def community_plot(
tree: str,
table: str,
sample_metadata: str,
output_dir: str,
pcoa: str,
feature_metadata: str,
ignore_missing_samples: bool,
filter_extra_samples: bool,
filter_missing_features: bool,
number_of_pcoa_features: int,
shear_to_table: bool,
) -> None:
tree_newick, fm = check_and_process_files(output_dir, tree,
feature_metadata)
table = load_table(table)
sample_metadata = pd.read_csv(sample_metadata, sep="\t", index_col=0)
if pcoa is not None:
pcoa = OrdinationResults.read(pcoa)
pcoa = prepare_pcoa(pcoa, number_of_pcoa_features)
viz = Empress(
tree_newick,
table=table,
sample_metadata=sample_metadata,
feature_metadata=fm,
ordination=pcoa,
ignore_missing_samples=ignore_missing_samples,
filter_extra_samples=filter_extra_samples,
filter_missing_features=filter_missing_features,
shear_to_table=shear_to_table,
)
os.makedirs(output_dir)
save_viz(viz, output_dir, q2=False)
def tree_plot(output_dir: str,
tree: NewickFormat,
feature_metadata: qiime2.Metadata = None) -> None:
"""Visualizes a tree (optionally with feature metadata)."""
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
t = get_bp(tree)
viz = Empress(tree=t, feature_metadata=feature_metadata)
save_viz(viz, output_dir)
def tree_plot(
tree: str,
output_dir: str,
feature_metadata: str,
shear_to_feature_metadata: bool,
) -> None:
tree_newick, fm = check_and_process_files(output_dir, tree,
feature_metadata)
viz = Empress(tree_newick,
feature_metadata=fm,
shear_to_feature_metadata=shear_to_feature_metadata)
os.makedirs(output_dir)
save_viz(viz, output_dir, q2=False)
def community_plot(output_dir: str,
tree: NewickFormat,
feature_table: biom.Table,
sample_metadata: qiime2.Metadata,
pcoa: OrdinationResults = None,
feature_metadata: qiime2.Metadata = None,
ignore_missing_samples: bool = False,
filter_extra_samples: bool = False,
filter_missing_features: bool = False,
number_of_features: int = 5,
shear_tree: bool = True) -> None:
"""Visualizes a tree alongside community-level data.
The functionality available in this visualization is a superset of the
functionality in tree_plot() -- including sample metadata coloring /
barplots, animations, and Emperor integration support.
"""
if pcoa is not None and pcoa.features is not None:
# select the top N most important features based on the vector's
# magnitude (coped from q2-emperor)
feats = pcoa.features.copy()
# in cases where the axes are all zero there might be all-NA
# columns
feats.fillna(0, inplace=True)
origin = np.zeros_like(feats.columns)
feats['importance'] = feats.apply(euclidean, axis=1, args=(origin, ))
feats.sort_values('importance', inplace=True, ascending=False)
feats.drop(['importance'], inplace=True, axis=1)
pcoa.features = feats[:number_of_features].copy()
sample_metadata = sample_metadata.to_dataframe()
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
t = get_bp(tree)
viz = Empress(tree=t,
table=feature_table,
sample_metadata=sample_metadata,
feature_metadata=feature_metadata,
ordination=pcoa,
ignore_missing_samples=ignore_missing_samples,
filter_extra_samples=filter_extra_samples,
filter_missing_features=filter_missing_features,
shear_tree=shear_tree)
save_viz(viz, output_dir)
def community_plot(output_dir: str,
tree: NewickFormat,
feature_table: biom.Table,
sample_metadata: qiime2.Metadata,
pcoa: OrdinationResults = None,
feature_metadata: qiime2.Metadata = None,
ignore_missing_samples: bool = False,
filter_extra_samples: bool = False,
filter_missing_features: bool = False,
number_of_features: int = 5,
shear_to_table: bool = True) -> None:
"""Visualizes a tree alongside community-level data.
The functionality available in this visualization is a superset of the
functionality in tree_plot() -- including sample metadata coloring /
barplots, animations, and Emperor integration support.
"""
if pcoa is not None and pcoa.features is not None:
pcoa = prepare_pcoa(pcoa, number_of_features)
sample_metadata = sample_metadata.to_dataframe()
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
t = get_bp(tree)
viz = Empress(tree=t,
table=feature_table,
sample_metadata=sample_metadata,
feature_metadata=feature_metadata,
ordination=pcoa,
ignore_missing_samples=ignore_missing_samples,
filter_extra_samples=filter_extra_samples,
filter_missing_features=filter_missing_features,
shear_to_table=shear_to_table)
save_viz(viz, output_dir)