From a534969caebbc11398fe4519b8c10a21f5268e1c Mon Sep 17 00:00:00 2001 From: elaubsch Date: Thu, 22 Jun 2023 18:00:35 -0700 Subject: [PATCH 1/6] Add cell counts function --- deepcell_spots/utils/results_utils.py | 50 ++++++++++++++++++++++ deepcell_spots/utils/results_utils_test.py | 23 +++++++++- 2 files changed, 72 insertions(+), 1 deletion(-) diff --git a/deepcell_spots/utils/results_utils.py b/deepcell_spots/utils/results_utils.py index 00742ae..2e54604 100644 --- a/deepcell_spots/utils/results_utils.py +++ b/deepcell_spots/utils/results_utils.py @@ -34,6 +34,56 @@ import pandas as pd from scipy.spatial import distance +from tqdm import tqdm + + +def get_cell_counts(df_spots, segmentation_output): + """Converts Polaris outputs into a DataFrame containing gene expression counts per cell. + Detection assigned to the background (value of 0 in `segmentation_output`) are discarded. + + 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`. + segmentation_output (array): Polaris segmentation result, shape is `(b,x,y,c)`. `c` should + equal one. `b` should be the number of fields of view (as defined by `batch_id` in + `df_spots`). + + Returns: + pandas.DataFrame: Gene expression counts per cell, columns are `batch_id`, `cell_id`, `x`, + `y`, and columns for each decoded gene in the sample. + """ + genes = list(df_spots.predicted_name.unique()) + if 'Background' in genes: + genes.remove('Background') + if 'Unknown' in genes: + genes.remove('Unknown') + genes = [item for item in genes if not('Blank' in item)] + df_cell_counts = pd.DataFrame(columns=['batch_id', 'cell_id', 'x', 'y'] + genes) + + for fov in tqdm(df_spots.batch_id.unique()): + df_fov = df_spots.loc[df_spots.batch_id==fov] + seg = segmentation_output[fov,...,0] + for cell in range(1,np.max(df_fov.cell_id.values)): + cell_inds = np.argwhere(seg==cell) + x = np.mean(cell_inds[:,0]) + y = np.mean(cell_inds[:,1]) + df_cell = df_fov.loc[df_fov.cell_id==cell] + counts = dict(df_cell.predicted_name.value_counts()) + data = {} + data['batch_id'] = [fov] + data['cell_id'] = [cell] + data['x'] = [x] + data['y'] = [y] + for gene in genes: + if gene in list(counts.keys()): + data[gene] = [counts[gene]] + else: + data[gene] = [0] + single_cell_counts = pd.DataFrame.from_dict(data) + + df_cell_counts = pd.concat([df_cell_counts, single_cell_counts], axis=0) + + return(df_cell_counts) def filter_results(df_spots, batch_id=None, cell_id=None, diff --git a/deepcell_spots/utils/results_utils_test.py b/deepcell_spots/utils/results_utils_test.py index 8d4fc99..354a464 100644 --- a/deepcell_spots/utils/results_utils_test.py +++ b/deepcell_spots/utils/results_utils_test.py @@ -34,11 +34,32 @@ import pandas as pd from tensorflow.python.platform import test -from deepcell_spots.utils.results_utils import filter_results, gene_visualization +from deepcell_spots.utils.results_utils import (filter_results, gene_visualization, + get_cell_counts) class TestResultsUtils(test.TestCase): + def test_get_cell_counts(self): + df_spots = pd.DataFrame( + [ + [10, 10, 0, 1, 0.95, 1, 'A', 0, 'prediction', 0], + [10, 20, 0, 1, 0.95, 1, 'A', 1, 'prediction', 0], + [10, 30, 0, 1, 0.95, 1, 'A', 2, 'prediction', 0], + [20, 20, 0, 1, 0.95, 1, 'B', 3, 'error rescue', 1], + [30, 30, 0, 1, 0.95, 1, 'C', 4, 'mixed rescue', 1] + ], + columns=['x', 'y', 'batch_id', 'cell_id', 'probability', 'predicted_id', + 'predicted_name', 'spot_index', 'source', 'masked'] + ) + segmentation_output = np.ones((1, 2048, 2048, 1)) + df_cell_counts = get_cell_counts(df_spots, segmentation_output) + self.assertEqual(df_cell_counts.batch_id.values, [0]*5) + self.assertEqual(df_cell_counts.cell_id.values, [1]*5) + self.assertEqual(df_cell_counts.A.values, [3]) + self.assertEqual(df_cell_counts.B.values, [1]) + self.assertEqual(df_cell_counts.C.values, [1]) + def test_filter_results(self): df_spots = pd.DataFrame( [ From 778de08a208cc2509e571a7931f6e866e0f067f1 Mon Sep 17 00:00:00 2001 From: elaubsch Date: Thu, 22 Jun 2023 18:09:24 -0700 Subject: [PATCH 2/6] Bug fix --- deepcell_spots/utils/results_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcell_spots/utils/results_utils.py b/deepcell_spots/utils/results_utils.py index 2e54604..ab2e8cd 100644 --- a/deepcell_spots/utils/results_utils.py +++ b/deepcell_spots/utils/results_utils.py @@ -63,7 +63,7 @@ def get_cell_counts(df_spots, segmentation_output): for fov in tqdm(df_spots.batch_id.unique()): df_fov = df_spots.loc[df_spots.batch_id==fov] seg = segmentation_output[fov,...,0] - for cell in range(1,np.max(df_fov.cell_id.values)): + for cell in range(1,np.max(df_fov.cell_id.values)+1): cell_inds = np.argwhere(seg==cell) x = np.mean(cell_inds[:,0]) y = np.mean(cell_inds[:,1]) From b88566773a086ea2a227b64ea9843718aacc9586 Mon Sep 17 00:00:00 2001 From: elaubsch Date: Thu, 22 Jun 2023 18:09:52 -0700 Subject: [PATCH 3/6] Fix tests --- deepcell_spots/utils/results_utils_test.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deepcell_spots/utils/results_utils_test.py b/deepcell_spots/utils/results_utils_test.py index 354a464..30077a8 100644 --- a/deepcell_spots/utils/results_utils_test.py +++ b/deepcell_spots/utils/results_utils_test.py @@ -54,11 +54,11 @@ def test_get_cell_counts(self): ) segmentation_output = np.ones((1, 2048, 2048, 1)) df_cell_counts = get_cell_counts(df_spots, segmentation_output) - self.assertEqual(df_cell_counts.batch_id.values, [0]*5) - self.assertEqual(df_cell_counts.cell_id.values, [1]*5) - self.assertEqual(df_cell_counts.A.values, [3]) - self.assertEqual(df_cell_counts.B.values, [1]) - self.assertEqual(df_cell_counts.C.values, [1]) + 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) def test_filter_results(self): df_spots = pd.DataFrame( From 3aacaf3dc435cc03daf76dec58ebdb74e358778f Mon Sep 17 00:00:00 2001 From: elaubsch Date: Fri, 23 Jun 2023 02:03:07 -0700 Subject: [PATCH 4/6] Remove centroid calculation --- deepcell_spots/utils/results_utils.py | 21 ++++++++------------- deepcell_spots/utils/results_utils_test.py | 3 +-- 2 files changed, 9 insertions(+), 15 deletions(-) diff --git a/deepcell_spots/utils/results_utils.py b/deepcell_spots/utils/results_utils.py index ab2e8cd..9bb1ab3 100644 --- a/deepcell_spots/utils/results_utils.py +++ b/deepcell_spots/utils/results_utils.py @@ -37,43 +37,37 @@ from tqdm import tqdm -def get_cell_counts(df_spots, segmentation_output): +def get_cell_counts(df_spots): """Converts Polaris outputs into a DataFrame containing gene expression counts per cell. Detection assigned to the background (value of 0 in `segmentation_output`) are discarded. 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`. - segmentation_output (array): Polaris segmentation result, shape is `(b,x,y,c)`. `c` should - equal one. `b` should be the number of fields of view (as defined by `batch_id` in - `df_spots`). Returns: - pandas.DataFrame: Gene expression counts per cell, columns are `batch_id`, `cell_id`, `x`, - `y`, and columns for each decoded gene in the sample. + pandas.DataFrame: Gene expression counts per cell, columns are `batch_id`, `cell_id`, and + columns for each decoded gene in the sample. """ genes = list(df_spots.predicted_name.unique()) if 'Background' in genes: genes.remove('Background') if 'Unknown' in genes: genes.remove('Unknown') + genes = [item for item in genes if not('Blank' in item)] - df_cell_counts = pd.DataFrame(columns=['batch_id', 'cell_id', 'x', 'y'] + genes) + df_cell_counts = pd.DataFrame(columns=['batch_id', 'cell_id'] + genes) for fov in tqdm(df_spots.batch_id.unique()): df_fov = df_spots.loc[df_spots.batch_id==fov] - seg = segmentation_output[fov,...,0] + for cell in range(1,np.max(df_fov.cell_id.values)+1): - cell_inds = np.argwhere(seg==cell) - x = np.mean(cell_inds[:,0]) - y = np.mean(cell_inds[:,1]) df_cell = df_fov.loc[df_fov.cell_id==cell] counts = dict(df_cell.predicted_name.value_counts()) data = {} data['batch_id'] = [fov] data['cell_id'] = [cell] - data['x'] = [x] - data['y'] = [y] + for gene in genes: if gene in list(counts.keys()): data[gene] = [counts[gene]] @@ -83,6 +77,7 @@ def get_cell_counts(df_spots, segmentation_output): df_cell_counts = pd.concat([df_cell_counts, single_cell_counts], axis=0) + df_cell_counts = df_cell_counts.reset_index(drop=True) return(df_cell_counts) diff --git a/deepcell_spots/utils/results_utils_test.py b/deepcell_spots/utils/results_utils_test.py index 30077a8..0354f60 100644 --- a/deepcell_spots/utils/results_utils_test.py +++ b/deepcell_spots/utils/results_utils_test.py @@ -52,8 +52,7 @@ def test_get_cell_counts(self): columns=['x', 'y', 'batch_id', 'cell_id', 'probability', 'predicted_id', 'predicted_name', 'spot_index', 'source', 'masked'] ) - segmentation_output = np.ones((1, 2048, 2048, 1)) - df_cell_counts = get_cell_counts(df_spots, segmentation_output) + 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) From a49b7ff488ce78a5bd44d3a17398da2f19d09a77 Mon Sep 17 00:00:00 2001 From: elaubsch Date: Fri, 23 Jun 2023 02:09:23 -0700 Subject: [PATCH 5/6] Add export example notebook --- notebooks/Export Polaris results.ipynb | 1355 ++++++++++++++++++++++++ 1 file changed, 1355 insertions(+) create mode 100644 notebooks/Export Polaris results.ipynb diff --git a/notebooks/Export Polaris results.ipynb b/notebooks/Export Polaris results.ipynb new file mode 100644 index 0000000..0201755 --- /dev/null +++ b/notebooks/Export Polaris results.ipynb @@ -0,0 +1,1355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "657d06de", + "metadata": {}, + "source": [ + "The function `get_cell_counts` allows you to convert the Polaris output to a gene counts per cell table. This format is compatible with many downstream analysis tools, such as scanpy and squidpy. The data in this form can also be exported for downstream analysis in R packages, like Seurat and SpatialExperiment.\n", + "\n", + "To run this notebook you will need to pip install scanpy, which is not included in the requirements file for this package." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7deda7c", + "metadata": {}, + "outputs": [], + "source": [ + "#pip install scanpy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d389e211", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from anndata import AnnData\n", + "import scanpy as sc\n", + "\n", + "from tensorflow.keras.utils import get_file\n", + "\n", + "from deepcell.datasets import Dataset\n", + "from deepcell_spots.utils.results_utils import get_cell_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "74ede9f2", + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(self, path=None):\n", + " path = path if path else self.path\n", + " \n", + " basepath = os.path.expanduser(os.path.join('~', '.keras', 'datasets'))\n", + " prefix = path.split(os.path.sep)[:-1]\n", + " data_dir = os.path.join(basepath, *prefix) if prefix else basepath\n", + " if not os.path.exists(data_dir):\n", + " os.makedirs(data_dir)\n", + " elif not os.path.isdir(data_dir):\n", + " raise IOError('{} exists but is not a directory'.format(data_dir))\n", + "\n", + " path = get_file(path,\n", + " origin=self.url,\n", + " file_hash=self.file_hash)\n", + " df = pd.read_csv(path, index_col=0)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "258409ca", + "metadata": {}, + "outputs": [], + "source": [ + "Dataset.load_data = load_data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f0b081b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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196 rows × 198 columns

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190 rows × 198 columns

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" + ], + "text/plain": [ + " batch_id cell_id Cckar Net1 Sdc1 Maoa Cd4 Neat1 Glp2r Cd79b ... Ptger2 \\\n", + "1 26 2 0 0 0 1 0 0 0 0 ... 0 \n", + "2 26 3 0 0 0 0 0 0 0 0 ... 0 \n", + "4 26 5 3 2 10 3 0 34 0 1 ... 0 \n", + "5 26 6 0 3 8 5 0 23 0 1 ... 0 \n", + "6 26 7 2 0 7 2 1 11 1 0 ... 0 \n", + ".. ... ... ... ... ... ... .. ... ... ... ... ... \n", + "190 26 191 0 0 0 0 1 0 1 1 ... 0 \n", + "192 26 193 0 0 0 0 0 1 0 0 ... 0 \n", + "193 26 194 1 0 1 0 9 4 2 0 ... 0 \n", + "194 26 195 1 0 0 0 0 0 0 0 ... 0 \n", + "195 26 196 0 0 1 0 0 0 0 0 ... 0 \n", + "\n", + " Taar8c Taar3 Rcor2 Htr5a Taar8b Drd3 Scn3a Gper1 Gpr18 \n", + "1 0 0 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 0 0 \n", + "5 0 0 0 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "190 0 0 0 0 0 0 0 0 0 \n", + "192 0 0 0 0 0 0 0 0 0 \n", + "193 0 0 0 0 0 0 0 0 0 \n", + "194 0 0 0 0 0 0 0 0 0 \n", + "195 0 0 0 0 0 0 0 0 0 \n", + "\n", + "[190 rows x 198 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zero_cells = []\n", + "for i in range(len(cell_counts)):\n", + " if sum(cell_counts.iloc[i].values[2:])==0:\n", + " zero_cells.append(i)\n", + "mask = cell_counts.index.isin(zero_cells)\n", + "cell_counts = cell_counts.loc[~mask]\n", + "cell_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d25eab4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(190, 196)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features = cell_counts.to_numpy()[:,2:]\n", + "features.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "04ce268c", + "metadata": {}, + "outputs": [], + "source": [ + "adata = AnnData(features)" + ] + }, + { + "cell_type": "markdown", + "id": "02728996", + "metadata": {}, + "source": [ + "Example clustering analysis for cell type assignment is shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2e59d9bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 190 × 196\n", + " obs: 'leiden'\n", + " uns: 'log1p', 'pca', 'neighbors', 'umap', 'leiden'\n", + " obsm: 'X_pca', 'X_umap'\n", + " varm: 'PCs'\n", + " obsp: 'distances', 'connectivities'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc.pp.normalize_total(adata)\n", + "sc.pp.log1p(adata)\n", + "sc.pp.pca(adata)\n", + "sc.pp.neighbors(adata)\n", + "sc.tl.umap(adata)\n", + "sc.tl.leiden(adata)\n", + "adata" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1527b5de", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cm = mpl.cm.get_cmap('Accent')\n", + "cell_assignments = np.array(adata.obs['leiden'].astype(int))\n", + "n_cell_types = max(cell_assignments)\n", + "\n", + "fig,ax = plt.subplots(figsize=(12,8))\n", + "ax.scatter(adata.obsm['X_umap'][:,0], adata.obsm['X_umap'][:,1],\n", + " c=np.array(cell_assignments),\n", + " cmap=cm, alpha=1,\n", + " vmax=n_cell_types\n", + " )\n", + "\n", + "bounds = np.linspace(0, n_cell_types, n_cell_types+1)\n", + "norm = mpl.colors.BoundaryNorm(bounds, cm.N)\n", + "ax2 = fig.add_axes([0.95, 0.1, 0.03, 0.8])\n", + "cb = mpl.colorbar.ColorbarBase(ax2, cmap=cm, norm=norm,\n", + " spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82896f84", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bf0508c2898ba99cb3b8fb9a8fc1dc40d5145582 Mon Sep 17 00:00:00 2001 From: elaubsch Date: Fri, 23 Jun 2023 02:14:07 -0700 Subject: [PATCH 6/6] Update description --- notebooks/Export Polaris results.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/notebooks/Export Polaris results.ipynb b/notebooks/Export Polaris results.ipynb index 0201755..bc453da 100644 --- a/notebooks/Export Polaris results.ipynb +++ b/notebooks/Export Polaris results.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "657d06de", "metadata": {}, "source": [ - "The function `get_cell_counts` allows you to convert the Polaris output to a gene counts per cell table. This format is compatible with many downstream analysis tools, such as scanpy and squidpy. The data in this form can also be exported for downstream analysis in R packages, like Seurat and SpatialExperiment.\n", + "The function `get_cell_counts` allows you to convert the Polaris output to a gene counts per cell table. This format is compatible with many downstream analysis tools, such as scanpy and squidpy. The data in this form can also be exported for downstream analysis in R packages, like Seurat.\n", "\n", "To run this notebook you will need to pip install scanpy, which is not included in the requirements file for this package." ] @@ -1244,6 +1245,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "02728996", "metadata": {}, @@ -1291,7 +1293,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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