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Add 'equal_frequency' option to highly_variable_genes #572

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Add 'equal_frequency' option to highly_variable_genes #572

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4e676e2
Add 'equal_frequency' option to highly_variable_genes
davidhbrann Mar 30, 2019
78649a9
Bugfix for case with many genes that have low means
davidhbrann Mar 30, 2019
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13 changes: 11 additions & 2 deletions scanpy/preprocessing/_highly_variable_genes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ def highly_variable_genes(
n_top_genes=None,
n_bins=20,
flavor='seurat',
binning_method = 'equal_width',
subset=False,
inplace=True
):
Expand Down Expand Up @@ -57,6 +58,9 @@ def highly_variable_genes(
Choose the flavor for computing normalized dispersion. In their default
workflows, Seurat passes the cutoffs whereas Cell Ranger passes
`n_top_genes`.
binning_method : `{'equal_width', 'equal_frequency'}`, optional (default: 'equal_width')
Choose the binning method for the means. In `equal_width`, each bin covers the same width.
For `equal_frequency`, each bin has an equal number of genes.
subset : `bool`, optional (default: `False`)
Inplace subset to highly-variable genes if `True` otherwise merely indicate
highly variable genes.
Expand Down Expand Up @@ -107,7 +111,12 @@ def highly_variable_genes(
df['mean'] = mean
df['dispersion'] = dispersion
if flavor == 'seurat':
df['mean_bin'] = pd.cut(df['mean'], bins=n_bins)
if binning_method == 'equal_width':
df['mean_bin'] = pd.cut(df['mean'], bins=n_bins)
elif binning_method == 'equal_frequency':
df['mean_bin'] = pd.qcut(df['mean'], q=n_bins, duplicates='drop')
else:
raise ValueError('`binning_method` needs to be "equal_width" or "equal_frequency"')
disp_grouped = df.groupby('mean_bin')['dispersion']
disp_mean_bin = disp_grouped.mean()
disp_std_bin = disp_grouped.std(ddof=1)
Expand Down Expand Up @@ -139,7 +148,7 @@ def highly_variable_genes(
from statsmodels import robust
df['mean_bin'] = pd.cut(df['mean'], np.r_[
-np.inf,
np.percentile(df['mean'], np.arange(10, 105, 5)),
np.percentile(df['mean'], np.linspace(10, 100, n_bins - 1)),
np.inf
])
disp_grouped = df.groupby('mean_bin')['dispersion']
Expand Down
1 change: 1 addition & 0 deletions scanpy/tests/test_highly_variable_genes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
def test_highly_variable_genes_basic():
adata = sc.datasets.blobs()
sc.pp.highly_variable_genes(adata)
sc.pp.highly_variable_genes(adata, binning_method="equal_frequency")


def test_higly_variable_genes_compare_to_seurat():
Expand Down