{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mesh between touching neighbors\n", "\n", "In this notebook we visualize which cells in a tissue are neighbord by drawing a mesh. Furthermore, we can change the intensity of the mesh so that it corresponds to the distance between the centroids of the cells." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pyclesperanto_prototype as cle\n", "import numpy as np\n", "from skimage.io import imshow\n", "import matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test data\n", "Let's generate some tissue-like structure consisting of cells which typicall have 6 neighbors" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 100. 200. 300. 400. 50. 150. 250. 350. 0. 100. 200. 300. 400.\n", " 50. 150. 250. 350. 0. 100. 200. 300. 400.]\n", " [ 0. 0. 0. 0. 0. 40. 40. 40. 40. 80. 80. 80. 80. 80.\n", " 120. 120. 120. 120. 160. 160. 160. 160. 160.]]\n" ] } ], "source": [ "# define centroids of cells\n", "pointlist = cle.push_zyx(np.asarray(\n", "[\n", " [0, 100, 200, 300, 400, 50, 150, 250, 350, 0, 100, 200, 300, 400, 50, 150, 250, 350, 0, 100, 200, 300, 400],\n", " [0, 0, 0, 0, 0, 40, 40, 40, 40, 80, 80, 80, 80, 80, 120, 120, 120, 120, 160, 160, 160, 160, 160]\n", "]))\n", "print(pointlist)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\rober\\miniconda3\\lib\\site-packages\\pyopencl\\__init__.py:252: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n", " warn(\"Non-empty compiler output encountered. Set the \"\n", "c:\\users\\rober\\miniconda3\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n", " lo, hi, cmap = _get_display_range(image)\n", "c:\\users\\rober\\miniconda3\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n", " lo, hi, cmap = _get_display_range(image)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# generate an image where the centroid are labelled (labelled spots image)\n", "labelled_spots = cle.pointlist_to_labelled_spots(pointlist)\n", "imshow(cle.pull_zyx(labelled_spots))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\rober\\miniconda3\\lib\\site-packages\\pyopencl\\__init__.py:252: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n", " warn(\"Non-empty compiler output encountered. Set the \"\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# extend the spots to make some artificial tissue image (label image)\n", "cells = cle.extend_labeling_via_voronoi(labelled_spots)\n", "\n", "cmap = matplotlib.colors.ListedColormap ( np.random.rand ( 256,3))\n", "imshow(cle.pull_zyx(cells), cmap=cmap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Draw a mesh between touching neighbors" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 13.554028 100. 200. 300. 386.44598 50.\n", " 150. 250. 350. 13.57647 100. 200.\n", " 300. 386.42352 50. 150. 250. 350.\n", " 13.845719 100. 200. 300. 386.15427 ]\n", " [ 15.785854 15.711423 15.711423 15.711423 15.785854 39.46\n", " 39.46 39.46 39.46 79.460785 79.46 79.46\n", " 79.46 79.460785 119.65166 119.65166 119.65166 119.65166\n", " 143.8476 143.92326 143.92326 143.92326 143.8476 ]]\n" ] } ], "source": [ "pointlist = cle.label_centroids_to_pointlist(cells)\n", "print(pointlist)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "touch_matrix = cle.generate_touch_matrix(cells)\n", "imshow(cle.pull_zyx(touch_matrix))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mesh = cle.touch_matrix_to_mesh(pointlist, touch_matrix)\n", "imshow(cle.pull_zyx(mesh))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse and visualize number of touching neighbors" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0. 2. 3. 3. 3. 2. 5. 5. 5. 5. 4. 6. 6. 6. 4. 5. 5. 5. 5. 2. 3. 3. 3. 2.]]\n" ] } ], "source": [ "number_of_touching_neighbors_vector = cle.count_touching_neighbors(touch_matrix)\n", "print(number_of_touching_neighbors_vector)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# visualize in a parametric image\n", "parametric_image = cle.replace_intensities(cells, number_of_touching_neighbors_vector)\n", "imshow(cle.pull_zyx(parametric_image))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Alternatively, in very short:\n", "parametric_image = cle.touching_neighbor_count_map(cells)\n", "imshow(cle.pull_zyx(parametric_image))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }