docs/notebooks/anndata_getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6a75c19-d507-41d5-af0a-577dc7a9c3eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import anndata\n",
    "import scanpy as sc\n",
    "import mudata as md\n",
    "from scipy import sparse\n",
    "import pooch\n",
    "import matplotlib.pyplot as plt\n",
    "import pooch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abce77a-3698-4c96-8641-91d761ee484c",
   "metadata": {},
   "source": [
    "# Getting started with the `anndata` package"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d162005-d62e-436f-a531-6c36aada4783",
   "metadata": {},
   "source": [
    "Suppose a colleague of yours did some single cell data analysis in Python and Scanpy, saving the results in an `AnnData` object and sending it to you in a `*.h5ad` file. This tutorial will walk you through that file and help you explore its structure and content --- even if you are new to anndata, Scanpy or Python.\n",
    "\n",
    "In this tutorial, we will refer to anndata as the package, and to its data structures as `AnnData` objects, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6771e977-ba4b-4843-97a9-00c4f9c1c02c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exploring the `AnnData` object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cb735c-de5a-443d-80f7-cb3ca64f4a9e",
   "metadata": {},
   "source": [
    "We will use a preprocessed PBMC data set for our example. It is hosted on figshare and will be downloaded using the `pooch` package. The following line downloads the file `scverse-getting-started-anndata-pbmc3k_processed.h5ad` into `./data` (ca. 100MB)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9d8e791-8132-42f1-9f2b-71f31e13d8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://figshare.com/ndownloader/files/40067737' to file '/home/runner/work/scverse-tutorials/scverse-tutorials/docs/notebooks/data/scverse-getting-started-anndata-pbmc3k_processed.h5ad'.\n"
     ]
    }
   ],
   "source": [
    "datapath = pooch.retrieve(\n",
    "    url=\"https://figshare.com/ndownloader/files/40067737\",\n",
    "    known_hash=\"md5:b80deb0997f96b45d06f19c694e46243\",\n",
    "    path=\"./data\",\n",
    "    fname=\"scverse-getting-started-anndata-pbmc3k_processed.h5ad\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c79a15-2f85-4781-b65b-4e957d7d65b0",
   "metadata": {},
   "source": [
    "Now we can open it: Given a path to a `*.h5ad` file, `read_h5ad` will load the file into python and create our first `AnnData` object `adata`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c47dc3f9-2d83-49f0-8212-48c546361de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = anndata.read_h5ad(datapath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf0da32f-5e03-43a5-8e8f-d6f82b9ba15d",
   "metadata": {},
   "source": [
    "(To load any `*.h5ad`file that already is on you machine, you would use `adata = anndata.read_h5ad('path/to/file.h5ad')`)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f68e54-0708-4f29-b6aa-e962c539155a",
   "metadata": {},
   "source": [
    "Let's have a first look at the `AnnData` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c38f8888-05e9-414b-9c11-2e31fcca575e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2638 × 11505\n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain_cell_types'\n",
       "    var: 'gene_names', 'n_cells', 'gene_ids'\n",
       "    uns: 'louvain', 'louvain_colors', 'pca'\n",
       "    obsm: 'X_pca', 'X_tsne', 'X_umap'\n",
       "    layers: 'raw'\n",
       "    obsp: 'distances_all'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38597996-c348-4768-ac4a-98c056ba0e96",
   "metadata": {},
   "source": [
    "This output tells us some general things about the data: its shape (2638 cells, 13714 genes) and which metadata fields and subfields it has. We will go through those one-by-one - let's start with the most important one:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d2154e-a632-4b28-b5af-2169a78e2e90",
   "metadata": {
    "tags": []
   },
   "source": [
    "### The *active* data matrix in `adata.X`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a22e18e-c775-4693-a7bf-9dbfb9261ae2",
   "metadata": {},
   "source": [
    "![Active data matrix X](img/X.png \"Active data matrix X\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd17ddf2-6000-4ef3-a779-30a1408da10c",
   "metadata": {},
   "source": [
    "The `adata.X` slot holds the data matrix that we are actively using for analysis (e.g. for plotting gene expression values). That also means that most functions will use this data as input by default, unless explicitly told otherwise.\n",
    "\n",
    "In this example, `adata.X` holds the matrix of normalized and log(1+x)-transformed mRNA counts. Its **rows are cells** (or observations), and its  **columns are genes** (or variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e4bf47d-4927-40d5-b8d3-f47a9a7ce897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2638x11505 sparse matrix of type '<class 'numpy.float32'>'\n",
       "\twith 2076576 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1bc809-b8f6-48a8-9a2b-626644f62799",
   "metadata": {},
   "source": [
    "We see that the data matrix is stored in the [scipy sparse matrix format](https://docs.scipy.org/doc/scipy/reference/sparse.html) for compression. In brief, that means that we only store the non-zero expression values and their coordinates/indices in the data matrix explicitly, knowing that all other values are zeros. This saves a lot of RAM and makes processing faster, but also changes how we can interact with this object compared to a \"normal\" dense array. For an overview of the functions that you can apply directly on sparse arrays, check the [scipy.sparse docs for the csr array](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_array.html#scipy.sparse.csr_array). \n",
    "\n",
    "Here are some examples to show how the sparse format works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5ab77ab-6c9b-483e-8c95-cd3f529a6bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6496621 0.6496621 1.0402015 ... 0.7506172 0.7506172 1.4713064]\n",
      "[   19    52    58 ... 11498 11501 11504]\n",
      "0.068420527186156\n"
     ]
    }
   ],
   "source": [
    "# look at the non-zero values in the data\n",
    "print(adata.X.data)\n",
    "# look at their indices / positions in the data matrix\n",
    "print(adata.X.indices)\n",
    "# compute the fraction of non-zero entries\n",
    "print(adata.X.nnz / np.product(adata.X.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32fd4d1-811d-465e-bada-ed8c617107a9",
   "metadata": {},
   "source": [
    "### Alternative versions of the active data in `layers`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ef75d0-a17e-4fc0-b427-485d5d0a36a8",
   "metadata": {},
   "source": [
    "![Additional data layers with the same shape as X](img/layers.png \"Additional data layers with the same shape as X\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4011d8d3-ae58-4963-9431-1b37e8168f55",
   "metadata": {},
   "source": [
    "The data representation that we want to use mostly for plotting and processing lives in `adata.X`, e.g. the normalized counts. In addition to that, we often want to keep additional versions of our data around, e.g. the raw counts. These versions must have the exact same shape as `adata.X` and can be found in `layers`. Technically, `layers` is a [Python dictionary](https://docs.python.org/3/tutorial/datastructures.html#dictionaries) and stores each layer under a separate key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "731e14be-0207-40e3-a12f-57f6050aa1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Layers with keys: raw"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d98d01-7726-4df4-a791-5df5def933ce",
   "metadata": {},
   "source": [
    "Our data only has a single additional layer, the raw counts, accessible via the layer key `'raw'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc1fa576-38f7-4af9-9495-3e03ec3b98e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2638x11505 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 2076576 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.layers[\"raw\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87bb2d1-219e-46ac-b00a-2bafbd7291d6",
   "metadata": {},
   "source": [
    "Its also a sparse matrix with the same shape as the active data in `adata.X`, but with integer values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bea90a72-6183-419b-bbb0-1cd8df65ed3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, ..., 1, 1, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.layers[\"raw\"].data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34109373-d262-46c7-9304-612e50ce1c29",
   "metadata": {},
   "source": [
    "Now let's add a new layer, for example \"counts per million\"-normalized counts. We can compute it from the `raw` layer and store it in `layers` with a new name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e585833f-465d-46e1-9cf7-7f1c135e827e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layers with keys: raw, counts_per_million\n"
     ]
    }
   ],
   "source": [
    "# copy raw counts to new layer\n",
    "adata.layers[\"counts_per_million\"] = adata.layers[\"raw\"].copy()\n",
    "# normalize the new layer to counts-per-million\n",
    "sc.pp.normalize_total(adata, target_sum=10**6, layer=\"counts_per_million\")\n",
    "print(adata.layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83cc502-0568-452f-92ba-a66aa9e5477f",
   "metadata": {},
   "source": [
    "Also note that we had to explicitly tell the normalization function from Scanpy to use the new `counts_per_million` layer as input and output! Many functions in the scverse ecosystem support this through using the `layer` argument - for example the following plotting function from `scanpy`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7c8d501-d9a1-4d97-88aa-465885437e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x250 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 2.5))\n",
    "genes_of_interest = [\"CD8A\", \"CD4\", \"KLRB1\"]\n",
    "sc.pl.matrixplot(\n",
    "    adata,\n",
    "    groupby=\"louvain_cell_types\",\n",
    "    var_names=genes_of_interest,\n",
    "    layer=\"counts_per_million\",  ## set which layer to plot\n",
    "    ax=ax1,\n",
    "    show=False,\n",
    ")\n",
    "ax1.set_title(\"CPM normalization\")\n",
    "\n",
    "sc.pl.matrixplot(\n",
    "    adata,\n",
    "    groupby=\"louvain_cell_types\",\n",
    "    var_names=genes_of_interest,\n",
    "    layer=\"raw\",  ## set which layer to plot\n",
    "    ax=ax2,\n",
    "    show=False,\n",
    ")\n",
    "ax2.set_title(\"raw counts\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4b70d0-08a8-4100-b67d-9927bf69b194",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Simple annotations for cells and genes in `obs` and `var`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c75ccd0-7243-4f01-b4b1-3b090a17dcaf",
   "metadata": {},
   "source": [
    "We usually want to store annotations for each cell and gene in `adata.X`. For cells, this can be the cluster assignments, batch IDs or a quality metric; for genes, it might be the gene name, ensembl ID or some measure of variance. \n",
    "\n",
    "In `AnnData` objects, this information lives in `adata.obs` for cell annotations - each of its rows correspond to a row / cell in `adata.X`. Similarly, `var` holds the gene annotations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73756a11-8ade-49bc-83c7-9863af3a2fd6",
   "metadata": {},
   "source": [
    "![Simple annotations for cells and genes](img/obsvar.png \"Simple annotations for cells and genes in `obs` and `var`\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0f5d4ed-ad01-4b11-a249-351ba016735b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_genes</th>\n",
       "      <th>percent_mito</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain_cell_types</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACATACAACCAC-1</th>\n",
       "      <td>781</td>\n",
       "      <td>0.030178</td>\n",
       "      <td>2419.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGAGCTAC-1</th>\n",
       "      <td>1352</td>\n",
       "      <td>0.037936</td>\n",
       "      <td>4903.0</td>\n",
       "      <td>B cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGATCAGC-1</th>\n",
       "      <td>1131</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGCTTCCG-1</th>\n",
       "      <td>960</td>\n",
       "      <td>0.017431</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGTATGCG-1</th>\n",
       "      <td>522</td>\n",
       "      <td>0.012245</td>\n",
       "      <td>980.0</td>\n",
       "      <td>NK cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACTCTCAT-1</th>\n",
       "      <td>1155</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>3459.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTGAGGCA-1</th>\n",
       "      <td>1227</td>\n",
       "      <td>0.009294</td>\n",
       "      <td>3443.0</td>\n",
       "      <td>B cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTTCCTCG-1</th>\n",
       "      <td>622</td>\n",
       "      <td>0.021971</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>B cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGAGAGGC-1</th>\n",
       "      <td>454</td>\n",
       "      <td>0.020548</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>B cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGCCTCAC-1</th>\n",
       "      <td>724</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2638 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  n_genes  percent_mito  n_counts louvain_cell_types\n",
       "cell_barcode                                                        \n",
       "AAACATACAACCAC-1      781      0.030178    2419.0        CD4 T cells\n",
       "AAACATTGAGCTAC-1     1352      0.037936    4903.0            B cells\n",
       "AAACATTGATCAGC-1     1131      0.008897    3147.0        CD4 T cells\n",
       "AAACCGTGCTTCCG-1      960      0.017431    2639.0    CD14+ Monocytes\n",
       "AAACCGTGTATGCG-1      522      0.012245     980.0           NK cells\n",
       "...                   ...           ...       ...                ...\n",
       "TTTCGAACTCTCAT-1     1155      0.021104    3459.0    CD14+ Monocytes\n",
       "TTTCTACTGAGGCA-1     1227      0.009294    3443.0            B cells\n",
       "TTTCTACTTCCTCG-1      622      0.021971    1684.0            B cells\n",
       "TTTGCATGAGAGGC-1      454      0.020548    1022.0            B cells\n",
       "TTTGCATGCCTCAC-1      724      0.008065    1984.0        CD4 T cells\n",
       "\n",
       "[2638 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dccdd3f-0796-4a64-9437-add6ff36d47b",
   "metadata": {},
   "source": [
    "This table is a [Pandas Data Frame](https://pandas.pydata.org/). We can see from its columns that we currently have four cell annotations:\n",
    "- the number of genes per cell\n",
    "- fraction of counts from mitochondrial genes in each cell (a quality metric)\n",
    "- total counts per cell\n",
    "- cell type assignment of a louvain clustering\n",
    "\n",
    "All of these annotations are one-dimensional - they contain a single number or string per cell. Also, each cell is uniquely identified by the index of the table - in this case, the cell barcoding sequence (more about indexing in the next section)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f15d76a-ec42-418d-bf73-88c3c34a69f2",
   "metadata": {},
   "source": [
    "To get a list of all cell annotations in the `obs` columns, you can use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf294fa2-65ef-4bbd-a0c5-319c823caeb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['n_genes', 'percent_mito', 'n_counts', 'louvain_cell_types']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs_keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256c32a4-efda-4cf8-9a3c-c94bb927ce2b",
   "metadata": {},
   "source": [
    "To access a specific column of the `obs` data frame, we can simply use its column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "813dbc8c-7f46-4ab1-81b5-c063bffdc407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_barcode\n",
       "AAACATACAACCAC-1        CD4 T cells\n",
       "AAACATTGAGCTAC-1            B cells\n",
       "AAACATTGATCAGC-1        CD4 T cells\n",
       "AAACCGTGCTTCCG-1    CD14+ Monocytes\n",
       "AAACCGTGTATGCG-1           NK cells\n",
       "                         ...       \n",
       "TTTCGAACTCTCAT-1    CD14+ Monocytes\n",
       "TTTCTACTGAGGCA-1            B cells\n",
       "TTTCTACTTCCTCG-1            B cells\n",
       "TTTGCATGAGAGGC-1            B cells\n",
       "TTTGCATGCCTCAC-1        CD4 T cells\n",
       "Name: louvain_cell_types, Length: 2638, dtype: category\n",
       "Categories (8, object): ['CD4 T cells', 'CD14+ Monocytes', 'B cells', 'CD8 T cells', 'NK cells', 'FCGR3A+ Monocytes', 'Dendritic cells', 'Megakaryocytes']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs[\"louvain_cell_types\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b7e5c5-b4e4-48f3-b381-c60c76973984",
   "metadata": {},
   "source": [
    "Now, we can directly interact with these annotations, e.g. count the numbers of B cells in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e6cb4c7a-ff09-4e26-a3e0-868621609c10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "342\n"
     ]
    }
   ],
   "source": [
    "print(sum(adata.obs[\"louvain_cell_types\"] == \"B cells\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c5b082-1bad-4eb8-a973-841d418b47fc",
   "metadata": {},
   "source": [
    "To add a new cell annotations, we simply add a new column to the `obs` data frame.\n",
    "\n",
    "For example, if we want to keep track of low-quality cells with a high mitochondrial gene count fraction, we can do the following to mark all cells above a certain threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55450943-a9d5-4334-9f3c-ee2c25c76be5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_genes</th>\n",
       "      <th>percent_mito</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain_cell_types</th>\n",
       "      <th>is_low_quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACATACAACCAC-1</th>\n",
       "      <td>781</td>\n",
       "      <td>0.030178</td>\n",
       "      <td>2419.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGAGCTAC-1</th>\n",
       "      <td>1352</td>\n",
       "      <td>0.037936</td>\n",
       "      <td>4903.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGATCAGC-1</th>\n",
       "      <td>1131</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGCTTCCG-1</th>\n",
       "      <td>960</td>\n",
       "      <td>0.017431</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGTATGCG-1</th>\n",
       "      <td>522</td>\n",
       "      <td>0.012245</td>\n",
       "      <td>980.0</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACTCTCAT-1</th>\n",
       "      <td>1155</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>3459.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTGAGGCA-1</th>\n",
       "      <td>1227</td>\n",
       "      <td>0.009294</td>\n",
       "      <td>3443.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTTCCTCG-1</th>\n",
       "      <td>622</td>\n",
       "      <td>0.021971</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGAGAGGC-1</th>\n",
       "      <td>454</td>\n",
       "      <td>0.020548</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGCCTCAC-1</th>\n",
       "      <td>724</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2638 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  n_genes  percent_mito  n_counts louvain_cell_types  \\\n",
       "cell_barcode                                                           \n",
       "AAACATACAACCAC-1      781      0.030178    2419.0        CD4 T cells   \n",
       "AAACATTGAGCTAC-1     1352      0.037936    4903.0            B cells   \n",
       "AAACATTGATCAGC-1     1131      0.008897    3147.0        CD4 T cells   \n",
       "AAACCGTGCTTCCG-1      960      0.017431    2639.0    CD14+ Monocytes   \n",
       "AAACCGTGTATGCG-1      522      0.012245     980.0           NK cells   \n",
       "...                   ...           ...       ...                ...   \n",
       "TTTCGAACTCTCAT-1     1155      0.021104    3459.0    CD14+ Monocytes   \n",
       "TTTCTACTGAGGCA-1     1227      0.009294    3443.0            B cells   \n",
       "TTTCTACTTCCTCG-1      622      0.021971    1684.0            B cells   \n",
       "TTTGCATGAGAGGC-1      454      0.020548    1022.0            B cells   \n",
       "TTTGCATGCCTCAC-1      724      0.008065    1984.0        CD4 T cells   \n",
       "\n",
       "                  is_low_quality  \n",
       "cell_barcode                      \n",
       "AAACATACAACCAC-1            True  \n",
       "AAACATTGAGCTAC-1            True  \n",
       "AAACATTGATCAGC-1           False  \n",
       "AAACCGTGCTTCCG-1           False  \n",
       "AAACCGTGTATGCG-1           False  \n",
       "...                          ...  \n",
       "TTTCGAACTCTCAT-1           False  \n",
       "TTTCTACTGAGGCA-1           False  \n",
       "TTTCTACTTCCTCG-1           False  \n",
       "TTTGCATGAGAGGC-1           False  \n",
       "TTTGCATGCCTCAC-1           False  \n",
       "\n",
       "[2638 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs[\"is_low_quality\"] = adata.obs[\"percent_mito\"] > 0.03\n",
    "adata.obs  # to inspect the updated table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c528080-cec9-4139-bb53-1c42c2cba548",
   "metadata": {},
   "source": [
    "The gene annotations in the `var` data frame work in the same way: Here, each row is a gene and in this example, the gene name is used as index. Additionally, the ensembl gene ID is available for each gene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0dd8e0d9-ba21-447c-bd7e-7a014967f306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_names</th>\n",
       "      <th>n_cells</th>\n",
       "      <th>gene_ids</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_names</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LINC00115</th>\n",
       "      <td>LINC00115</td>\n",
       "      <td>18</td>\n",
       "      <td>ENSG00000225880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOC2L</th>\n",
       "      <td>NOC2L</td>\n",
       "      <td>258</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLHL17</th>\n",
       "      <td>KLHL17</td>\n",
       "      <td>9</td>\n",
       "      <td>ENSG00000187961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLEKHN1</th>\n",
       "      <td>PLEKHN1</td>\n",
       "      <td>7</td>\n",
       "      <td>ENSG00000187583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HES4</th>\n",
       "      <td>HES4</td>\n",
       "      <td>145</td>\n",
       "      <td>ENSG00000188290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND4L</th>\n",
       "      <td>MT-ND4L</td>\n",
       "      <td>398</td>\n",
       "      <td>ENSG00000212907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND4</th>\n",
       "      <td>MT-ND4</td>\n",
       "      <td>2588</td>\n",
       "      <td>ENSG00000198886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND5</th>\n",
       "      <td>MT-ND5</td>\n",
       "      <td>1399</td>\n",
       "      <td>ENSG00000198786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND6</th>\n",
       "      <td>MT-ND6</td>\n",
       "      <td>249</td>\n",
       "      <td>ENSG00000198695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-CYB</th>\n",
       "      <td>MT-CYB</td>\n",
       "      <td>2517</td>\n",
       "      <td>ENSG00000198727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11505 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_names  n_cells         gene_ids\n",
       "gene_names                                     \n",
       "LINC00115   LINC00115       18  ENSG00000225880\n",
       "NOC2L           NOC2L      258  ENSG00000188976\n",
       "KLHL17         KLHL17        9  ENSG00000187961\n",
       "PLEKHN1       PLEKHN1        7  ENSG00000187583\n",
       "HES4             HES4      145  ENSG00000188290\n",
       "...               ...      ...              ...\n",
       "MT-ND4L       MT-ND4L      398  ENSG00000212907\n",
       "MT-ND4         MT-ND4     2588  ENSG00000198886\n",
       "MT-ND5         MT-ND5     1399  ENSG00000198786\n",
       "MT-ND6         MT-ND6      249  ENSG00000198695\n",
       "MT-CYB         MT-CYB     2517  ENSG00000198727\n",
       "\n",
       "[11505 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874ab451-d2bd-4854-89da-d6a68a9b808d",
   "metadata": {},
   "source": [
    "If you want to learn how to interact with the `obs` and `var` data frames in more advanced ways, you can have a look at these [Pandas tutorials](https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909c91fa-abc2-4aae-8fcb-6d67d5cb419b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Index cells and genes with `obs_names` and `var_names`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0479d4-5ee9-4166-90c0-961f68bd8a96",
   "metadata": {},
   "source": [
    "One important aspect of `AnnData` objects is the shared indexing. Consider the case of cell annotations - no matter if we look at a cells raw counts, its cluster assignments or its k-nearest neighbors in the PCA space: we always want to be able to refer to the same cell with the same \"name\". That is why all cell annotations share the same index, the `obs_names`. Similarly, we have `var_names` indexing our genes and their annotations. These annotations should be unique and many functions in the scverse ecosystem will raise warnings if this is not the case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1b63d8-065f-43f9-beae-daca95119234",
   "metadata": {},
   "source": [
    "![Shared indexing for cells and genes](img/names.png \"Shared indexing for cells and genes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931cc165-c1b1-4b98-8577-1be2d2f18fad",
   "metadata": {},
   "source": [
    "For the cells in our example data, the cell barcode from sequencing is used as index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0503e978-afce-438c-bbd9-94e1b695de9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AAACATACAACCAC-1', 'AAACATTGAGCTAC-1', 'AAACATTGATCAGC-1',\n",
       "       'AAACCGTGCTTCCG-1', 'AAACCGTGTATGCG-1', 'AAACGCACTGGTAC-1',\n",
       "       'AAACGCTGACCAGT-1', 'AAACGCTGGTTCTT-1', 'AAACGCTGTAGCCA-1',\n",
       "       'AAACGCTGTTTCTG-1',\n",
       "       ...\n",
       "       'TTTCAGTGTCACGA-1', 'TTTCAGTGTCTATC-1', 'TTTCAGTGTGCAGT-1',\n",
       "       'TTTCCAGAGGTGAG-1', 'TTTCGAACACCTGA-1', 'TTTCGAACTCTCAT-1',\n",
       "       'TTTCTACTGAGGCA-1', 'TTTCTACTTCCTCG-1', 'TTTGCATGAGAGGC-1',\n",
       "       'TTTGCATGCCTCAC-1'],\n",
       "      dtype='object', name='cell_barcode', length=2638)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b9305a-7a2c-4051-8121-7d0e482a039f",
   "metadata": {},
   "source": [
    "Genes are indexed by gene name in this example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a752130-d613-4e3b-8369-e5ea88ad3497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LINC00115', 'NOC2L', 'KLHL17', 'PLEKHN1', 'HES4', 'ISG15', 'AGRN',\n",
       "       'C1orf159', 'TNFRSF18', 'TNFRSF4',\n",
       "       ...\n",
       "       'MT-CO2', 'MT-ATP8', 'MT-ATP6', 'MT-CO3', 'MT-ND3', 'MT-ND4L', 'MT-ND4',\n",
       "       'MT-ND5', 'MT-ND6', 'MT-CYB'],\n",
       "      dtype='object', name='gene_names', length=11505)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85636af1-7344-4574-be9d-41e270ebe5e0",
   "metadata": {},
   "source": [
    "If needed, we can change the way cells or genes are indexed by using a different index array for `obs_names` or `var_names`.  For that, we just need to set them to a new array of indices! For example, we can use the gene IDs instead of the gene name to index the genes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9ce70f6-fb23-4a01-92ea-adf6bf31c516",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var_names = adata.var[\"gene_ids\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb034e6d-951f-4ee0-a561-ef9522f096b7",
   "metadata": {},
   "source": [
    "As `adata.var_names` directly mirrors the index of `adata.var`, the index changes as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6e8449f-a9a4-4d9c-a2da-55f70050a54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_names</th>\n",
       "      <th>n_cells</th>\n",
       "      <th>gene_ids</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_ids</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000225880</th>\n",
       "      <td>LINC00115</td>\n",
       "      <td>18</td>\n",
       "      <td>ENSG00000225880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000188976</th>\n",
       "      <td>NOC2L</td>\n",
       "      <td>258</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000187961</th>\n",
       "      <td>KLHL17</td>\n",
       "      <td>9</td>\n",
       "      <td>ENSG00000187961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000187583</th>\n",
       "      <td>PLEKHN1</td>\n",
       "      <td>7</td>\n",
       "      <td>ENSG00000187583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000188290</th>\n",
       "      <td>HES4</td>\n",
       "      <td>145</td>\n",
       "      <td>ENSG00000188290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000212907</th>\n",
       "      <td>MT-ND4L</td>\n",
       "      <td>398</td>\n",
       "      <td>ENSG00000212907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198886</th>\n",
       "      <td>MT-ND4</td>\n",
       "      <td>2588</td>\n",
       "      <td>ENSG00000198886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198786</th>\n",
       "      <td>MT-ND5</td>\n",
       "      <td>1399</td>\n",
       "      <td>ENSG00000198786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198695</th>\n",
       "      <td>MT-ND6</td>\n",
       "      <td>249</td>\n",
       "      <td>ENSG00000198695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>MT-CYB</td>\n",
       "      <td>2517</td>\n",
       "      <td>ENSG00000198727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11505 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                gene_names  n_cells         gene_ids\n",
       "gene_ids                                            \n",
       "ENSG00000225880  LINC00115       18  ENSG00000225880\n",
       "ENSG00000188976      NOC2L      258  ENSG00000188976\n",
       "ENSG00000187961     KLHL17        9  ENSG00000187961\n",
       "ENSG00000187583    PLEKHN1        7  ENSG00000187583\n",
       "ENSG00000188290       HES4      145  ENSG00000188290\n",
       "...                    ...      ...              ...\n",
       "ENSG00000212907    MT-ND4L      398  ENSG00000212907\n",
       "ENSG00000198886     MT-ND4     2588  ENSG00000198886\n",
       "ENSG00000198786     MT-ND5     1399  ENSG00000198786\n",
       "ENSG00000198695     MT-ND6      249  ENSG00000198695\n",
       "ENSG00000198727     MT-CYB     2517  ENSG00000198727\n",
       "\n",
       "[11505 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7b2a6e-b3b9-4000-9d6b-d9411a885135",
   "metadata": {},
   "source": [
    "`AnnData` also automatically checks if the new index has the correct length - setting `var_names` with too few or too many indices will throw an error.\n",
    "\n",
    "For now, let's go back to the gene name index, as it is more interpretable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c85804e7-1078-499b-8305-0eee5624a4e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_names</th>\n",
       "      <th>n_cells</th>\n",
       "      <th>gene_ids</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_names</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LINC00115</th>\n",
       "      <td>LINC00115</td>\n",
       "      <td>18</td>\n",
       "      <td>ENSG00000225880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOC2L</th>\n",
       "      <td>NOC2L</td>\n",
       "      <td>258</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLHL17</th>\n",
       "      <td>KLHL17</td>\n",
       "      <td>9</td>\n",
       "      <td>ENSG00000187961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLEKHN1</th>\n",
       "      <td>PLEKHN1</td>\n",
       "      <td>7</td>\n",
       "      <td>ENSG00000187583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HES4</th>\n",
       "      <td>HES4</td>\n",
       "      <td>145</td>\n",
       "      <td>ENSG00000188290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND4L</th>\n",
       "      <td>MT-ND4L</td>\n",
       "      <td>398</td>\n",
       "      <td>ENSG00000212907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND4</th>\n",
       "      <td>MT-ND4</td>\n",
       "      <td>2588</td>\n",
       "      <td>ENSG00000198886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND5</th>\n",
       "      <td>MT-ND5</td>\n",
       "      <td>1399</td>\n",
       "      <td>ENSG00000198786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND6</th>\n",
       "      <td>MT-ND6</td>\n",
       "      <td>249</td>\n",
       "      <td>ENSG00000198695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-CYB</th>\n",
       "      <td>MT-CYB</td>\n",
       "      <td>2517</td>\n",
       "      <td>ENSG00000198727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11505 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_names  n_cells         gene_ids\n",
       "gene_names                                     \n",
       "LINC00115   LINC00115       18  ENSG00000225880\n",
       "NOC2L           NOC2L      258  ENSG00000188976\n",
       "KLHL17         KLHL17        9  ENSG00000187961\n",
       "PLEKHN1       PLEKHN1        7  ENSG00000187583\n",
       "HES4             HES4      145  ENSG00000188290\n",
       "...               ...      ...              ...\n",
       "MT-ND4L       MT-ND4L      398  ENSG00000212907\n",
       "MT-ND4         MT-ND4     2588  ENSG00000198886\n",
       "MT-ND5         MT-ND5     1399  ENSG00000198786\n",
       "MT-ND6         MT-ND6      249  ENSG00000198695\n",
       "MT-CYB         MT-CYB     2517  ENSG00000198727\n",
       "\n",
       "[11505 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var_names = adata.var[\"gene_names\"]\n",
    "adata.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fab3c9a-250e-4b6f-b91c-9d461e46b374",
   "metadata": {},
   "source": [
    "### Subsetting `AnnData` objects\n",
    "\n",
    "`AnnData` objects allow consistent subsetting of genes, cells or both.\n",
    "There are several options to subset:\n",
    " - by name index (=`var_names` and/or `obs_names`) - in our example that would be lists of gene names `['LYZ','TUBB1','MALAT1']` or cell barcodes `[AAACATACAACCAC-1', 'AAACATTGAGCTAC-1', 'AAACATTGATCAGC-1']`\n",
    " - by numerical index - e.g. `:10` or `0:10` for the first 10 gene/cell, `5:10` for the sixth to the tenth, or `[0,2,4]` for the first, third and fifth\n",
    " - by boolean index - e.g. our flag for low-quality cells `adata.obs['is_low_quality']` or an index to all cells in the B cell cluster as in  `adata.obs['louvain']=='B cells'`\n",
    " \n",
    "All of these index operations can be combined, and will automatically subset the active data in `adata.X`, all `layers`, and all annotation fields to the selected subset. For example, if we want to get the first five cells in the data for three specific genes we are interested in, `obs` and `var` will be subsetted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "617c35ee-dd0f-4bd0-85e2-eaef46a42997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_small = adata[:5, [\"LYZ\", \"FOS\", \"MALAT1\"]]\n",
    "adata_small.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e30ef45e-92c0-4282-8532-6d65e49de3c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6496621 , 2.4036813 , 3.8249342 ],\n",
       "       [0.8553989 , 0.64266497, 4.174533  ],\n",
       "       [0.878057  , 2.8014445 , 4.7918878 ],\n",
       "       [3.0494576 , 1.6464083 , 2.3237872 ],\n",
       "       [0.        , 0.        , 3.923651  ]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# active data (log-normalized counts) after subsetting\n",
    "adata_small.X.A  # we use .A just for display reasons here:\n",
    "# it turns the sparse data in X into a dense array that can be nicely printed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bcf5c64a-e04a-4ab8-a9a0-e5c6df10a6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,  11,  49],\n",
       "       [  3,   2, 142],\n",
       "       [  2,  22, 170],\n",
       "       [ 24,   5,  11],\n",
       "       [  0,   0,  22]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# raw data layer after subsetting\n",
    "adata_small.layers[\"raw\"].A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f4295f0-fe16-4f63-8f3d-95368d927daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_genes</th>\n",
       "      <th>percent_mito</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain_cell_types</th>\n",
       "      <th>is_low_quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACATACAACCAC-1</th>\n",
       "      <td>781</td>\n",
       "      <td>0.030178</td>\n",
       "      <td>2419.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGAGCTAC-1</th>\n",
       "      <td>1352</td>\n",
       "      <td>0.037936</td>\n",
       "      <td>4903.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGATCAGC-1</th>\n",
       "      <td>1131</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGCTTCCG-1</th>\n",
       "      <td>960</td>\n",
       "      <td>0.017431</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGTATGCG-1</th>\n",
       "      <td>522</td>\n",
       "      <td>0.012245</td>\n",
       "      <td>980.0</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  n_genes  percent_mito  n_counts louvain_cell_types  \\\n",
       "cell_barcode                                                           \n",
       "AAACATACAACCAC-1      781      0.030178    2419.0        CD4 T cells   \n",
       "AAACATTGAGCTAC-1     1352      0.037936    4903.0            B cells   \n",
       "AAACATTGATCAGC-1     1131      0.008897    3147.0        CD4 T cells   \n",
       "AAACCGTGCTTCCG-1      960      0.017431    2639.0    CD14+ Monocytes   \n",
       "AAACCGTGTATGCG-1      522      0.012245     980.0           NK cells   \n",
       "\n",
       "                  is_low_quality  \n",
       "cell_barcode                      \n",
       "AAACATACAACCAC-1            True  \n",
       "AAACATTGAGCTAC-1            True  \n",
       "AAACATTGATCAGC-1           False  \n",
       "AAACCGTGCTTCCG-1           False  \n",
       "AAACCGTGTATGCG-1           False  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cell annotations after subsetting\n",
    "adata_small.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84af3b9f-0fce-43ab-9e5e-848d8562a10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_names</th>\n",
       "      <th>n_cells</th>\n",
       "      <th>gene_ids</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_names</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LYZ</th>\n",
       "      <td>LYZ</td>\n",
       "      <td>1631</td>\n",
       "      <td>ENSG00000090382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOS</th>\n",
       "      <td>FOS</td>\n",
       "      <td>2473</td>\n",
       "      <td>ENSG00000170345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALAT1</th>\n",
       "      <td>MALAT1</td>\n",
       "      <td>2699</td>\n",
       "      <td>ENSG00000251562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_names  n_cells         gene_ids\n",
       "gene_names                                     \n",
       "LYZ               LYZ     1631  ENSG00000090382\n",
       "FOS               FOS     2473  ENSG00000170345\n",
       "MALAT1         MALAT1     2699  ENSG00000251562"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gene annotations after subsetting\n",
    "adata_small.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8092917d-421e-415e-8e4c-906308fb1c4b",
   "metadata": {},
   "source": [
    "Lets also try some boolean indexing, where we keep the gene axis unchanged (using `:`), and keeping only the high quality cells (by inverting `adata.obs['is_low_quality']` with the \"NOT\" operator `~`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7bb3936b-f919-404c-ae84-ea3df44f2993",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_high_quality = adata[~adata.obs[\"is_low_quality\"], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cfce12a5-7f7b-4879-98dd-f9213d118f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_genes</th>\n",
       "      <th>percent_mito</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain_cell_types</th>\n",
       "      <th>is_low_quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACATTGATCAGC-1</th>\n",
       "      <td>1131</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGCTTCCG-1</th>\n",
       "      <td>960</td>\n",
       "      <td>0.017431</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGTATGCG-1</th>\n",
       "      <td>522</td>\n",
       "      <td>0.012245</td>\n",
       "      <td>980.0</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCACTGGTAC-1</th>\n",
       "      <td>782</td>\n",
       "      <td>0.016644</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGTAGCCA-1</th>\n",
       "      <td>533</td>\n",
       "      <td>0.011765</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACTCTCAT-1</th>\n",
       "      <td>1155</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>3459.0</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTGAGGCA-1</th>\n",
       "      <td>1227</td>\n",
       "      <td>0.009294</td>\n",
       "      <td>3443.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTTCCTCG-1</th>\n",
       "      <td>622</td>\n",
       "      <td>0.021971</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGAGAGGC-1</th>\n",
       "      <td>454</td>\n",
       "      <td>0.020548</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGCCTCAC-1</th>\n",
       "      <td>724</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2257 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  n_genes  percent_mito  n_counts louvain_cell_types  \\\n",
       "cell_barcode                                                           \n",
       "AAACATTGATCAGC-1     1131      0.008897    3147.0        CD4 T cells   \n",
       "AAACCGTGCTTCCG-1      960      0.017431    2639.0    CD14+ Monocytes   \n",
       "AAACCGTGTATGCG-1      522      0.012245     980.0           NK cells   \n",
       "AAACGCACTGGTAC-1      782      0.016644    2163.0        CD8 T cells   \n",
       "AAACGCTGTAGCCA-1      533      0.011765    1275.0        CD4 T cells   \n",
       "...                   ...           ...       ...                ...   \n",
       "TTTCGAACTCTCAT-1     1155      0.021104    3459.0    CD14+ Monocytes   \n",
       "TTTCTACTGAGGCA-1     1227      0.009294    3443.0            B cells   \n",
       "TTTCTACTTCCTCG-1      622      0.021971    1684.0            B cells   \n",
       "TTTGCATGAGAGGC-1      454      0.020548    1022.0            B cells   \n",
       "TTTGCATGCCTCAC-1      724      0.008065    1984.0        CD4 T cells   \n",
       "\n",
       "                  is_low_quality  \n",
       "cell_barcode                      \n",
       "AAACATTGATCAGC-1           False  \n",
       "AAACCGTGCTTCCG-1           False  \n",
       "AAACCGTGTATGCG-1           False  \n",
       "AAACGCACTGGTAC-1           False  \n",
       "AAACGCTGTAGCCA-1           False  \n",
       "...                          ...  \n",
       "TTTCGAACTCTCAT-1           False  \n",
       "TTTCTACTGAGGCA-1           False  \n",
       "TTTCTACTTCCTCG-1           False  \n",
       "TTTGCATGAGAGGC-1           False  \n",
       "TTTGCATGCCTCAC-1           False  \n",
       "\n",
       "[2257 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obs data frame after subsettings shows that the low-quality cells are gone\n",
    "adata_high_quality.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62163e7c-f72d-44a8-952d-f0304ec204af",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    \n",
    "**Note:**\n",
    "\n",
    "Technically, subsetting `AnnData` objects returns a _view_ of the `AnnData` you are subsetting from instead of making a copy. That avoids having the same data in memory twice. We describe the details of how views work in a separate section below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487b004e-5ada-4e8a-90db-64001ecae705",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Multidimensional annotations for cells and genes in `obsm` and `varm`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "611fc307-305c-4ce1-a17e-fc438032a2f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['louvain', 'louvain_colors', 'pca'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.uns.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404b6e74-5453-4421-bfcb-83a6f3cf536d",
   "metadata": {},
   "source": [
    "Sometimes, the simple, one-dimensional annotations in `obs` and `var` are not enough to store the results of our analysis. \n",
    "\n",
    "For example, if we embed our data with t-SNE or UMAP, we usually obtain a two-dimensional vector of embedding coordinates for each cell. For a PCA with, say, 50 components, we would even get a 50-dimensional vector per cell. To store these multidimensional representations of the cells, `AnnData` objects provide the `obsm` object. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2931c3ca-e031-4ffd-a8b9-161d9292a85a",
   "metadata": {},
   "source": [
    "![Multidimensional information for cells and genes in `obsm` and `varm`](img/obsmvarm.png \"Multidimensional information for cells and genes in `obsm` and `varm`\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9abdc364-2c80-4503-a226-29093109966d",
   "metadata": {},
   "source": [
    "For multidimensional information about genes, we have `varm`, which works similarly and could e.g. hold the PCA loadings for each gene."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0fa56b3-ad3d-43dc-8569-fbb501076a9b",
   "metadata": {},
   "source": [
    "Now, let's have a look at our example `adata.obsm`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ad7ed9fc-f209-4def-9b22-0b87d18219e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AxisArrays with keys: X_pca, X_tsne, X_umap"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obsm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe68dff-b895-4609-8ad8-b87598029fb1",
   "metadata": {},
   "source": [
    "In our example, it contains representation of our cells after PCA, t-SNE and UMAP. Technically, these three representations are stored in a [Python dictionary](https://docs.python.org/3/tutorial/datastructures.html#dictionaries) with separate keys.\n",
    "\n",
    "We can look at the data shape for each of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ae67579-e51d-467c-925c-4183a214902b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_pca\t(2638, 50)\n",
      "X_tsne\t(2638, 2)\n",
      "X_umap\t(2638, 2)\n"
     ]
    }
   ],
   "source": [
    "for key in adata.obsm_keys():\n",
    "    print(key, adata.obsm[key].shape, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf64ff5-7465-4b5e-bd4b-5e231d6f185d",
   "metadata": {},
   "source": [
    "The numbers of observations always has to be the same as in `adata.X`, but the number of dimensions can vary.\n",
    "\n",
    "Also, let's make a simple plot of the the first two PCs from PCA and the UMAP / t-SNE embeddings, and highlight the B cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f2e35c19-4133-4598-a8cc-bd8b9461ad35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# PCA\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.scatter(\n",
    "    x=adata.obsm[\"X_pca\"][:, 0],  # PCA dim 1\n",
    "    y=adata.obsm[\"X_pca\"][:, 1],  # PCA dim 2\n",
    "    c=adata.obs[\"louvain_cell_types\"] == \"B cells\",  # B cell flag\n",
    "    s=3,\n",
    "    linewidth=0,\n",
    "    cmap=\"coolwarm\",\n",
    ")\n",
    "plt.title(\"PCA\")\n",
    "plt.axis(\"off\")\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "\n",
    "# t-SNE\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.scatter(\n",
    "    x=adata.obsm[\"X_tsne\"][:, 0],  # t-SNE dim 1\n",
    "    y=adata.obsm[\"X_tsne\"][:, 1],  # t-SNE dim 2\n",
    "    c=adata.obs[\"louvain_cell_types\"] == \"B cells\",  # B cell flag\n",
    "    s=3,\n",
    "    linewidth=0,\n",
    "    cmap=\"coolwarm\",\n",
    ")\n",
    "plt.title(\"t-SNE\")\n",
    "plt.axis(\"off\")\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "\n",
    "# UMAP\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.scatter(\n",
    "    x=adata.obsm[\"X_umap\"][:, 0],  # UMAP dim 1\n",
    "    y=adata.obsm[\"X_umap\"][:, 1],  # UMAP dim 2\n",
    "    c=adata.obs[\"louvain_cell_types\"] == \"B cells\",  # B cell flag\n",
    "    s=3,\n",
    "    linewidth=0,\n",
    "    cmap=\"coolwarm\",\n",
    ")\n",
    "plt.title(\"UMAP\")\n",
    "plt.axis(\"off\")\n",
    "plt.gca().set_aspect(\"equal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f365c6-02d8-463a-bd38-283447e6355f",
   "metadata": {},
   "source": [
    "Note: There is a function in Scanpy to produce such [embedding scatter plots](https://scanpy.readthedocs.io/en/stable/generated/scanpy.pl.embedding.html) automatically. We show the manual way here just to illustrate how the embeddings are stored in the `AnnData` object."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8da89c-c86a-41f5-b7ae-e9f8d6098d23",
   "metadata": {},
   "source": [
    "### Annotations for cell-cell and gene-gene pairs in `obsp` and `varp`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b03288-a53c-499c-9d24-db098ba1fe05",
   "metadata": {},
   "source": [
    "Many techniques in single cell data analysis consider pairs of cells or genes: For example, to construct a k-nearest neighbor graph of cells, we need to compute a distance matrix first. This matrix holds the distances between all pairs of cells. Similarly for genes, we might be interested of describing gene regulation networks with a matrix that quantifies for each gene-gene pair how strong they interact.\n",
    "\n",
    "To store these matrices, `AnnData` has the `obsp` field for pairs of cells, and `varp` for pairs of genes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fabe768-88f4-492d-8264-265a846e8081",
   "metadata": {},
   "source": [
    "![Multidimensional information for cells and genes in `obsm` and `varm`](img/obspvarp.png \"Multidimensional information for cells and genes in `obsm` and `varm`\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae28379-ce89-4004-b023-cdef54f8f575",
   "metadata": {},
   "source": [
    "Note that all matrices in `obsp` must have shape `(n_cells, n_cells)` and use `obs_names` as an index to both dimensions. Similarly, `varp` must be `(n_genes x n_genes)` and use `var_names` to index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf153a0-7b51-4d43-b2b5-f90ab9a9e82d",
   "metadata": {},
   "source": [
    "Our example data now holds the following cell-cell pair matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4129b6ae-74a9-483e-87ac-1358ef808cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PairwiseArrays with keys: distances_all"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obsp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e030fc-7d7e-4aef-bf29-143a3d936ed2",
   "metadata": {},
   "source": [
    "`distances_all` is the the full distance matrix between all pairs of cells.\n",
    "\n",
    "As before with `obsm` and `layers`, `obsp` is a [Python dictionary](https://docs.python.org/3/tutorial/datastructures.html#dictionaries), so we can access the distance matrix by its key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a13586a6-e80b-40d1-91ac-e82b8e91a53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , 18.98223389, 15.39625646, ..., 17.44791227,\n",
       "        19.66225537, 13.4530516 ],\n",
       "       [18.98223389,  0.        , 21.37320952, ..., 17.39917931,\n",
       "        16.59099551, 20.29010799],\n",
       "       [15.39625646, 21.37320952,  0.        , ..., 17.48199881,\n",
       "        19.54703132, 13.22466662],\n",
       "       ...,\n",
       "       [17.44791227, 17.39917931, 17.48199881, ...,  0.        ,\n",
       "        14.53959947, 14.15720293],\n",
       "       [19.66225537, 16.59099551, 19.54703132, ..., 14.53959947,\n",
       "         0.        , 16.96518854],\n",
       "       [13.4530516 , 20.29010799, 13.22466662, ..., 14.15720293,\n",
       "        16.96518854,  0.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obsp[\"distances_all\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c5f198-c589-4083-9e9a-8b9a348829f5",
   "metadata": {},
   "source": [
    "Lets have a closer look at the full distance matrix and plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cbc64a76-4b94-4d7d-83ca-3fbeca97024a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distance matrix\n",
    "plt.imshow(adata.obsp[\"distances_all\"])\n",
    "plt.colorbar(label=\"Euclidean distance in PCA space\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cabe685-2e70-4919-a8b2-4760173c1e0f",
   "metadata": {},
   "source": [
    "Here, we cannot see much structure. This is because the order of the cells is random! Therefore, let's re-order the cells such that they are sorted by cell type before plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "44a3fcf4-76ab-4089-935a-4e877cbe2d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate numerical index to sort cells by louvain cluster assignments\n",
    "reorder_by_celltype = np.argsort(adata.obs[\"louvain_cell_types\"])\n",
    "# same distance matrix plot as before, but with `adata` reordered!\n",
    "plt.imshow(adata[reorder_by_celltype, :].obsp[\"distances_all\"])\n",
    "plt.colorbar(label=\"Euclidean distance in PCA space\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2b202b-691b-408d-a85d-fe3f57873efa",
   "metadata": {},
   "source": [
    "Now, we can clearly see the block structure of the cell-cell distance matrix. \n",
    "\n",
    "In summary, the `obsp` and `varp` fields allow to hold any kind of cell-cell or gene-gene paired data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcca2de-7808-44b4-8698-867a97aaec05",
   "metadata": {},
   "source": [
    "### Unstructured annotations or general metadata in `uns`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c71dfa36-2662-41ed-a250-d40752d04529",
   "metadata": {},
   "source": [
    "Finally, there is only one subfield left! If we have metadata that does not fit any of the other fields or that describes the whole dataset and not specific cells or genes, we can store it as unstructured annotation in `adata.uns`:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fde7775-88f6-4fd6-a60b-7dfa512a6456",
   "metadata": {},
   "source": [
    "![Multidimensional information for cells and genes in `obsm` and `varm`](img/anndata_schema_full.png \"Multidimensional information for cells and genes in `obsm` and `varm`\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77214b66-1a13-4524-8766-02819eab6fc5",
   "metadata": {},
   "source": [
    "`adata.uns` is another Python dictionary, so we can access its contents via its keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "24fe77a7-d451-482c-b770-8fc8d8be6be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['louvain', 'louvain_colors', 'pca']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.uns_keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6542b32c-7fc2-453a-bbcb-e55fbee5f989",
   "metadata": {},
   "source": [
    "In our example, `uns` holds some processing parameters or intermediate results of the methods that were already run on the data. \n",
    "\n",
    "Let's look at the `pca` and `louvain` fields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a6cf9122-e7e4-4d0f-8150-d719a4c105e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'params': {'random_state': array([0]), 'resolution': array([1])}}\n"
     ]
    }
   ],
   "source": [
    "# louvain clustering parameters\n",
    "print(adata.uns[\"louvain\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f3956d52-bb10-4f48-b1bd-7e7f510ad3a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#1f77b4' '#ff7f0e' '#2ca02c' '#d62728' '#9467bd' '#8c564b' '#e377c2'\n",
      " '#bcbd22']\n"
     ]
    }
   ],
   "source": [
    "# louvain clustering plotting colors\n",
    "print(adata.uns[\"louvain_colors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1414a770-f471-403a-8813-bb2ce8f56a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'variance': array([32.11044  , 18.718647 , 15.607319 , 13.235274 ,  4.8012376,\n",
      "        3.977329 ,  3.5053132,  3.1576602,  3.028463 ,  2.9777625,\n",
      "        2.8842385,  2.8583548,  2.849085 ,  2.8220255,  2.811057 ,\n",
      "        2.781576 ,  2.7436602,  2.7404478,  2.736062 ,  2.6872916,\n",
      "        2.671316 ,  2.6690092,  2.6442325,  2.6394093,  2.6157827,\n",
      "        2.6102393,  2.575101 ,  2.5691617,  2.563295 ,  2.5489197,\n",
      "        2.5080354,  2.4762378,  2.264355 ,  2.1844513,  2.1353922,\n",
      "        2.096509 ,  2.0606086,  2.0105643,  1.9703175,  1.9465197,\n",
      "        1.9220033,  1.8847997,  1.8349565,  1.8038161,  1.7930729,\n",
      "        1.7611799,  1.7322571,  1.721284 ,  1.6937429,  1.6519767],\n",
      "      dtype=float32), 'variance_ratio': array([0.02012818, 0.01173364, 0.00978333, 0.00829643, 0.00300962,\n",
      "       0.00249316, 0.00219728, 0.00197936, 0.00189837, 0.00186659,\n",
      "       0.00180796, 0.00179174, 0.00178593, 0.00176896, 0.00176209,\n",
      "       0.00174361, 0.00171984, 0.00171783, 0.00171508, 0.00168451,\n",
      "       0.00167449, 0.00167305, 0.00165752, 0.00165449, 0.00163968,\n",
      "       0.00163621, 0.00161418, 0.00161046, 0.00160678, 0.00159777,\n",
      "       0.00157214, 0.00155221, 0.00141939, 0.00136931, 0.00133855,\n",
      "       0.00131418, 0.00129168, 0.00126031, 0.00123508, 0.00122016,\n",
      "       0.00120479, 0.00118147, 0.00115023, 0.00113071, 0.00112397,\n",
      "       0.00110398, 0.00108585, 0.00107897, 0.00106171, 0.00103553],\n",
      "      dtype=float32)}\n"
     ]
    }
   ],
   "source": [
    "# PCA: variance explained\n",
    "print(adata.uns[\"pca\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b7d38d-7176-43ae-940f-0d6e8e677974",
   "metadata": {},
   "source": [
    "In summary, the `adata.uns` field is helpful to store metadata that relates to the dataset as a whole (processing parameters, experimental details etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77fa1b9e-b9ac-4561-8498-7ac51c4f5f88",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Views and copies of `AnnData` objects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc54783f-1aaa-4e19-8796-a556657bd604",
   "metadata": {},
   "source": [
    "As we mentioned in the section on subsetting, whenever you subset an `AnnData` objects, you will always get a view in return instead of making a copy. That avoids having the same data in memory twice. Sometimes you will want to have a copy instead of a view, i.e. to modify the subset independent of the parent `AnnData` object. We will get to that below - but first, let's demonstrate what a \"view\" is.\n",
    "\n",
    "For that, we first look at a 5x5 subset of our active data matrix, by indexing `X` directly. We add `.A` to turn the sparse `X` into a nice print-out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0bf1f984-1830-4cba-8d98-e6a7c2a51b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.64266497, 0.        ],\n",
       "       [0.532456  , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [2.1446393 , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X[:5, 5:10].A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6914a0-0633-442b-bad8-a5c59675931f",
   "metadata": {},
   "source": [
    "Now, if we index `adata` directly, we get a \"view\" - that means it does _not_ copy the data of our 5x5 subset and stores it somewhere. Instead, when we use it, it will use the data of the original `AnnData` object that is already in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "29b2cb82-9001-45dd-9b23-2b3271ebe43a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 5 × 5\n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain_cell_types', 'is_low_quality'\n",
       "    var: 'gene_names', 'n_cells', 'gene_ids'\n",
       "    uns: 'louvain', 'louvain_colors', 'pca'\n",
       "    obsm: 'X_pca', 'X_tsne', 'X_umap'\n",
       "    layers: 'raw', 'counts_per_million'\n",
       "    obsp: 'distances_all'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view = adata[:5, 5:10]\n",
    "adata_view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dcfefc-e91e-4471-8a48-e4d0f4cf448c",
   "metadata": {},
   "source": [
    "Of course, the data inside the `.X` of our `adata_view` is exactly our subset from above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bf9ade28-c467-4279-aff8-e457b48bcdcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.64266497, 0.        ],\n",
       "       [0.532456  , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [2.1446393 , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view.X.A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9cc5f4-32bf-4f75-8921-92a250633a39",
   "metadata": {},
   "source": [
    "However, what if we change something in the parent `AnnData` object? Lets add a fake value in the first row of the original `adata.X` and then check what happens see what happens in the `X` of the view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9d591dca-72eb-4286-a36f-9b4de8bef335",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/share/miniconda3/envs/tutorials/lib/python3.10/site-packages/scipy/sparse/_index.py:100: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_intXint(row, col, x.flat[0])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        , 99.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.64266497,  0.        ],\n",
       "       [ 0.532456  ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 2.1446393 ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X[0, 7] = 99\n",
    "adata_view.X.A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08668363-a279-4d62-8093-aa28439a898b",
   "metadata": {},
   "source": [
    "The change propagated as expected! Because the view just mirrors the parent `AnnData` object, we can see the fake value inserted in the parent `X` also in the view.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "Propagating changes from a parent `AnnData` object to its view(s) currently works for all numpy-derived fields (`X`, `layers`, `obsm`, `varm`, `obsp` and `varp`) but _not_ for the pandas-derived `obs` and `var` dataframes! See this [github issue](https://github.com/scverse/anndata/issues/887) for status updates.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10cb0a98-3258-4d12-b8e6-1012028dee59",
   "metadata": {},
   "source": [
    "#### Turn a view into a copy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8470860a-f472-4825-b6fa-2be676c4b53b",
   "metadata": {},
   "source": [
    "As mentioned above, sometimes you might want a copy of the parent `AnnData` instead of just a view. You can get an actual `AnnData` object from a view in two ways:\n",
    "- call `.copy()` on the view - a copy is returned\n",
    "- modify any of elements of the view - the view object will be turned into copy\n",
    "\n",
    "Lets try this - we start with the `adata_view` that we created above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8806fa84-143a-4d01-b220-7dfbcf34251a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 5 × 5\n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain_cell_types', 'is_low_quality'\n",
       "    var: 'gene_names', 'n_cells', 'gene_ids'\n",
       "    uns: 'louvain', 'louvain_colors', 'pca'\n",
       "    obsm: 'X_pca', 'X_tsne', 'X_umap'\n",
       "    layers: 'raw', 'counts_per_million'\n",
       "    obsp: 'distances_all'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view  # still a view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c149160c-4a80-4411-b02e-623a39978dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 5 × 5\n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain_cell_types', 'is_low_quality'\n",
       "    var: 'gene_names', 'n_cells', 'gene_ids'\n",
       "    uns: 'louvain', 'louvain_colors', 'pca'\n",
       "    obsm: 'X_pca', 'X_tsne', 'X_umap'\n",
       "    layers: 'raw', 'counts_per_million'\n",
       "    obsp: 'distances_all'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view_copy = adata_view.copy()\n",
    "adata_view_copy  # an actual AnnData object!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cf9584aa-f534-42cc-b68a-d2cc16cce8ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2167/3524272241.py:1: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  adata_view.obs[\"new_column\"] = \"Test\"  # add a new column to the `obs` dataframe\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 5 × 5\n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain_cell_types', 'is_low_quality', 'new_column'\n",
       "    var: 'gene_names', 'n_cells', 'gene_ids'\n",
       "    uns: 'louvain', 'louvain_colors', 'pca'\n",
       "    obsm: 'X_pca', 'X_tsne', 'X_umap'\n",
       "    layers: 'raw', 'counts_per_million'\n",
       "    obsp: 'distances_all'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view.obs[\"new_column\"] = \"Test\"  # add a new column to the `obs` dataframe\n",
    "adata_view  # Not a view anymore!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6417e00-366e-4ab1-9834-9e393bca2afb",
   "metadata": {},
   "source": [
    "Note that when we modified the `adata_view`, this not only turned `adata_view` into a propper `AnnData` object by making a copy internally - it also triggered the `ImplicitModificationWarning` to let you know what happened."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562bfbdc-ee83-4c79-8a3f-fb7522f0003b",
   "metadata": {},
   "source": [
    "Now, both `adata_view_copy` and `adata_view` store actual data and are separated from their parent `AnnData` object `adata`. We can demonstrate that by adding another fake value to `adata.X` as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5851b3c5-57eb-4d20-b789-c99d880ab2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/share/miniconda3/envs/tutorials/lib/python3.10/site-packages/scipy/sparse/_index.py:100: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_intXint(row, col, x.flat[0])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        , 99.        , 98.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.64266497,  0.        ],\n",
       "       [ 0.532456  ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 2.1446393 ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X[0, 8] = 98\n",
    "adata.X[:5, 5:10].A  # the parent of course has the change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6939560-6236-4356-94a1-f92bf6d065ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        , 99.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.64266497,  0.        ],\n",
       "       [ 0.532456  ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 2.1446393 ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view.X.A  # the former view (that now is a actual AnnData object) does not have it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "80c70d47-7ac5-458d-bb60-959224320593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        , 99.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.64266497,  0.        ],\n",
       "       [ 0.532456  ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 2.1446393 ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_view_copy.X.A  # the copy of the view (also an actual AnnData object) does not have it either"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e08a7a-1fb8-4b7d-bf6a-ce3d547c1b47",
   "metadata": {},
   "source": [
    "In summary, views can help to quickly create a subset of an existing `AnnData` object without using additional memory. However, if you modify them, they will turn into an actual `AnnData` object and contain a separate copy of the data you subsetted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f164a7c9-07bc-49b4-b07e-8496e6330d33",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Where can I get help, and what tutorial should I try next?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0b8647e1-f3db-44af-bacd-dedfd118fb15",
   "metadata": {},
   "source": [
    "This is the end of the this introductory tutorial. You can find more details on `anndata` functionality on the [documentation page](https://anndata.readthedocs.io/en/latest/). On the `anndata` [GitHub] you can read about and report new technical issues. On the [scverse Discord](https://discourse.scverse.org/) you can ask questions to the scverse community and get help from our developers.\n",
    "\n",
    "\n",
    "### `MuData`: `AnnData`, but for multimodal datasets\n",
    "\n",
    "`mudata` is the extension of `anndata` to multimodal datasets, i.e. whenever you have high-dimensional observations from more than one modality (e.g. combined scRNA and scATAC data) and want to manage annotations of cells/genes/... in a consistent and convenient way. It builds intuitively on `anndata` by treating the data from each modality as a separate `AnnData` object, but combining them to a joint, multimodal way in the `MuData` object. Try the [introductory tutorial](https://mudata.readthedocs.io/en/latest/notebooks/quickstart_mudata.html) to get started, and see the page on [multimodal data containers](https://muon.readthedocs.io/en/latest/io/mudata.html) to get a first overview of the `MuData` object and its contents.\n",
    "\n",
    "### Advanced tutorials\n",
    "\n",
    "For some advanced topics, we have dedicated tutorials that you can try next. They can be found on the [scverse tutorial page](https://scverse.org/learn/). Here are a few recommendations:\n",
    "\n",
    "- Coming to Python&scverse from R? Try our [interoperability tutorial](https://scverse-tutorials.readthedocs.io/en/latest/notebooks/scverse_data_interoperability.html), where we show how `AnnData` objects can be interfaced with single cell data structures from other languages (e.g. `Seurat`. `SingleCellExperiment`, ...)\n",
    "- Work with large datasets and don't want to fully load them into memory? Check out our `anndata`s [backed mode tutorial](https://scverse-tutorials.readthedocs.io/en/latest/notebooks/scverse_data_backed.html)\n",
    "- Want to combine datasets? We a [tutorial on how axes and concatenation work](https://scverse-tutorials.readthedocs.io/en/latest/notebooks/tutorial_axes_anndata_mudata.html) for both `AnnData` and `MuData` objects. \n",
    "<!-- - Want to start with your scRNA analysis? Here is our [example scRNA processing workflow_LINK_MISSING](LINK) in `scanpy`. -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4d2f61",
   "metadata": {},
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}