{
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   "cell_type": "markdown",
   "id": "0dcc5993-3e46-4fd2-9bb0-12203baf3e01",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Aggregate VSI per cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628bdf37-ee6b-48c9-9edf-a424ac1d5698",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Here, we will demonstrate the workflow how to aggregate the VSI information for a cell from all its associated pixels in the _ovrlpy_ analysis using a Xenium dataset, but conceptually this works the same for other imaging-based SRT platforms, as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "666214c6-175c-47eb-ae36-1b9656a6df68",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import polars as pl\n",
    "import seaborn as sns\n",
    "\n",
    "import ovrlpy\n",
    "\n",
    "matplotlib.rc(\"font\", size=6)\n",
    "\n",
    "data_folder = Path(\"path/to/data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4273a753-6fe2-40d1-b82d-0a3a14ea66c5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We start by reading the transcript data and running the _ovrlpy_ pipeline. Of course the parameters used here can be adjusted to your needs.\n",
    "\n",
    "Importantly, we additionally load the column mapping transcripts to cell-IDs. This is necessary for one of the approaches and will be explained in the corresponding section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc900dff-1f71-48c9-97f2-731aa1d6cdc2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "transcripts = ovrlpy.io.read_Xenium(\n",
    "    data_folder / \"transcripts.parquet\",\n",
    "    additional_columns=[\"cell_id\"],\n",
    ")\n",
    "\n",
    "brain = ovrlpy.Ovrlp(transcripts, n_workers=8, random_state=42)\n",
    "brain.analyse(min_transcripts=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da788f9-f5e4-4550-926a-7886adb4120d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Aggregate VSI per cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29cdfa3e-17f4-42c6-b8f4-907b94f2f30a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### via transcripts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd889f6e-6187-4e52-8b77-8c3b8091a6a2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "When using the assignments from transcripts to cells, we need to make sure that the corresponding column is already included before we run the _ovrlpy_ pipeline. The functions in {py:mod}`ovrlpy.io` module support reading more than just the transcript coordinate columns with the `additional_columns` argument.\n",
    "Two details are required when trying to extract the VSI and signal strength values using this method: the name of the column with the transcript to cell asignment, and the value that corresponds to _unassigned_ transcripts. \n",
    "- Xenium and MERSCOPE: Typically, the column is named `'cell_id'` and unassigned transcripts are indicated with `-1`.\n",
    "- CosMx: There is no column uniquely identifying a cell but the combination of  `'cell_ID'` and `'fov'` can be used. The unassigned transcripts are indicated with `0` for the `'cell_ID'`.\n",
    "\n",
    "This appraoch is easy and fast, however, depending on the sparsity of the data some pixels corresponding to a cell may be missed if they do not have any associated transcripts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4659ed3-f26b-4c83-af1a-fbaa47f25d43",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Using the {py:func}`ovrlpy.cell_integrity_from_transcripts` functions we can now obtain the signal strength and VSI per cell. We actually get multiple values (one per pixel) that can be used for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e4e04e3-aac5-4d9f-bff9-091e401be06f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<small>shape: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>cell_id</th><th>x_pixel</th><th>y_pixel</th><th>signal</th><th>vsi</th></tr><tr><td>i32</td><td>i32</td><td>i32</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>114141</td><td>5674</td><td>2389</td><td>0.836131</td><td>0.704874</td></tr><tr><td>114064</td><td>5480</td><td>2233</td><td>0.316398</td><td>0.438043</td></tr><tr><td>73743</td><td>8215</td><td>6233</td><td>1.564292</td><td>0.609592</td></tr><tr><td>27878</td><td>1326</td><td>3683</td><td>0.796194</td><td>0.839414</td></tr><tr><td>14921</td><td>2186</td><td>5415</td><td>2.458118</td><td>0.775897</td></tr></tbody></table></div>"
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       "shape: (5, 5)\n",
       "┌─────────┬─────────┬─────────┬──────────┬──────────┐\n",
       "│ cell_id ┆ x_pixel ┆ y_pixel ┆ signal   ┆ vsi      │\n",
       "│ ---     ┆ ---     ┆ ---     ┆ ---      ┆ ---      │\n",
       "│ i32     ┆ i32     ┆ i32     ┆ f32      ┆ f32      │\n",
       "╞═════════╪═════════╪═════════╪══════════╪══════════╡\n",
       "│ 114141  ┆ 5674    ┆ 2389    ┆ 0.836131 ┆ 0.704874 │\n",
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       "│ 73743   ┆ 8215    ┆ 6233    ┆ 1.564292 ┆ 0.609592 │\n",
       "│ 27878   ┆ 1326    ┆ 3683    ┆ 0.796194 ┆ 0.839414 │\n",
       "│ 14921   ┆ 2186    ┆ 5415    ┆ 2.458118 ┆ 0.775897 │\n",
       "└─────────┴─────────┴─────────┴──────────┴──────────┘"
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   "source": [
    "cell_pixels = ovrlpy.cell_integrity_from_transcripts(\n",
    "    brain, cell_id=\"cell_id\", unassigned=-1\n",
    ")\n",
    "\n",
    "cell_pixels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419c5a67-beea-4b49-b1a9-4fc3766e24b6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### via segmentation masks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ca228b-5d4b-4b62-ab8b-7c19986cb64c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Instead of using the transcipt to cell assignments, we can also use the segmentation masks to aggregate the VSI and signal strength per cell.\n",
    "\n",
    "For this we need two pieces of information, a segmentation mask per cell (as a [`shapely` Geometry](https://shapely.readthedocs.io/page/geometry.html), typically a Polygon), and a cell-ID."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb89b5a-cbb4-4270-b97a-84a592801362",
   "metadata": {},
   "source": [
    "Note, that for this approach we need additional optional dependencies that can be installed via the ovrlpy extra\n",
    "\n",
    "```sh\n",
    "pip install ovrlpy[segmentation]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59425df-adf4-4e6b-b4de-2c45f5d9616d",
   "metadata": {},
   "source": [
    "Below you can find example code on how to generate this information from the standard output of some of the widely adopted platforms."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3efd4654-ebf7-402d-9c42-571971471180",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### Examples how to load segmentation masks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f6f9e6-73f1-48a2-bd43-69ac60fd2491",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "##### Xenium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b77dd1ec-c3b2-422d-bd4e-6a42076fb87f",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import Polygon\n",
    "\n",
    "boundary_file = data_folder / \"cell_boundaries.parquet\"\n",
    "\n",
    "\n",
    "boundaries = (\n",
    "    pl.scan_parquet(boundary_file)\n",
    "    .select(\"cell_id\", pl.concat_arr([\"vertex_x\", \"vertex_y\"]).alias(\"coords\"))\n",
    "    .group_by(\"cell_id\")\n",
    "    .agg(pl.col(\"coords\"))\n",
    "    .with_columns(\n",
    "        pl.col(\"coords\").map_elements(Polygon, return_dtype=pl.Object).alias(\"mask\")\n",
    "    )\n",
    "    .collect()\n",
    ")\n",
    "\n",
    "masks = boundaries[\"mask\"]\n",
    "ids = boundaries[\"cell_id\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b1993e-f821-49d2-a34b-efd323097134",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "##### MERSCOPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e48fa1c-b7f6-4c00-828c-47b372e758dc",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd  # geopandas must be installed separately\n",
    "from shapely import union_all\n",
    "\n",
    "boundary_file = data_folder / \"cell_boundaries.parquet\"\n",
    "\n",
    "\n",
    "boundaries = (\n",
    "    gpd.read_parquet(boundary_file, columns=[\"EntityID\", \"Geometry\"])\n",
    "    # get union across all z-planes as we only need a single 2D cell mask\n",
    "    # though its also possible to select a single z-plane instead\n",
    "    .groupby(\"EntityID\")\n",
    "    .aggregate({\"Geometry\": union_all})\n",
    "    .set_geometry(\"Geometry\")\n",
    ")\n",
    "\n",
    "masks = boundaries[\"Geometry\"]\n",
    "ids = boundaries.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ee98a2-de30-4b95-bf25-cd7c3dd216bb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "##### CosMx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8b9bc2-8474-4fa4-b376-60bad8efe432",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import polars.selectors as cs\n",
    "from shapely.geometry import Polygon\n",
    "\n",
    "boundary_file = data_folder / \"S0-polygons.csv.gz\"\n",
    "\n",
    "boundaries = (\n",
    "    pl.read_csv(\n",
    "        boundary_file,\n",
    "        columns=[\"cell\", \"x_global_px\", \"y_global_px\"],\n",
    "        schema_overrides={\n",
    "            \"cell\": pl.Categorical,\n",
    "            # Due to weird encoding of the coordinates in the csv (using scientific notation)\n",
    "            # they could only be parsed as floats (even if they are actually integers)\n",
    "            # and then needed to be cast to integer.\n",
    "            # This may not be necessary for all datasets.\n",
    "            \"x_global_px\": pl.Float32,\n",
    "            \"y_global_px\": pl.Float32,\n",
    "        },\n",
    "    )\n",
    "    .lazy()\n",
    "    .with_columns(cs.numeric().cast(pl.Int32))  # see comment above\n",
    "    .with_columns(pl.concat_arr([\"x_global_px\", \"y_global_px\"]).alias(\"coords\"))\n",
    "    .group_by(\"cell\")\n",
    "    .agg(pl.col(\"coords\"))\n",
    "    .filter(pl.col(\"coords\").list.len() >= 3)  # remove invalid polygons\n",
    "    .with_columns(\n",
    "        pl.col(\"coords\").map_elements(Polygon, return_dtype=pl.Object).alias(\"mask\")\n",
    "    )\n",
    "    .collect()\n",
    ")\n",
    "\n",
    "\n",
    "masks = boundaries[\"mask\"]\n",
    "ids = boundaries[\"cell\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b558dc-0c2f-4660-aebf-9a894b13207e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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    "tags": []
   },
   "source": [
    "Using the segmentation masks and the cell-IDs we can aggregate the VSI and signal to obtain a similar DataFrame as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6640cc0-5439-42fd-8ca1-746bee802286",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
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       "<small>shape: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>cell_id</th><th>x_pixel</th><th>y_pixel</th><th>signal</th><th>vsi</th></tr><tr><td>i32</td><td>i64</td><td>i64</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>16712</td><td>3960</td><td>28</td><td>1.330162</td><td>0.901204</td></tr><tr><td>16712</td><td>3961</td><td>28</td><td>1.138057</td><td>0.734445</td></tr><tr><td>16712</td><td>3959</td><td>29</td><td>1.26439</td><td>0.944789</td></tr><tr><td>16712</td><td>3960</td><td>29</td><td>1.1826</td><td>0.913471</td></tr><tr><td>16712</td><td>3961</td><td>29</td><td>1.004933</td><td>0.72393</td></tr></tbody></table></div>"
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       "shape: (5, 5)\n",
       "┌─────────┬─────────┬─────────┬──────────┬──────────┐\n",
       "│ cell_id ┆ x_pixel ┆ y_pixel ┆ signal   ┆ vsi      │\n",
       "│ ---     ┆ ---     ┆ ---     ┆ ---      ┆ ---      │\n",
       "│ i32     ┆ i64     ┆ i64     ┆ f32      ┆ f32      │\n",
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       "│ 16712   ┆ 3960    ┆ 29      ┆ 1.1826   ┆ 0.913471 │\n",
       "│ 16712   ┆ 3961    ┆ 29      ┆ 1.004933 ┆ 0.72393  │\n",
       "└─────────┴─────────┴─────────┴──────────┴──────────┘"
      ]
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   ],
   "source": [
    "cell_pixels = ovrlpy.cell_integrity_from_masks(brain, masks, ids)\n",
    "\n",
    "cell_pixels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f039e0-3d95-4de2-8568-c9193f3f5348",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Filtering low VSI cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f91da5",
   "metadata": {},
   "source": [
    "### Defining low-quality cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9f5523-042f-43fc-909d-666389c1fc2d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "There are multiple ways that come to mind how the VSI and signal values per cell can be analysed. We haven't benchmarked these yet but some ideas are listed below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142e6200-26eb-4f3b-af44-adb2eea555e3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "In general we would recommend to filter the pixels by a minimum signal strength cutoff to avoid effects of pixels with low gene expression which are therefore likely noisy. The exact value depends on your dataset and is influenced by parameters such as the gene panel (highly vs lowly expressed genes), the panel size, sensitivity, ...\n",
    "\n",
    "Similarly, the VSI threshold depends on the analysed samples and should be evaluated and carefully picked. Here, we will use a value 0.7 as threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4379c08f-4f54-423c-adce-31fe60982822",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "min_s = 1.5  # signal strength threshold\n",
    "min_vsi = 0.7  # VSI threshold\n",
    "\n",
    "cell_pixels = cell_pixels.filter(pl.col(\"signal\") > min_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbaca093-0b89-439f-b21b-d48cd09711e7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "One idea is to evaluate cells based on the fraction of the area (here using pixels as proxy) that is affected by low vertical signal integrity. A reasonable threshold for the VSI may depend on your experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22edc0c7-3395-4535-ac60-bff98f132a5c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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   "outputs": [
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       "│ 108019  ┆ 0.0                 │\n",
       "│ 45237   ┆ 0.100917            │\n",
       "│ 61609   ┆ 0.065574            │\n",
       "└─────────┴─────────────────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraction_of_low_vsi_per_cell = cell_pixels.group_by(\"cell_id\").agg(\n",
    "    fraction_low_vsi_px=(pl.col(\"vsi\") < min_vsi).sum() / pl.col(\"vsi\").len()\n",
    ")\n",
    "\n",
    "fraction_of_low_vsi_per_cell.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa24760-d81c-45a5-a506-0deb8e05b734",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Another option is to calculate the mean VSI per cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5401c428-262f-4742-9327-54f8d10273ec",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>cell_id</th><th>vsi</th></tr><tr><td>i32</td><td>f32</td></tr></thead><tbody><tr><td>148927</td><td>0.901035</td></tr><tr><td>141567</td><td>0.894772</td></tr><tr><td>42608</td><td>0.878738</td></tr><tr><td>5121</td><td>0.829888</td></tr><tr><td>143684</td><td>0.826892</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 2)\n",
       "┌─────────┬──────────┐\n",
       "│ cell_id ┆ vsi      │\n",
       "│ ---     ┆ ---      │\n",
       "│ i32     ┆ f32      │\n",
       "╞═════════╪══════════╡\n",
       "│ 148927  ┆ 0.901035 │\n",
       "│ 141567  ┆ 0.894772 │\n",
       "│ 42608   ┆ 0.878738 │\n",
       "│ 5121    ┆ 0.829888 │\n",
       "│ 143684  ┆ 0.826892 │\n",
       "└─────────┴──────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_vsi_per_cell = cell_pixels.group_by(\"cell_id\").agg(pl.col(\"vsi\").mean())\n",
    "\n",
    "mean_vsi_per_cell.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da00de1-829a-4c76-8f0d-992ec33313c3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Filtering cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27c8dce-f844-4599-99e8-1bf8654efd68",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "To demonstrate the effect of low VSI cells (using these two measures) we will visualize the calculated metrics per cell in a UMAP and see what happens if we remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efc8b765-9ed5-421c-9c0a-af7e61348520",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# to avoid cluttering the output with warnings\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18fa3e21-a3c5-4b54-9d51-1e443be806e4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "\n",
    "cells = sc.read_10x_h5(data_folder / \"cell_feature_matrix.h5\")\n",
    "\n",
    "ax = sns.histplot(cells.X.sum(axis=1).A1, bins=200)\n",
    "_ = ax.set(xlabel=\"Transcripts per cell\", ylabel=\"No. of cells\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5bc5a25-dd02-466f-8818-f0f4f9c3bcca",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We will just perform some minimal filtering of the cells and very basic processing for the demonstration. Of course you can adjust this workflow to your liking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7588b93-1914-465d-8d87-58f9aa451a23",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc.pp.filter_cells(cells, min_counts=50)\n",
    "sc.pp.filter_cells(cells, max_counts=1_000)\n",
    "\n",
    "sc.pp.pca(cells, n_comps=30)\n",
    "sc.pp.neighbors(cells)\n",
    "sc.tl.umap(cells, min_dist=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eee225a-ed57-44f4-98e0-71c07fe7175a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Next, we will add the calculated metrics as metadata to our cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cf55f25-7589-4f41-a0ad-c774502ec9a5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# transform to pandas.DataFrame and make the index a string (necessary for AnnData)\n",
    "metrics = (\n",
    "    mean_vsi_per_cell.join(\n",
    "        fraction_of_low_vsi_per_cell, on=\"cell_id\", how=\"full\", coalesce=True\n",
    "    )\n",
    "    .to_pandas()\n",
    "    .astype({\"cell_id\": str})\n",
    "    .set_index(\"cell_id\")\n",
    ")\n",
    "\n",
    "# drop cells for which we do not have VSI info\n",
    "cells = cells[cells.obs_names.isin(metrics.index)]\n",
    "\n",
    "cells.obs = cells.obs.join(metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ef2fc3-0ec1-4de7-93fa-a2de7240dcf0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "And visualize the results of cell filtering in UMAPs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a8d801a-d290-4f4f-8c17-5a49cb354d6a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "from ovrlpy._plotting import BIH_CMAP\n",
    "\n",
    "\n",
    "def plot_UMAPs(\n",
    "    umaps, hue, threshold_label, umap_cols=[\"UMAP1\", \"UMAP2\"], cmap=BIH_CMAP\n",
    "):\n",
    "    x, y = umap_cols\n",
    "\n",
    "    with plt.style.context(\"dark_background\"):\n",
    "        fig, axs = plt.subplots(\n",
    "            ncols=5, figsize=(7, 2.2), width_ratios=[1, 1, 1, 1, 0.05]\n",
    "        )\n",
    "\n",
    "        scatter_kwargs = dict(\n",
    "            x=x,\n",
    "            y=y,\n",
    "            s=0.1,\n",
    "            lw=0,\n",
    "            rasterized=True,\n",
    "            hue=hue,\n",
    "            palette=cmap,\n",
    "            legend=False,\n",
    "            hue_norm=matplotlib.colors.Normalize(vmin=0, vmax=1),\n",
    "        )\n",
    "\n",
    "        for i, (ax, t) in enumerate(zip(axs, umaps)):\n",
    "            df = umaps[t]\n",
    "            n = df.shape[0]\n",
    "            _ = sns.scatterplot(data=df, ax=ax, **scatter_kwargs)\n",
    "            if i == 0:\n",
    "                ax.set(title=f\"All cells\\n({n:,} cells)\")\n",
    "            else:\n",
    "                threshold = threshold_label.format(t=t)\n",
    "                ax.set(title=f\"Filtered cells\\n({threshold}, {n:,} cells)\")\n",
    "\n",
    "            if i == 3:\n",
    "                m = plt.cm.ScalarMappable(norm=ax.collections[0].norm, cmap=cmap)\n",
    "                fig.colorbar(m, ax=ax, cax=axs[i + 1], ticks=[0, 1], label=hue)\n",
    "\n",
    "            ax.set_aspect(\"equal\", adjustable=\"box\")\n",
    "            ax.set(xticks=[], yticks=[], xlabel=None, ylabel=None)\n",
    "            ax.axis(\"square\")\n",
    "\n",
    "        fig.tight_layout()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f230ec1e-03b9-4707-b49a-a50d278f47b3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### Mean VSI\n",
    "\n",
    "First a quick look at the mean VSI distribution, which can help find an appropriate threshold for filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9af001c3-9043-4d69-b265-53072ab401aa",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(cells.obs[\"vsi\"], bins=50)\n",
    "_ = ax.set(xlabel=\"Mean VSI\", ylabel=\"No. of cells\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bdcb41-f481-470c-9512-922717cd3521",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "In the UMAP we can observe how the separation of clusters has improved by removing these potential doublets. We used a few thresholds to demonstrate the effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2a191dc-33ab-49a1-9fcb-a163e11b2e10",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def calculate_umap(adata):\n",
    "    sc.pp.pca(adata, n_comps=30)\n",
    "    sc.pp.neighbors(adata)\n",
    "    sc.tl.umap(adata, min_dist=0.1)\n",
    "\n",
    "    df = adata.obs\n",
    "    df[[\"UMAP1\", \"UMAP2\"]] = adata.obsm[\"X_umap\"]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9367b14c-16e2-43ba-a63f-1c13e7166a09",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x220 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap = cells.obs.copy()\n",
    "umap[[\"UMAP1\", \"UMAP2\"]] = cells.obsm[\"X_umap\"]\n",
    "\n",
    "umaps = {None: umap}\n",
    "\n",
    "# define doublets by mean VSI below threshold and generate UMAPs w/o doublets\n",
    "for t in [0.3, 0.5, 0.7]:\n",
    "    umaps[f\"{t:.1f}\"] = calculate_umap(cells[cells.obs[\"vsi\"] >= t].copy())\n",
    "\n",
    "\n",
    "fig = plot_UMAPs(umaps, hue=\"vsi\", threshold_label=\"≥{t}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db707f1-60b9-451f-a22c-3618770f3c52",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### Fraction of low VSI pixels\n",
    "\n",
    "Following the same procedure as before, we first have a look at the overall distribution of our metric and then decide on a threshold for filtering and show the results in a UMAP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2824149c-2fdb-40ce-a2be-aeabfd059f50",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(cells.obs[\"fraction_low_vsi_px\"], bins=50)\n",
    "_ = ax.set(xlabel=\"Fraction of low VSI px per cell\", ylabel=\"No. of cells\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cc1986a7-7770-4170-9c72-d201310e7333",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x220 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "umaps = {None: umap}\n",
    "\n",
    "# define doublets by more than X% pixels with VSI below threshold\n",
    "for t in [1, 0.5, 0.2]:\n",
    "    umaps[f\"{t:.1f}\"] = calculate_umap(\n",
    "        cells[cells.obs[\"fraction_low_vsi_px\"] < t].copy()\n",
    "    )\n",
    "\n",
    "fig = plot_UMAPs(\n",
    "    umaps, hue=\"fraction_low_vsi_px\", threshold_label=\"<{t}\", cmap=\"flare_r\"\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:muellni-ovrlpy_dev]",
   "language": "python",
   "name": "conda-env-muellni-ovrlpy_dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}