def test_skip_zero_cluster_cols(self):
clustered = cluster(self.dataframe, cluster_cols=True, skip_zero=True)
self.assertEqual(
clustered.as_matrix().tolist(),
[[0, 0, 4, 5], [8, 8, 4, 5], [1, 1, 4, 5], [9, 9, 4, 5]])
self.assertEqual(clustered.columns.tolist(), ['c1', 'c3', 'c2', 'c4'])
self.assertEqual(clustered.index.tolist(), ['r1', 'r3', 'r4', 'r6'])
def assert_no_change(self, array):
dataframe = pandas.DataFrame(array)
clustered = cluster(dataframe,
skip_zero=False,
cluster_rows=True,
cluster_cols=True)
self.assertEqual(clustered.as_matrix().tolist(), array)
def test_pass_through(self):
clustered = cluster(self.dataframe,
skip_zero=False,
cluster_rows=False,
cluster_cols=False)
self.assertEqual(clustered.as_matrix().tolist(),
[[0, 4, 0, 5], [0, 0, 0, 0], [8, 4, 8, 5],
[1, 4, 1, 5], [0, 0, 0, 0], [9, 4, 9, 5]])
self.assertEqual(clustered.columns.tolist(), ['c1', 'c2', 'c3', 'c4'])
self.assertEqual(clustered.index.tolist(),
['r1', 'r2', 'r3', 'r4', 'r5', 'r6'])
def test_no_skip_cluster_both(self):
clustered = cluster(self.dataframe,
skip_zero=False,
cluster_rows=True,
cluster_cols=True)
self.assertEqual(clustered.as_matrix().tolist(), [
[8, 8, 4, 5],
[9, 9, 4, 5],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 4, 5],
[1, 1, 4, 5],
])
self.assertEqual(clustered.columns.tolist(), ['c1', 'c3', 'c2', 'c4'])
self.assertEqual(clustered.index.tolist(),
['r3', 'r6', 'r2', 'r5', 'r1', 'r4'])
def _update_heatmap(self, selected_conditions_ids_json,
selected_gene_ids_json, scale, palette, cluster_rows,
cluster_cols, label_rows_mode, label_cols_mode,
row_scaling_mode):
selected_conditions = (json.loads(selected_conditions_ids_json)
or self._conditions)
base_df = self._scale_dataframe(row_scaling_mode)
selected_conditions_df = base_df[selected_conditions]
selected_conditions_genes_df = self._filter_by_gene_ids_json(
selected_conditions_df, selected_gene_ids_json)
truncated_dataframe = (
sort_by_variance(selected_conditions_genes_df).head(
self._most_variable_rows)
if self._most_variable_rows else selected_conditions_genes_df)
cluster_dataframe = cluster(truncated_dataframe,
cluster_rows=(cluster_rows == 'cluster'),
cluster_cols=(cluster_cols == 'cluster'))
show_genes = (len(cluster_dataframe.index.tolist()) < 40
and label_rows_mode == 'auto') or \
label_rows_mode == 'always'
show_conditions = label_cols_mode in ['always', 'auto']
# With a proportional font, this is only an estimate.
char_width = 10
if show_genes:
row_max = max([len(s) for s in list(cluster_dataframe.index)])
left_margin = row_max * char_width
else:
left_margin = 75
if show_conditions:
col_max = max([len(s) for s in list(cluster_dataframe)])
bottom_margin = col_max * char_width
else:
bottom_margin = 10
return {
'data': [
self._heatmap(cluster_dataframe,
is_log_scale=(scale == 'log'),
palette=palettes[palette])
],
'layout':
go.Layout(
xaxis={
'ticks': '',
'showticklabels': show_conditions,
'tickangle': 90
},
yaxis={
'ticks': '',
'showticklabels': show_genes
},
margin={
'l': left_margin,
'b': bottom_margin,
't': 30, # so infobox on hover is not truncated
'r': 0
})
}