{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d717b014",
   "metadata": {},
   "source": [
    "# orthomap: Step 4 - TEI calculation\n",
    "\n",
    "This notebook will demonstrate how to to add a transcriptome evolutionary index (short: TEI) to scRNA data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb58ae3",
   "metadata": {},
   "source": [
    "## Notebook file\n",
    "\n",
    "Notebook file can be obtained here:\n",
    "\n",
    "[https://raw.githubusercontent.com/kullrich/orthomap/main/docs/notebooks/add_tei.ipynb](https://raw.githubusercontent.com/kullrich/orthomap/main/docs/notebooks/add_tei.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9f9c62",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e81e5626",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statannot import add_stat_annotation\n",
    "# increase dpi\n",
    "%matplotlib inline\n",
    "#plt.rcParams['figure.dpi'] = 300\n",
    "#plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['figure.figsize'] = [6, 4.5]\n",
    "#plt.rcParams['figure.figsize'] = [4.4, 3.3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966bf9fe",
   "metadata": {},
   "source": [
    "## Import orthomap python package submodules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d39256b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submodules\n",
    "from orthomap import qlin, gtf2t2g, of2orthomap, orthomap2tei, datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9ad0db",
   "metadata": {},
   "source": [
    "## Step 0, Step 1, Step 2 and Step 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c112b1b",
   "metadata": {},
   "source": [
    "In order to come to Step 4, TEI calculation, one needs to have the results from Step 0, Step 1, Step 2 and Step 3.\n",
    "\n",
    "The query species in this part is: __*Danio rerio*__ (zebrafish).\n",
    "\n",
    "Please have a look at the documentation of [Step 0 - run OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/orthofinder.html) to get to know what information and files are mandatory to extract gene age classes from [OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results.\n",
    "\n",
    "In [Step 1 - get taxonomic information](https://orthomap.readthedocs.io/en/latest/tutorials/query_lineage.html) you have already been introduced how to extract query lineage information with `orthomap` and the `qlin.get_qlin()` function.\n",
    "\n",
    "In [Step 2 - gene age class assignment](https://orthomap.readthedocs.io/en/latest/tutorials/get_orthomap.html) you have already been introduced how to extract an orthomap (gene age class) from [OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results with `orthomap` and the `of2orthomap.get_orthomap()` function or how to import pre-calculated orthomaps with the `orthomap2tei.read_orthomap()` function.\n",
    "\n",
    "In [Step 3 - map gene/transcript IDs](https://orthomap.readthedocs.io/en/latest/tutorials/geneset_overlap.html) you have already been introduced how to extract gene IDs from `GTF` file with `orthoamp` and the `gtf2t2g.parse_gtf()` function. You have also been introduced how to use the `orthomap2tei.geneset_overlap()` function to check the overlap between the gene IDs and have learned how to use the `orthomap2tei.replace_by()` function to e.g. reduce isoform gene IDs to gene IDs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8488bd",
   "metadata": {},
   "source": [
    "### Step 0 - run OrthoFinder\n",
    "\n",
    "For this documentation part all mandatory [OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) ([Emms and Kelly, 2019](https://doi.org/10.1186/s13059-019-1832-y)) results have been pre-calculated.\n",
    "\n",
    "Please have a look at the documentation of [Step 0 - run OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/orthofinder.html) to get further insides.\n",
    "\n",
    "The results are available here: \n",
    "\n",
    "https://doi.org/10.5281/zenodo.7242264\n",
    "\n",
    "or can be accessed with the `dataset` submodule of `orthomap`\n",
    "\n",
    "`datasets.ensembl105(datapath='data')` (download folder set to `'data'`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7809c37",
   "metadata": {},
   "source": [
    "### Step 1 - get taxonomic information\n",
    "\n",
    "Please have a look at the documentation of [Step 1 - get taxonomic information](https://orthomap.readthedocs.io/en/latest/tutorials/query_lineage.html) to get further insides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5dc5c7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query name: Danio rerio\n",
      "query taxID: 7955\n",
      "query kingdom: Eukaryota\n",
      "query lineage names: \n",
      "['root(1)', 'cellular organisms(131567)', 'Eukaryota(2759)', 'Opisthokonta(33154)', 'Metazoa(33208)', 'Eumetazoa(6072)', 'Bilateria(33213)', 'Deuterostomia(33511)', 'Chordata(7711)', 'Craniata(89593)', 'Vertebrata(7742)', 'Gnathostomata(7776)', 'Teleostomi(117570)', 'Euteleostomi(117571)', 'Actinopterygii(7898)', 'Actinopteri(186623)', 'Neopterygii(41665)', 'Teleostei(32443)', 'Osteoglossocephalai(1489341)', 'Clupeocephala(186625)', 'Otomorpha(186634)', 'Ostariophysi(32519)', 'Otophysi(186626)', 'Cypriniphysae(186627)', 'Cypriniformes(7952)', 'Cyprinoidei(30727)', 'Danionidae(2743709)', 'Danioninae(2743711)', 'Danio(7954)', 'Danio rerio(7955)']\n",
      "query lineage: \n",
      "[1, 131567, 2759, 33154, 33208, 6072, 33213, 33511, 7711, 89593, 7742, 7776, 117570, 117571, 7898, 186623, 41665, 32443, 1489341, 186625, 186634, 32519, 186626, 186627, 7952, 30727, 2743709, 2743711, 7954, 7955]\n"
     ]
    }
   ],
   "source": [
    "# get query species taxonomic lineage information\n",
    "query_lineage = qlin.get_qlin(q='Danio rerio')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f7d754",
   "metadata": {},
   "source": [
    "### Step 2 - gene age class assignment\n",
    "\n",
    "Here, `orthomap` use the query species information and [OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results to extract the oldest common tree node per orthogroup along a species tree and to assign this node as the gene age to the corresponding genes.\n",
    "\n",
    "Please have a look at the documentation of [Step 2 - gene age class assignment](https://orthomap.readthedocs.io/en/latest/tutorials/get_orthomap.html) to get further insides."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86828cf6",
   "metadata": {},
   "source": [
    "### Step 3 - map gene/transcript IDs\n",
    "\n",
    "To be able to link gene ages assignments from an orthomap and gene or transcript of scRNA dataset, one needs to check the overlap of the annotated gene names. With the `gtf2t2g` submodule of `orthomap` and the `gtf2t2g.parse_gtf()` function, one can extract gene and transcript names from a given gene feature file (`GTF`).\n",
    "\n",
    "Please have a look at the documentation of [Step 3 - map gene/transcript IDs](https://orthomap.readthedocs.io/en/latest/tutorials/geneset_overlap.html) to get further insides."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d05faf",
   "metadata": {},
   "source": [
    "Here, the pre-calculated orthomap from *Danio rerio* (zebrafish) obtained via Step 0, Step 1, Step 2 and Step 3 is loaded as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e57a22b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [..........................................................................] 1920563 / 1920563"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'data/zebrafish_ensembl_105_orthomap.tsv'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.zebrafish_ensembl105_orthomap(datapath='data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d445639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>seqID</th>\n",
       "      <th>Orthogroup</th>\n",
       "      <th>PSnum</th>\n",
       "      <th>PStaxID</th>\n",
       "      <th>PSname</th>\n",
       "      <th>PScontinuity</th>\n",
       "      <th>geneID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSDART00000127643.3</td>\n",
       "      <td>OG0000000</td>\n",
       "      <td>6</td>\n",
       "      <td>33213</td>\n",
       "      <td>Bilateria</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>ENSDARG00000087544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSDART00000171750.2</td>\n",
       "      <td>OG0000000</td>\n",
       "      <td>6</td>\n",
       "      <td>33213</td>\n",
       "      <td>Bilateria</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>ENSDARG00000095745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSDART00000190648.1</td>\n",
       "      <td>OG0000000</td>\n",
       "      <td>6</td>\n",
       "      <td>33213</td>\n",
       "      <td>Bilateria</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>ENSDARG00000097551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSDART00000130167.3</td>\n",
       "      <td>OG0000001</td>\n",
       "      <td>10</td>\n",
       "      <td>7742</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ENSDARG00000086420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSDART00000150909.2</td>\n",
       "      <td>OG0000001</td>\n",
       "      <td>10</td>\n",
       "      <td>7742</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ENSDARG00000086613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25167</th>\n",
       "      <td>ENSDART00000180796.1</td>\n",
       "      <td>OG0029510</td>\n",
       "      <td>19</td>\n",
       "      <td>186625</td>\n",
       "      <td>Clupeocephala</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>ENSDARG00000110427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25168</th>\n",
       "      <td>ENSDART00000145618.2</td>\n",
       "      <td>OG0029511</td>\n",
       "      <td>19</td>\n",
       "      <td>186625</td>\n",
       "      <td>Clupeocephala</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>ENSDARG00000093188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25169</th>\n",
       "      <td>ENSDART00000143229.2</td>\n",
       "      <td>OG0029512</td>\n",
       "      <td>29</td>\n",
       "      <td>7955</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ENSDARG00000069978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25170</th>\n",
       "      <td>ENSDART00000143837.3</td>\n",
       "      <td>OG0029512</td>\n",
       "      <td>29</td>\n",
       "      <td>7955</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ENSDARG00000078193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25171</th>\n",
       "      <td>ENSDART00000180573.1</td>\n",
       "      <td>OG0029513</td>\n",
       "      <td>13</td>\n",
       "      <td>117571</td>\n",
       "      <td>Euteleostomi</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>ENSDARG00000109747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25172 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      seqID Orthogroup  PSnum  PStaxID         PSname  \\\n",
       "0      ENSDART00000127643.3  OG0000000      6    33213      Bilateria   \n",
       "1      ENSDART00000171750.2  OG0000000      6    33213      Bilateria   \n",
       "2      ENSDART00000190648.1  OG0000000      6    33213      Bilateria   \n",
       "3      ENSDART00000130167.3  OG0000001     10     7742     Vertebrata   \n",
       "4      ENSDART00000150909.2  OG0000001     10     7742     Vertebrata   \n",
       "...                     ...        ...    ...      ...            ...   \n",
       "25167  ENSDART00000180796.1  OG0029510     19   186625  Clupeocephala   \n",
       "25168  ENSDART00000145618.2  OG0029511     19   186625  Clupeocephala   \n",
       "25169  ENSDART00000143229.2  OG0029512     29     7955    Danio rerio   \n",
       "25170  ENSDART00000143837.3  OG0029512     29     7955    Danio rerio   \n",
       "25171  ENSDART00000180573.1  OG0029513     13   117571   Euteleostomi   \n",
       "\n",
       "       PScontinuity              geneID  \n",
       "0          0.846154  ENSDARG00000087544  \n",
       "1          0.846154  ENSDARG00000095745  \n",
       "2          0.846154  ENSDARG00000097551  \n",
       "3          0.909091  ENSDARG00000086420  \n",
       "4          0.909091  ENSDARG00000086613  \n",
       "...             ...                 ...  \n",
       "25167      0.400000  ENSDARG00000110427  \n",
       "25168      0.400000  ENSDARG00000093188  \n",
       "25169      1.000000  ENSDARG00000069978  \n",
       "25170      1.000000  ENSDARG00000078193  \n",
       "25171      0.222222  ENSDARG00000109747  \n",
       "\n",
       "[25172 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get query species orthomap\n",
    "\n",
    "# download zebrafish orthomap results here: https://doi.org/10.5281/zenodo.7242264\n",
    "# or download with datasets.zebrafish_ensembl_105_orthomap('data')\n",
    "\n",
    "query_orthomap = orthomap2tei.read_orthomap('data/zebrafish_ensembl_105_orthomap.tsv')\n",
    "query_orthomap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a57f5b2",
   "metadata": {},
   "source": [
    "### Import now, the scRNA dataset of the query species\n",
    "\n",
    "Here, data is used, like in the publication ([Farrell et al., 2018](https://doi.org/10.1126/science.aar3131); [Wagner et al., 2018](https://doi.org/10.1126/science.aar4362); [Qiu et al., 2022](https://doi.org/10.1038/s41588-022-01018-x)).\n",
    "\n",
    "scRNA data was downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy or orthomap package and is available here:\n",
    "\n",
    "https://doi.org/10.5281/zenodo.7243602\n",
    "\n",
    "or can be accessed with the `dataset` submodule of `orthomap`:\n",
    "\n",
    "`datasets.qiu22_zebrafish(datapath='data')` (download folder set to `'data'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db955abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load scRNA data\n",
    "\n",
    "# download zebrafish scRNA data here: https://doi.org/10.5281/zenodo.7243602\n",
    "# or download with datasets.qui22_zebrafish(datapath='data')\n",
    "\n",
    "#zebrafish_data = datasets.qiu22_zebrafish(datapath='data')\n",
    "zebrafish_data = sc.read('data/zebrafish_data.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ea81d7",
   "metadata": {},
   "source": [
    "## Step 4 - Get TEI values and add them to scRNA dataset\n",
    "\n",
    "Since now the gene names correspond to each other in the orthomap and the scRNA `adata` object, one can calculate the transcriptome evolutionary index (`TEI`) and add them to the scRNA dataset (`adata` object).\n",
    "\n",
    "The `TEI` measure represents the weighted arithmetic mean (expression levels as weights for the phylostratum value) over all evolutionary age categories denoted as _phylostra_.\n",
    "\n",
    "$TEI_s = \\sum (e_{is} * ps_i) / \\sum e_{is}$\n",
    "\n",
    ", where $TEI_s$ denotes the `TEI` value in developmental stage ${s, e_{is}}$ denotes the gene expression level of gene ${i}$ in stage ${s}$, and ${ps_i}$ denotes the corresponding phylostratum of gene ${i, i = 1,...,N}$ and ${N = total\\ number\\ of\\ genes}$.\n",
    "\n",
    "Note: If e.g. two different isoforms would fall into two different gene age classes, their gene ages might differ based on the oldest ortholog found in their corresponding orthologous groups. However, both isoforms share the same gene name and their gene ages would clash. In this case one can decide either to use the `keep='min'` or `keep='max'` gene age to be kept by the `get_tei` function, which defaults to keep in this cases the `keep='min'` or in other words the 'older' gene age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b2f8bd",
   "metadata": {},
   "source": [
    "To be able to re-use the original `count` data, they are added as a new `layer` to the `adata` object. This is useful because later on the `count` data can be used to extract either the relative expression per gene age class or re-calculate other metrics. \n",
    "\n",
    "This can be done either on un-normalized `counts`, on `normalized` and `log-transformed` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c51ffc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "zebrafish_data.layers['counts'] = zebrafish_data.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2ddcc3",
   "metadata": {},
   "source": [
    "### add TEI to adata object\n",
    "\n",
    "Using the submodule `orthomap2tei` from `orthomap` and the `orthomap2tei.get_tei()` function, transcriptome evolutionary index (`TEI`) values are calculated and directyl added to the existing `adata` object (`add_obs=True`).\n",
    "\n",
    "There are other options to e.g. not start from the `adata.X` `counts` but from another `layer` from the `adata` object, the default is to use the `adata.X` (`layer=None`). By default, the values are pre-processed by the `normalize_total` option and the `log1p` option.\n",
    "\n",
    "__Note:__ Please have a look at [Liu & Robinson-Rechavi, 2018](https://doi.org/10.1093/gbe/evy177>) to get more insides how expression transformation can influence TEI calculation.\n",
    "\n",
    "If `add_obs=True` the resulting `TEI` values are added to the existing `adata` object as a new observation with the name set with the `obs_name` option.\n",
    "\n",
    "If `add_var=True` the gene age values are added to the existing `adata` object as a new variable with the name set with the `var_name` option.\n",
    "\n",
    "__Note:__ Genes not assigned to any gene class will get a missing assignment.\n",
    "\n",
    "If one wants to calculate bootstrap `TEI` values per cell, the `boot` option can be set to `boot=True` and gene age classes will be randomly chosen prior calculating `TEI` values `bt=10` times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19f86d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>tei</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hpf3.3_ZFHIGH_WT_DS5_AAAAGTTGCCTC</th>\n",
       "      <td>5.531953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf3.3_ZFHIGH_WT_DS5_AAACAAGTGTAT</th>\n",
       "      <td>5.550115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf3.3_ZFHIGH_WT_DS5_AAACACCTCGTC</th>\n",
       "      <td>5.501283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf3.3_ZFHIGH_WT_DS5_AAATGAGGTTTN</th>\n",
       "      <td>5.521703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf3.3_ZFHIGH_WT_DS5_AACCCTCTCGAT</th>\n",
       "      <td>5.614314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf24_DEW057_TGACACAACAG_GCCACATC</th>\n",
       "      <td>5.136634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf24_DEW057_CTTACGGG_AACCTGAC</th>\n",
       "      <td>5.322264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf24_DEW057_TGAACATCTAT_GACGATGG</th>\n",
       "      <td>5.121005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf24_DEW057_TGAGGTTTCTC_CTCAGAAT</th>\n",
       "      <td>4.963845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hpf24_DEW057_ACGTGCTAG_CAAGTCAT</th>\n",
       "      <td>5.270514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71203 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        tei\n",
       "hpf3.3_ZFHIGH_WT_DS5_AAAAGTTGCCTC  5.531953\n",
       "hpf3.3_ZFHIGH_WT_DS5_AAACAAGTGTAT  5.550115\n",
       "hpf3.3_ZFHIGH_WT_DS5_AAACACCTCGTC  5.501283\n",
       "hpf3.3_ZFHIGH_WT_DS5_AAATGAGGTTTN  5.521703\n",
       "hpf3.3_ZFHIGH_WT_DS5_AACCCTCTCGAT  5.614314\n",
       "...                                     ...\n",
       "hpf24_DEW057_TGACACAACAG_GCCACATC  5.136634\n",
       "hpf24_DEW057_CTTACGGG_AACCTGAC     5.322264\n",
       "hpf24_DEW057_TGAACATCTAT_GACGATGG  5.121005\n",
       "hpf24_DEW057_TGAGGTTTCTC_CTCAGAAT  4.963845\n",
       "hpf24_DEW057_ACGTGCTAG_CAAGTCAT    5.270514\n",
       "\n",
       "[71203 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add TEI values to existing adata object\n",
    "orthomap2tei.get_tei(adata=zebrafish_data,\n",
    "    gene_id=query_orthomap['geneID'],\n",
    "    gene_age=query_orthomap['PSnum'],\n",
    "    keep='min',\n",
    "    layer=None,\n",
    "    add_var=True,\n",
    "    var_name='Phylostrata',\n",
    "    add_obs=True,\n",
    "    obs_name='tei',\n",
    "    boot=False,\n",
    "    bt=10,\n",
    "    normalize_total=True,\n",
    "    log1p=True,\n",
    "    target_sum=1e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7836aa",
   "metadata": {},
   "source": [
    "In the `adata` object, for each cell there is now a new variable called `tei`, which can be used in downstream analysis.\n",
    "\n",
    "Once the gene age data has been added to the scRNA dataset, one can e.g. plot the corresponding transcriptome evolutionary index (`TEI`) values by any given observation pre-defined in the scRNA dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddeedaeb",
   "metadata": {},
   "source": [
    "### Boxplot gene age class per embryo stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "830e8730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 747.4x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.violin(adata=zebrafish_data,\n",
    "             keys=['tei'],\n",
    "             groupby='stage',\n",
    "             rotation=90,\n",
    "             palette='Paired',\n",
    "             stripplot=False,\n",
    "             inner='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0f4437",
   "metadata": {},
   "source": [
    "Please have a look at the documentation of [Downstream analysis](https://orthomap.readthedocs.io/en/latest/tutorials/index.html#orthomap-downstream-analysis) to get further insides."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:scanpy]",
   "language": "python",
   "name": "conda-env-scanpy-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": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}