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Add function for barcode assignment to cells for optical pooled screens #70

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Jul 22, 2023
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Add function for barcode assignment to cells for optical pooled screens #70

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62 changes: 62 additions & 0 deletions deepcell_spots/utils/results_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,6 +81,68 @@ def get_cell_counts(df_spots):
return(df_cell_counts)


def assign_barcodes(df_spots, segmentation_results):
"""Assigns barcode identity to a cell for Polaris prediction for data from optical pooled
screens. This function does not support multi-batch inputs.

Args:
df_spots (pandas.DataFrame): Polaris result, columns are `x`, `y`, `batch_id`, `cell_id`,
`probability`, `predicted_id`, `predicted_name`, `spot_index`, `source`, and `masked`.
`batch_id` should only have one unique value.
segmentation_results (numpy.array): Segmentation result from Polaris with shape
`(1,x,y,1)`. Pixel values should match `cell_id` values in `df_spots`. The background
pixels are assumed to have the value 0.

Returns:
pandas.DataFrame: Barcode assignment for each cell, columns are `cell_id`, `x`, `y`,
`predicted_name`, `predicted_id`, `spot_counts`, `spot_fraction`. `x` and `y` are the
centroid of the cell with value `cell_id` in `segmentation_results`. `predicted_name`
and `predicted_id` correspond to the assigned barcode. `spot_counts` is the number of
spots detected in a cell with the assigned barcode. `spot_fraction` is the fraction
of detections in a cell with the assigned barcode.
"""
df_assignments = pd.DataFrame(columns=['cell_id', 'x', 'y', 'predicted_name', 'predicted_id',
'spot_counts', 'spot_fraction'])

if len(segmentation_results.shape) != 4:
raise ValueError('Input data must have {} dimensions. '
'Input data only has {} dimensions'.format(
4, len(segmentation_results.shape)))
if segmentation_results.shape[0] != 1:
raise ValueError('Input data must have a batch dimension of size 1. '
'Input data only has a batch dimension of size {}.'.format(
segmentation_results.shape[0]))

for i in tqdm(range(1,np.max(segmentation_results).astype(int)+1)):
df_cell = df_spots.loc[df_spots.cell_id == i]
df_cell = df_cell.loc[~df_cell.predicted_name.isin(['Background', 'Unknown'])]
n_spots = len(df_cell)

cell_pixels = np.argwhere(segmentation_results == i)
x = np.mean(cell_pixels[:,1])
y = np.mean(cell_pixels[:,2])

if n_spots > 0:
barcode_dict = {}
for barcode in df_cell.predicted_name.unique():
df_barcode = df_cell.loc[df_cell.predicted_name==barcode]
barcode_dict[barcode] = sum(df_barcode.probability)
assignment = max(barcode_dict, key=barcode_dict.get)
df_correct = df_cell.loc[df_cell.predicted_name==assignment]
assignment_id = df_correct.predicted_id.values[0]
counts = len(df_correct)
fraction = counts/n_spots
else:
assignment = 'None'
assignment_id = -1
counts = 0
fraction = 0

df_assignments.loc[len(df_assignments)] = [i, x, y, assignment, assignment_id, counts, fraction]

return(df_assignments)


def filter_results(df_spots, batch_id=None, cell_id=None,
gene_name=None, source=None, masked=False):
"""Filter Pandas DataFrame output from Polaris application by batch ID, cell ID,
Expand Down
78 changes: 72 additions & 6 deletions deepcell_spots/utils/results_utils_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@
from tensorflow.python.platform import test

from deepcell_spots.utils.results_utils import (filter_results, gene_visualization,
get_cell_counts)
get_cell_counts, assign_barcodes)


class TestResultsUtils(test.TestCase):
Expand All @@ -53,11 +53,11 @@ def test_get_cell_counts(self):
'predicted_name', 'spot_index', 'source', 'masked']
)
df_cell_counts = get_cell_counts(df_spots)
self.assertAllEqual(df_cell_counts.batch_id.values[0], 0)
self.assertAllEqual(df_cell_counts.cell_id.values[0], 1)
self.assertAllEqual(df_cell_counts.A.values[0], 3)
self.assertAllEqual(df_cell_counts.B.values[0], 1)
self.assertAllEqual(df_cell_counts.C.values[0], 1)
self.assertEqual(df_cell_counts.batch_id.values[0], 0)
self.assertEqual(df_cell_counts.cell_id.values[0], 1)
self.assertEqual(df_cell_counts.A.values[0], 3)
self.assertEqual(df_cell_counts.B.values[0], 1)
self.assertEqual(df_cell_counts.C.values[0], 1)

def test_filter_results(self):
df_spots = pd.DataFrame(
Expand Down Expand Up @@ -116,6 +116,72 @@ def test_filter_results(self):
self.assertEqual(len(df_spots.columns), len(df_filter.columns))


def test_assign_barcodes(self):
# Test one spot example
df_spots = pd.DataFrame(
[
[10, 10, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0]
],
columns=['x', 'y', 'batch_id', 'cell_id', 'probability', 'predicted_id',
'predicted_name', 'spot_index', 'source', 'masked']
)
segmentation_results = np.ones((1, 20, 20, 1))
df_assignments = assign_barcodes(df_spots, segmentation_results)
self.assertEqual(len(df_assignments), 1)
self.assertEqual(df_assignments.predicted_name.values[0], 'A')
self.assertEqual(df_assignments.predicted_id.values[0], 1)
self.assertEqual(df_assignments.spot_counts.values[0], 1)
self.assertEqual(df_assignments.spot_fraction.values[0], 1)

# Test multi spot example
df_spots = pd.DataFrame(
[
[8, 8, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0],
[9, 9, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0],
[10, 10, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0],
[11, 11, 0, 1, 0.9, 2, 'B', 0, 'prediction', 0],
[12, 12, 0, 1, 0.9, 2, 'B', 0, 'prediction', 0]
],
columns=['x', 'y', 'batch_id', 'cell_id', 'probability', 'predicted_id',
'predicted_name', 'spot_index', 'source', 'masked']
)
segmentation_results = np.ones((1, 20, 20, 1))
df_assignments = assign_barcodes(df_spots, segmentation_results)
self.assertEqual(len(df_assignments), 1)
self.assertEqual(df_assignments.predicted_name.values[0], 'A')
self.assertEqual(df_assignments.predicted_id.values[0], 1)
self.assertEqual(df_assignments.spot_counts.values[0], 3)
self.assertEqual(df_assignments.spot_fraction.values[0], 0.6)

# Test cell with no spots/two cells
df_spots = pd.DataFrame(
[
[8, 8, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0],
[9, 9, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0],
[10, 10, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0],
[11, 11, 0, 1, 0.9, 2, 'B', 0, 'prediction', 0],
[12, 12, 0, 1, 0.9, 2, 'B', 0, 'prediction', 0]
],
columns=['x', 'y', 'batch_id', 'cell_id', 'probability', 'predicted_id',
'predicted_name', 'spot_index', 'source', 'masked']
)
segmentation_results = np.ones((1, 20, 20, 1))
segmentation_results[0,0] += 1
df_assignments = assign_barcodes(df_spots, segmentation_results)
self.assertEqual(len(df_assignments), 2)
self.assertEqual(df_assignments.predicted_name.values[1], 'None')
self.assertEqual(df_assignments.predicted_id.values[1], -1)
self.assertEqual(df_assignments.spot_counts.values[1], 0)
self.assertEqual(df_assignments.spot_fraction.values[1], 0)

# Test raises errors
segmentation_results = np.ones((1, 20, 20))
with self.assertRaises(ValueError):
_ = assign_barcodes(df_spots, segmentation_results)
segmentation_results = np.ones((2, 20, 20, 1))
with self.assertRaises(ValueError):
_ = assign_barcodes(df_spots, segmentation_results)

def test_gene_visualization(self):
df_spots = pd.DataFrame(
[
Expand Down