def gradient_clustering(table: pd.DataFrame,
gradient: MetadataCategory,
weighted=True) -> skbio.TreeNode:
""" Builds a tree for features based on a gradient.
Parameters
----------
table : pd.DataFrame
Contingency table where rows are samples and columns are features.
gradient : qiime2.MetadataCategory
Continuous vector of measurements corresponding to samples.
weighted : bool
Specifies if abundance or presence/absence information
should be used to perform the clustering.
Returns
-------
skbio.TreeNode
Represents the partitioning of features with respect to the gradient.
"""
c = gradient.to_series()
c = c.astype(np.float)
if not weighted:
table = table > 0
t = gradient_linkage(table, c, method='average')
mean_g = mean_niche_estimator(table, c)
mean_g = pd.Series(mean_g, index=table.columns)
mean_g = mean_g.sort_values()
t = gradient_sort(t, mean_g)
return t
def dendrogram_heatmap(output_dir: str, table: pd.DataFrame,
tree: TreeNode, metadata: MetadataCategory,
ndim=10, method='clr', color_map='viridis'):
nodes = [n.name for n in tree.levelorder() if not n.is_tip()]
nlen = min(ndim, len(nodes))
numerator_color, denominator_color = '#fb9a99', '#e31a1c'
highlights = pd.DataFrame([[numerator_color, denominator_color]] * nlen,
index=nodes[:nlen])
if method == 'clr':
mat = pd.DataFrame(clr(centralize(table)),
index=table.index,
columns=table.columns)
elif method == 'log':
mat = pd.DataFrame(np.log(table),
index=table.index,
columns=table.columns)
# TODO: There are a few hard-coded constants here
# will need to have some adaptive defaults set in the future
fig = heatmap(mat, tree, metadata.to_series(), highlights, cmap=color_map,
highlight_width=0.01, figsize=(12, 8))
fig.savefig(os.path.join(output_dir, 'heatmap.svg'))
fig.savefig(os.path.join(output_dir, 'heatmap.pdf'))
css = r"""
.square {
float: left;
width: 100px;
height: 20px;
margin: 5px;
border: 1px solid rgba(0, 0, 0, .2);
}
.numerator {
background: %s;
}
.denominator {
background: %s;
}
""" % (numerator_color, denominator_color)
index_fp = os.path.join(output_dir, 'index.html')
with open(index_fp, 'w') as index_f:
index_f.write('<html><body>\n')
index_f.write('<h1>Dendrogram heatmap</h1>\n')
index_f.write('<img src="heatmap.svg" alt="heatmap">')
index_f.write('<a href="heatmap.pdf">')
index_f.write('Download as PDF</a><br>\n')
index_f.write('<style>%s</style>' % css)
index_f.write('<div class="square numerator">'
'Numerator<br/></div>')
index_f.write('<div class="square denominator">'
'Denominator<br/></div>')
index_f.write('</body></html>\n')
def dendrogram_heatmap(output_dir: str,
table: pd.DataFrame,
tree: TreeNode,
metadata: MetadataCategory,
ndim=10,
method='clr',
color_map='viridis'):
nodes = [n.name for n in tree.levelorder()]
nlen = min(ndim, len(nodes))
highlights = pd.DataFrame([['#00FF00', '#FF0000']] * nlen,
index=nodes[:nlen])
if method == 'clr':
mat = pd.DataFrame(clr(centralize(table)),
index=table.index,
columns=table.columns)
elif method == 'log':
mat = pd.DataFrame(np.log(table),
index=table.index,
columns=table.columns)
# TODO: There are a few hard-coded constants here
# will need to have some adaptive defaults set in the future
fig = heatmap(mat,
tree,
metadata.to_series(),
highlights,
cmap=color_map,
highlight_width=0.01,
figsize=(12, 8))
fig.savefig(os.path.join(output_dir, 'heatmap.svg'))
index_fp = os.path.join(output_dir, 'index.html')
with open(index_fp, 'w') as index_f:
index_f.write('<html><body>\n')
index_f.write('<h1>Dendrogram heatmap</h1>\n')
index_f.write('<img src="heatmap.svg" alt="heatmap">')
index_f.write('</body></html>\n')
def balance_taxonomy(output_dir: str, balances: pd.DataFrame, tree: TreeNode,
taxonomy: pd.DataFrame,
balance_name: Str,
taxa_level: Int = 0,
metadata: MetadataCategory = None) -> None:
# parse out headers for taxonomy
taxa_data = list(taxonomy['Taxon'].apply(lambda x: x.split(';')).values)
taxa_df = pd.DataFrame(taxa_data,
index=taxonomy.index)
# fill in NAs
def f(x):
y = np.array(list(map(lambda k: k is not None, x)))
i = max(0, np.where(y)[0][-1])
x[np.logical_not(y)] = [x[i]] * np.sum(np.logical_not(y))
return x
taxa_df = taxa_df.apply(f, axis=1)
num_clade = tree.find(balance_name).children[NUMERATOR]
denom_clade = tree.find(balance_name).children[DENOMINATOR]
if num_clade.is_tip():
num_features = pd.DataFrame(
{num_clade.name: taxa_df.loc[num_clade.name]}
).T
else:
num_features = taxa_df.loc[num_clade.subset()]
if denom_clade.is_tip():
denom_features = pd.DataFrame(
{denom_clade.name: taxa_df.loc[denom_clade.name]}
).T
else:
denom_features = taxa_df.loc[denom_clade.subset()]
num_color, denom_color = '#4c72b0', '#4c72b0'
fig, (ax_num, ax_denom) = plt.subplots(2)
balance_barplots(tree, balance_name, taxa_level, taxa_df,
denom_color=denom_color, num_color=num_color,
axes=(ax_num, ax_denom))
ax_num.set_title(
r'$%s_{numerator} \; taxa \; (%d \; taxa)$' % (balance_name,
len(num_features)))
ax_denom.set_title(
r'$%s_{denominator} \; taxa \; (%d \; taxa)$' % (balance_name,
len(denom_features)))
ax_denom.set_xlabel('Number of unique taxa')
plt.tight_layout()
fig.savefig(os.path.join(output_dir, 'barplots.svg'))
fig.savefig(os.path.join(output_dir, 'barplots.pdf'))
if metadata is not None:
fig2, ax = plt.subplots()
c = metadata.to_series()
data, c = match(balances, c)
data[c.name] = c
y = data[balance_name]
# check if continuous
try:
c = c.astype(np.float64)
ax.scatter(c.values, y)
ax.set_xlabel(c.name)
except:
balance_boxplot(balance_name, data, y=c.name, ax=ax)
ylabel = (r"$%s = \ln \frac{%s_{numerator}}"
"{%s_{denominator}}$") % (balance_name,
balance_name,
balance_name)
ax.set_title(ylabel, rotation=0)
ax.set_ylabel('log ratio')
fig2.savefig(os.path.join(output_dir, 'balance_metadata.svg'))
fig2.savefig(os.path.join(output_dir, 'balance_metadata.pdf'))
index_fp = os.path.join(output_dir, 'index.html')
with open(index_fp, 'w') as index_f:
index_f.write('<html><body>\n')
if metadata is not None:
index_f.write('<h1>Balance vs %s </h1>\n' % c.name)
index_f.write(('<img src="balance_metadata.svg" '
'alt="barplots">\n\n'
'<a href="balance_metadata.pdf">'
'Download as PDF</a><br>\n'))
index_f.write(('<h1>Balance Taxonomy</h1>\n'
'<img src="barplots.svg" alt="barplots">\n\n'
'<a href="barplots.pdf">'
'Download as PDF</a><br>\n'
'<h3>Numerator taxa</h3>\n'
'<a href="numerator.csv">\n'
'Download as CSV</a><br>\n'
'<h3>Denominator taxa</h3>\n'
'<a href="denominator.csv">\n'
'Download as CSV</a><br>\n'))
num_features.to_csv(os.path.join(output_dir, 'numerator.csv'),
header=True, index=True)
denom_features.to_csv(os.path.join(output_dir, 'denominator.csv'),
header=True, index=True)
index_f.write('</body></html>\n')