueError('Metadata column is missing values for the '
'following samples. Values need to be added for '
'these samples, or the samples need to be removed '
'from the table: %s' % missing_data_sids)
ancom_results = skbio_ancom(table,
metadata.to_series(),
significance_test=f_oneway)
ancom_results[0].sort_values(by='W', ascending=False, inplace=True)
ancom_results[0].rename(columns={'reject': 'Reject null hypothesis'},
inplace=True)
significant_features = ancom_results[0][ancom_results[0]
['Reject null hypothesis']]
context = dict()
if not significant_features.empty:
context['significant_features'] = q2templates.df_to_html(
significant_features['W'].to_frame())
context['percent_abundances'] = q2templates.df_to_html(
ancom_results[1].loc[significant_features.index])
metadata = metadata.to_series()
cats = list(set(metadata))
transform_function_name = transform_function
transform_function = _transform_functions[transform_function]
transformed_table = table.apply(transform_function,
axis=1,
result_type='broadcast')
if difference_function is None:
if len(cats) == 2:
difference_function = 'mean_difference'
else: # len(categories) > 2
difference_function = 'f_statistic'
_d_func = _difference_functions[difference_function]
def diff_func(x):
args = _d_func(*[x[metadata == c] for c in cats])
if isinstance(args, tuple):
return args[0]
else:
return args
# effectively doing a groupby operation wrt to the metadata
fold_change = transformed_table.apply(diff_func, axis=0)
if not pd.isnull(fold_change).all():
pre_filtered_ids = set(fold_change.index)
with pd.option_context('mode.use_inf_as_na', True):
fold_change = fold_change.dropna(axis=0)
filtered_ids = pre_filtered_ids - set(fold_change.index)
filtered_ancom_results = ancom_results[0].drop(labels=filtered_ids)
volcano_results = pd.DataFrame({
transform_function_name: fold_change,
'W': filtered_ancom_results.W
})
volcano_results.index.name = 'id'
volcano_results.to_csv(os.path.join(output_dir, 'data.tsv'),
header=True,
index=True,
sep='\t')
volcano_results = volcano_results.reset_index(drop=False)
spec = {
'$schema':
'https://vega.github.io/schema/vega/v4.json',
'width':
300,
'height':
300,
'data': [{
'name': 'values',
'values': volcano_results.to_dict(orient='records')
}],
'scales': [{
'name': 'xScale',
'domain': {
'data': 'values',
'field': transform_function_name
},
'range': 'width'
}, {
'name': 'yScale',
'domain': {
'data': 'values',
'field': 'W'
},
'range': 'height'
}],
'axes': [{
'scale': 'xScale',
'orient': 'bottom',
'title': transform_function_name
}, {
'scale': 'yScale',
'orient': 'left',
'title': 'W'
}],
'marks': [{
'type': 'symbol',
'from': {
'data': 'values'
},
'encode': {
'hover': {
'fill': {
'value': '#FF0000'
},
'opacity': {
'value': 1
}
},
'enter': {
'x': {
'scale': 'xScale',
'field': transform_function_name
},
'y': {
'scale': 'yScale',
'field': 'W'
}
},
'update': {
'fill': {
'value': 'black'
},
'opacity': {
'value': 0.3
},
'tooltip': {
'signal':
"{{'title': datum['id'], '{0}': "
"datum['{0}'], 'W': datum['W']}}".format(
transform_function_name)
}
}
}
}]
}
context['vega_spec'] = json.dumps(spec)
if filtered_ids:
context['filtered_ids'] = ', '.join(sorted(filtered_ids))
copy_tree(os.path.join(TEMPLATES, 'ancom'), output_dir)
ancom_results[0].to_csv(os.path.join(output_dir, 'ancom.tsv'),
header=True,
index=True,
sep='\t')
ancom_results[1].to_csv(os.path.join(output_dir, 'percent-abundances.tsv'),
header=True,
index=True,
sep='\t')
index = os.path.join(TEMPLATES, 'ancom', 'index.html')
q2templates.render(index, output_dir, context=context)
def ancom(output_dir: str,
table: pd.DataFrame,
metadata: qiime2.CategoricalMetadataColumn,
transform_function: str = 'clr',
difference_function: str = None) -> None:
metadata = metadata.filter_ids(table.index)
if metadata.has_missing_values():
missing_data_sids = metadata.get_ids(where_values_missing=True)
missing_data_sids = ', '.join(sorted(missing_data_sids))
raise ValueError('Metadata column is missing values for the '
'following samples. Values need to be added for '
'these samples, or the samples need to be removed '
'from the table: %s' % missing_data_sids)
ancom_results = skbio_ancom(table,
metadata.to_series(),
significance_test=f_oneway)
ancom_results[0].sort_values(by='W', ascending=False, inplace=True)
ancom_results[0].rename(columns={'reject': 'Reject null hypothesis'},
inplace=True)
significant_features = ancom_results[0][
ancom_results[0]['Reject null hypothesis']]
context = dict()
if not significant_features.empty:
context['significant_features'] = q2templates.df_to_html(
significant_features['W'].to_frame())
context['percent_abundances'] = q2templates.df_to_html(
ancom_results[1].loc[significant_features.index])
metadata = metadata.to_series()
cats = list(set(metadata))
transform_function_name = transform_function
transform_function = _transform_functions[transform_function]
transformed_table = table.apply(
transform_function, axis=1, result_type='broadcast')
if difference_function is None:
if len(cats) == 2:
difference_function = 'mean_difference'
else: # len(categories) > 2
difference_function = 'f_statistic'
_d_func = _difference_functions[difference_function]
def diff_func(x):
args = _d_func(*[x[metadata == c] for c in cats])
if isinstance(args, tuple):
return args[0]
else:
return args
# effectively doing a groupby operation wrt to the metadata
fold_change = transformed_table.apply(diff_func, axis=0)
if not pd.isnull(fold_change).all():
volcano_results = pd.DataFrame({transform_function_name: fold_change,
'W': ancom_results[0].W})
volcano_results = volcano_results.reset_index(drop=False)
spec = {
'$schema': 'https://vega.github.io/schema/vega/v4.json',
'width': 300,
'height': 300,
'data': [
{'name': 'values',
'values': volcano_results.to_dict(orient='records')}],
'scales': [
{'name': 'xScale',
'domain': {'data': 'values',
'field': transform_function_name},
'range': 'width'},
{'name': 'yScale',
'domain': {'data': 'values', 'field': 'W'},
'range': 'height'}],
'axes': [
{'scale': 'xScale', 'orient': 'bottom',
'title': transform_function_name},
{'scale': 'yScale', 'orient': 'left', 'title': 'W'}],
'marks': [
{'type': 'symbol',
'from': {'data': 'values'},
'encode': {
'hover': {
'fill': {'value': '#FF0000'},
'opacity': {'value': 1}},
'enter': {
'x': {'scale': 'xScale',
'field': transform_function_name},
'y': {'scale': 'yScale', 'field': 'W'}},
'update': {
'fill': {'value': 'black'},
'opacity': {'value': 0.3},
'tooltip': {
'signal': "{{'title': datum['index'], '{0}': "
"datum['{0}'], 'W': datum['W']}}".format(
transform_function_name)}}}}]}
context['vega_spec'] = json.dumps(spec)
copy_tree(os.path.join(TEMPLATES, 'ancom'), output_dir)
ancom_results[0].to_csv(os.path.join(output_dir, 'ancom.tsv'),
header=True, index=True, sep='\t')
ancom_results[1].to_csv(os.path.join(output_dir,
'percent-abundances.tsv'),
header=True, index=True, sep='\t')
index = os.path.join(TEMPLATES, 'ancom', 'index.html')
q2templates.render(index, output_dir, context=context)
