{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interoperability\n", "\n", "This notebook shows some way that you can import and export data from `spatialproteomics`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "import spatialproteomics as sp\n", "import pandas as pd\n", "import xarray as xr\n", "import os\n", "import shutil\n", "import anndata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting Data\n", "\n", "Once you are happy with your analysis, you will likely want to export the results. The easiest way to do this is by using the `zarr` format, but `csv`, `anndata`, and `spatialdata` are also supported." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:               (cells: 56, cells_2: 56, channels: 5, y: 101, x: 101,\n",
       "                           la_features: 2, labels: 4, la_props: 2,\n",
       "                           neighborhoods: 5, nh_props: 2, features: 6)\n",
       "Coordinates:\n",
       "  * cells                 (cells) int64 448B 1 2 3 4 5 6 7 ... 51 52 53 54 55 56\n",
       "  * cells_2               (cells_2) int64 448B 1 2 3 4 5 6 ... 51 52 53 54 55 56\n",
       "  * channels              (channels) <U11 220B 'DAPI' 'PAX5' 'CD3' 'CD4' 'CD8'\n",
       "  * features              (features) <U14 336B 'CD4_binarized' ... 'centroid-1'\n",
       "  * la_features           (la_features) object 16B 'labels_0' 'labels_1'\n",
       "  * la_props              (la_props) <U6 48B '_color' '_name'\n",
       "  * labels                (labels) int64 32B 1 2 3 4\n",
       "  * neighborhoods         (neighborhoods) int64 40B 1 2 3 4 5\n",
       "  * nh_props              (nh_props) <U6 48B '_color' '_name'\n",
       "  * x                     (x) int64 808B 1600 1601 1602 1603 ... 1698 1699 1700\n",
       "  * y                     (y) int64 808B 2100 2101 2102 2103 ... 2198 2199 2200\n",
       "Data variables:\n",
       "    _adjacency_matrix     (cells, cells_2) int64 25kB dask.array<chunksize=(56, 56), meta=np.ndarray>\n",
       "    _image                (channels, y, x) uint8 51kB dask.array<chunksize=(5, 101, 101), meta=np.ndarray>\n",
       "    _intensity            (cells, channels) float64 2kB dask.array<chunksize=(56, 5), meta=np.ndarray>\n",
       "    _la_layers            (cells, la_features) object 896B dask.array<chunksize=(56, 2), meta=np.ndarray>\n",
       "    _la_properties        (labels, la_props) object 64B dask.array<chunksize=(4, 2), meta=np.ndarray>\n",
       "    _neighborhoods        (cells, labels) float64 2kB dask.array<chunksize=(56, 4), meta=np.ndarray>\n",
       "    _nh_properties        (neighborhoods, nh_props) <U14 560B dask.array<chunksize=(5, 2), meta=np.ndarray>\n",
       "    _obs                  (cells, features) float64 3kB dask.array<chunksize=(56, 6), meta=np.ndarray>\n",
       "    _percentage_positive  (cells, channels) float64 2kB dask.array<chunksize=(56, 5), meta=np.ndarray>\n",
       "    _segmentation         (y, x) int64 82kB dask.array<chunksize=(101, 101), meta=np.ndarray>
" ], "text/plain": [ " Size: 171kB\n", "Dimensions: (cells: 56, cells_2: 56, channels: 5, y: 101, x: 101,\n", " la_features: 2, labels: 4, la_props: 2,\n", " neighborhoods: 5, nh_props: 2, features: 6)\n", "Coordinates:\n", " * cells (cells) int64 448B 1 2 3 4 5 6 7 ... 51 52 53 54 55 56\n", " * cells_2 (cells_2) int64 448B 1 2 3 4 5 6 ... 51 52 53 54 55 56\n", " * channels (channels) \n", " _image (channels, y, x) uint8 51kB dask.array\n", " _intensity (cells, channels) float64 2kB dask.array\n", " _la_layers (cells, la_features) object 896B dask.array\n", " _la_properties (labels, la_props) object 64B dask.array\n", " _neighborhoods (cells, labels) float64 2kB dask.array\n", " _nh_properties (neighborhoods, nh_props) \n", " _obs (cells, features) float64 3kB dask.array\n", " _percentage_positive (cells, channels) float64 2kB dask.array\n", " _segmentation (y, x) int64 82kB dask.array" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# loading a test file which we will export later\n", "# notice how easy it is to load the file from a zarr using xarray\n", "ds = xr.open_zarr(\"../../tests/test_files/ds_neighborhoods.zarr\")\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting to Zarr\n", "This is the easiest file format to work with. It allows you to store and load the xarray objects with a single line of code. It is highly recommended to call `drop_encoding()` before exporting to zarr. There are several open issues linked to encoding problems, and this is the easiest way to circumvent them. For more references, refer to these issues: [issue 1](https://github.com/pydata/xarray/issues/3476), [issue 2](https://github.com/pydata/xarray/issues/9037)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zarr_path = \"tmp.zarr\"\n", "\n", "# removing the zarr if it exists\n", "if os.path.exists(zarr_path):\n", " shutil.rmtree(zarr_path)\n", "\n", "# exporting as zarr\n", "ds.drop_encoding().to_zarr(\"tmp.zarr\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting Tables to CSV\n", "Let's say you want to export some tables as csvs. This can be done with pandas." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " CD4_binarized CD8_binarized _labels _neighborhoods centroid-0 \\\n", "1 0.0 0.0 B Neighborhood 1 2103.768519 \n", "2 0.0 1.0 T_tox Neighborhood 1 2103.857143 \n", "3 1.0 1.0 T_h Neighborhood 3 2104.837037 \n", "4 0.0 1.0 T_tox Neighborhood 3 2101.750000 \n", "5 0.0 1.0 B Neighborhood 3 2104.416058 \n", "\n", " centroid-1 \n", "1 1607.277778 \n", "2 1630.741071 \n", "3 1668.733333 \n", "4 1677.000000 \n", "5 1685.627737 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = ds.pp.get_layer_as_df(\"_obs\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [], "source": [ "# exporting as csv\n", "df.to_csv(\"tmp.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting to AnnData\n", "AnnData is a format used by scanpy, which can be useful to create interesting plots and downstream analyses. For this reason, you can export the xarray object as an AnnData object. Note that this object will only store the tabular data, but not the image or the segmentation layer." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 56 × 5\n", " obs: 'CD4_binarized', 'CD8_binarized', '_labels', '_neighborhoods', 'centroid-0', 'centroid-1'\n", " uns: '_labels_colors', 'label_colors'\n", " obsm: 'spatial'\n", " layers: 'percentage_positive'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# putting the expression matrix into an anndata object\n", "adata = ds.tl.convert_to_anndata(\n", " expression_matrix_key=\"_intensity\",\n", " additional_layers={\"percentage_positive\": \"_percentage_positive\"},\n", " additional_uns={\"label_colors\": \"_la_properties\"},\n", ")\n", "adata" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "... storing '_labels' as categorical\n", "... storing '_neighborhoods' as categorical\n" ] } ], "source": [ "# writing to disk as hdf5\n", "adata.write(\"tmp.h5ad\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting to SpatialData\n", "SpatialData is a data format which is commonly used for spatial omics analysis and combines the power of zarr with anndata. You can export to this data format as well." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/meyerben/meyerben/.conda/envs/tmp_env_3/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34mINFO \u001b[0m Transposing `data` of type: \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'dask.array.core.Array'\u001b[0m\u001b[1m>\u001b[0m to \u001b[1m(\u001b[0m\u001b[32m'c'\u001b[0m, \u001b[32m'y'\u001b[0m, \u001b[32m'x'\u001b[0m\u001b[1m)\u001b[0m. \n", "\u001b[34mINFO \u001b[0m Transposing `data` of type: \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'dask.array.core.Array'\u001b[0m\u001b[1m>\u001b[0m to \u001b[1m(\u001b[0m\u001b[32m'y'\u001b[0m, \u001b[32m'x'\u001b[0m\u001b[1m)\u001b[0m. \n" ] }, { "data": { "text/plain": [ "SpatialData object\n", "├── Images\n", "│ └── 'image': DataArray[cyx] (5, 101, 101)\n", "├── Labels\n", "│ └── 'segmentation': DataArray[yx] (101, 101)\n", "└── Tables\n", " └── 'table': AnnData (56, 5)\n", "with coordinate systems:\n", " ▸ 'global', with elements:\n", " image (Images), segmentation (Labels)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spatialdata_object = ds.tl.convert_to_spatialdata(expression_matrix_key=\"_intensity\")\n", "spatialdata_object" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34mINFO \u001b[0m The Zarr backing store has been changed from \u001b[3;35mNone\u001b[0m the new file path: tmp.zarr \n" ] } ], "source": [ "# storing as zarr file\n", "spatialdata_object.write(\"tmp.zarr\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing from Spatialdata\n", "\n", "In the example workflow, you have already seen how to read data from a tiff file. If you already have your data in `spatialdata` format, you can also read it in from there. Reading in the data like this will convert the data from `spatialdata` format to `xarray` format, so that you can use the `xarray` backend of `spatialproteomics`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "root_attr: multiscales\n", "root_attr: omero\n", "datasets [{'coordinateTransformations': [{'scale': [1.0, 1.0, 1.0], 'type': 'scale'}], 'path': '0'}]\n", "/home/meyerben/meyerben/.conda/envs/tmp_env_3/lib/python3.10/site-packages/zarr/creation.py:614: UserWarning: ignoring keyword argument 'read_only'\n", " compressor, fill_value = _kwargs_compat(compressor, fill_value, kwargs)\n", "resolution: 0\n", " - shape ('c', 'y', 'x') = (3, 768, 1024)\n", " - chunks = ['3', '768', '1024']\n", " - dtype = uint8\n", "root_attr: multiscales\n", "root_attr: omero\n", "Unsupported transform Identity , resetting coordinates for the spatialproteomics object.\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset> Size: 9MB\n",
       "Dimensions:        (channels: 3, y: 768, x: 1024, cells: 70, features: 2)\n",
       "Coordinates:\n",
       "  * channels       (channels) int64 24B 0 1 2\n",
       "  * y              (y) int64 6kB 0 1 2 3 4 5 6 7 ... 761 762 763 764 765 766 767\n",
       "  * x              (x) int64 8kB 0 1 2 3 4 5 6 ... 1018 1019 1020 1021 1022 1023\n",
       "  * cells          (cells) int64 560B 1 2 3 4 5 6 7 8 ... 64 65 66 67 68 69 70\n",
       "  * features       (features) <U10 80B 'centroid-0' 'centroid-1'\n",
       "Data variables:\n",
       "    _image         (channels, y, x) uint8 2MB dask.array<chunksize=(3, 768, 1024), meta=np.ndarray>\n",
       "    _segmentation  (y, x) int64 6MB 0 0 0 0 0 0 0 0 ... 69 69 69 69 69 69 69 69\n",
       "    _obs           (cells, features) float64 1kB 44.79 402.5 ... 736.5 890.5
" ], "text/plain": [ " Size: 9MB\n", "Dimensions: (channels: 3, y: 768, x: 1024, cells: 70, features: 2)\n", "Coordinates:\n", " * channels (channels) int64 24B 0 1 2\n", " * y (y) int64 6kB 0 1 2 3 4 5 6 7 ... 761 762 763 764 765 766 767\n", " * x (x) int64 8kB 0 1 2 3 4 5 6 ... 1018 1019 1020 1021 1022 1023\n", " * cells (cells) int64 560B 1 2 3 4 5 6 7 8 ... 64 65 66 67 68 69 70\n", " * features (features) \n", " _segmentation (y, x) int64 6MB 0 0 0 0 0 0 0 0 ... 69 69 69 69 69 69 69 69\n", " _obs (cells, features) float64 1kB 44.79 402.5 ... 736.5 890.5" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = sp.read_from_spatialdata(\"../../data/spatialdata_example.zarr\", image_key=\"raccoon\")\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting to Seurat\n", "\n", "`Seurat` is widely used in the R community to analyze spatial omics data. Here, we show how you can create a `Seurat` object from a `spatialproteomics` one. It is most sensible to store the data frame to disk first, and then read it in with R. For demonstration purposes, we run R directly in this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "# setup and data loading\n", "import os\n", "import sys\n", "\n", "# setup for rpy2 to use the correct R binary\n", "env_r_bin = \"/g/huber/users/meyerben/.conda/envs/tmp_env_3/bin\"\n", "os.environ[\"PATH\"] = env_r_bin + os.pathsep + os.environ.get(\"PATH\", \"\")\n", "if \"R_HOME\" in os.environ:\n", " del os.environ[\"R_HOME\"]\n", "\n", "%load_ext rpy2.ipython\n", "\n", "import xarray as xr\n", "import spatialproteomics as sp\n", "\n", "celltype_colors = {\n", " \"B cell\": \"#5799d1\",\n", " \"T cell\": \"#ebc850\",\n", " \"Myeloid cell\": \"#de6866\",\n", " \"Dendritic cell\": \"#4cbcbd\",\n", " \"Macrophage\": \"#bb7cb4\",\n", " \"Stromal cell\": \"#62b346\",\n", " \"Endothelial cell\": \"#bf997d\",\n", "}\n", "\n", "# loading in a data set\n", "ds = xr.open_zarr(\"../../data/LN_24_1.zarr\")\n", "# for clearer visualizations, we set the cell types to the broadest level (B cells, T cells, ...)\n", "ds = ds.la.set_label_level(\"labels_0\", ignore_neighborhoods=True).la.set_label_colors(\n", " celltype_colors.keys(), celltype_colors.values()\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# this gives us a data frame of cells by markers\n", "intensity_df = ds.pp.get_layer_as_df(\"_intensity\")\n", "\n", "# getting the spatial information and cell types from the spatialproteomics object\n", "spatial_df = ds.pp.get_layer_as_df()[[\"centroid-0\", \"centroid-1\", \"_labels\"]]\n", "# renaming the columns for better compatibility with R\n", "spatial_df.columns = [\"centroid.0\", \"centroid.1\", \"labels\"]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "# send the data to R, so that we can use it directly\n", "%R -i intensity_df\n", "%R -i spatial_df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " WARNING: The R package \"reticulate\" only fixed recently\n", " an issue that caused a segfault when used with rpy2:\n", " https://github.com/rstudio/reticulate/pull/1188\n", " Make sure that you use a version of that package that includes\n", " the fix.\n", " " ] }, { "data": { "text/plain": [ "Loading required package: SeuratObject\n", "Loading required package: sp\n", "‘SeuratObject’ was built with package ‘Matrix’ 1.7.3 but the current\n", "version is 1.7.4; it is recomended that you reinstall ‘SeuratObject’ as\n", "the ABI for ‘Matrix’ may have changed\n", "\n", "Attaching package: ‘SeuratObject’\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, t\n", "\n", "\n", "Attaching package: ‘dplyr’\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "Warning: Data is of class matrix. Coercing to dgCMatrix.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(Seurat)\n", "library(dplyr)\n", "\n", "# intensity_df: each row = cell, each col = marker\n", "# transpose so markers become \"genes\"\n", "mat <- t(as.matrix(intensity_df))\n", "\n", "# Make sure row/col names align\n", "colnames(mat) <- rownames(intensity_df)\n", "\n", "# Create Seurat object\n", "seu <- CreateSeuratObject(counts = mat)\n", "\n", "# Add metadata (spatial info + cell labels)\n", "seu <- AddMetaData(seu, metadata = spatial_df)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [], "source": [ "%%R\n", "# Make sure the rownames exactly match the Seurat cells\n", "spatial_coords <- spatial_df[, c(\"centroid.0\", \"centroid.1\")]\n", "\n", "# The cell names in the Seurat object\n", "cell_names <- Cells(seu)\n", "\n", "# Assign rownames\n", "rownames(spatial_coords) <- cell_names\n", "colnames(spatial_coords) <- c(1, 2)\n", "\n", "# Convert to matrix\n", "embeddings_mat <- as.matrix(spatial_coords)\n", "\n", "# Now create the spatial reduction\n", "seu[[\"spatial\"]] <- CreateDimReducObject(\n", " embeddings = embeddings_mat,\n", " key = \"spatial_\",\n", " assay = DefaultAssay(seu)\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Learn more about the underlying theory at https://ggplot2-book.org/\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(ggplot2)\n", "\n", "p <- DimPlot(\n", " seu,\n", " reduction = \"spatial\",\n", " group.by = \"labels\",\n", " pt.size = 1\n", ") + coord_fixed() # ensures x and y axes have equal scale\n", "\n", "print(p)" ] } ], "metadata": { "kernelspec": { "display_name": "tmp_env_3", "language": "python", "name": "tmp_env_3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" } }, "nbformat": 4, "nbformat_minor": 4 }