scripts/01_prepare_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1338ec2-a3ba-42e1-82d8-c6213b531a2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata\n",
    "import scanpy as sc\n",
    "from scipy.io import mmread\n",
    "import requests\n",
    "import os\n",
    "from tqdm import trange\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88334aa1-c75f-4957-8f15-c9f1602d08d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def download_file(doi,ext):\n",
    "    url = 'https://api.datacite.org/dois/'+doi+'/media'\n",
    "    r = requests.get(url).json()\n",
    "    netcdf_url = r['data'][0]['attributes']['url']\n",
    "    r = requests.get(netcdf_url,stream=True)\n",
    "    #Set file name\n",
    "    fname = doi.split('/')[-1]+ext\n",
    "    #Download file with progress bar\n",
    "    if r.status_code == 403:\n",
    "        print(\"File Unavailable\")\n",
    "    if 'content-length' not in r.headers:\n",
    "        print(\"Did not get file\")\n",
    "    else:\n",
    "        with open(fname, 'wb') as f:\n",
    "            total_length = int(r.headers.get('content-length'))\n",
    "            pbar = trange(int(total_length/1024), unit=\"B\")\n",
    "            for chunk in r.iter_content(chunk_size=1024):\n",
    "                if chunk:\n",
    "                    pbar.update()\n",
    "                    f.write(chunk)\n",
    "        return fname"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d188a7b-d18d-41e7-a290-51e6eba0089d",
   "metadata": {},
   "source": [
    "### Prepare Ex Utero data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bed94e43-e22b-47b9-ba76-847e0fc6b1b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-26 11:55:44--  https://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149372/suppl/GSE149372_scRNA.MetaData.85.csv.gz\n",
      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 130.14.250.13, 130.14.250.12, 2607:f220:41e:250::12, ...\n",
      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|130.14.250.13|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 224984 (220K) [application/x-gzip]\n",
      "Saving to: ‘../data/raw/GSE149372_scRNA.MetaData.85.csv.gz’\n",
      "\n",
      "../data/raw/GSE1493 100%[===================>] 219,71K   529KB/s    in 0,4s    \n",
      "\n",
      "2024-03-26 11:55:45 (529 KB/s) - ‘../data/raw/GSE149372_scRNA.MetaData.85.csv.gz’ saved [224984/224984]\n",
      "\n",
      "--2024-03-26 11:55:45--  https://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149372/suppl/GSE149372_scRNA.normalized.assay85.csv.gz\n",
      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 130.14.250.12, 130.14.250.13, 2607:f220:41e:250::12, ...\n",
      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|130.14.250.12|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 202630450 (193M) [application/x-gzip]\n",
      "Saving to: ‘../data/raw/GSE149372_scRNA.normalized.assay85.csv.gz’\n",
      "\n",
      "../data/raw/GSE1493 100%[===================>] 193,24M  17,3MB/s    in 16s     \n",
      "\n",
      "2024-03-26 11:56:02 (12,1 MB/s) - ‘../data/raw/GSE149372_scRNA.normalized.assay85.csv.gz’ saved [202630450/202630450]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Download\n",
    "!wget -O ../data/raw/GSE149372_scRNA.MetaData.85.csv.gz https://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149372/suppl/GSE149372_scRNA.MetaData.85.csv.gz\n",
    "!wget -O ../data/raw/GSE149372_scRNA.normalized.assay85.csv.gz https://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149372/suppl/GSE149372_scRNA.normalized.assay85.csv.gz\n",
    "!gunzip -f -k ../data/raw/*.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f4584a5-98b8-4100-a43e-051bbc891dbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19588, 10290)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load full data\n",
    "counts_e85 = pd.read_csv('../data/raw/GSE149372_scRNA.normalized.assay85.csv',index_col=0)\n",
    "cellMeta_e85 = pd.read_csv('../data/raw/GSE149372_scRNA.MetaData.85.csv')\n",
    "counts_e85.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b83efe91-0660-47c1-8ece-cb87bfb9605c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22398/3245848769.py:46: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  adata_exut.obs['colors'] = colors_new\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(6205, 19588)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#process metadata for all E8.5 data\n",
    "count_mat_e85 = counts_e85.values.T\n",
    "adata_e85 = anndata.AnnData(X = count_mat_e85)\n",
    "adata_e85.obs_names = list(counts_e85.columns)\n",
    "adata_e85.var_names = list(counts_e85.index)\n",
    "adata_e85.obs['Utero'] = cellMeta_e85.Utero.values\n",
    "adata_e85.obs['Cluster'] = cellMeta_e85.seurat_clusters.values\n",
    "adata_e85.obs['n_count_rna'] = cellMeta_e85.nCount_RNA.values\n",
    "\n",
    "#subset to only ex-utero data\n",
    "adata_exut = adata_e85[adata_e85.obs['Utero']=='ExUt']\n",
    "\n",
    "#celltype infos and colors as Chari&Pachter\n",
    "celltype_dict = {'0' : 'Blood', '11': 'Blood' ,\n",
    "                 '1' : 'Somitic Mesoderm',\n",
    "                 '2' : 'Foregut Mid-hindgut',\n",
    "                 '3' : 'Cardiac',\n",
    "                 '4' : 'Mid-hindbrain',\n",
    "                 '5' : 'Endothelial',\n",
    "                 '6' : 'Presomitic/Mixed Mesoderm',\n",
    "                 '7' : 'Amnion', '15' : 'Amnion', '17' : 'Amnion',\n",
    "                 '8' : 'Extra-Embryonic Endoderm',\n",
    "                 '9' : 'Pharyngeal Mesoderm',\n",
    "                 '10': 'Extra-Embryonic Ectoderm',\n",
    "                 '12': 'Placodes',\n",
    "                 '13': 'Neural Tube', '16' : 'Neural Tube',\n",
    "                 '14': 'Extra-Embryonic Mesoderm',\n",
    "                 '18': 'Endothelial'}\n",
    "\n",
    "color_dict = {'Blood':'#70D4CE','Presomitic/Mixed Mesoderm':'#81A483','Foregut Mid-hindgut':'#4D50E3',\n",
    "        'Cardiac':'#E0B7CF','Mid-hindbrain':'#FC514A','Endothelial':'#EDE7D1',\n",
    "        'Amnion':'#B4CA72',\n",
    "        'Extra-Embryonic Endoderm':'#C7A2F3','Pharyngeal Mesoderm':'#CCD0BC','Extra-Embryonic Ectoderm':'#73F0FD',\n",
    "        'Placodes':'#AE995B','Neural Tube':'#87BD7E','Somitic Mesoderm':'#D34FEA',\n",
    "        'Extra-Embryonic Mesoderm':'#0F5813'}\n",
    "\n",
    "colors_exut = [color_dict[celltype_dict[str(cl)]] for cl in adata_exut.obs['Cluster'].values]\n",
    "colors_new = np.array(list(colors_exut.copy()),dtype='<U15')\n",
    "\n",
    "#add extra colors for clusters that get the same colors in the original Chari&Pachter palette\n",
    "colors_new[adata_exut.obs['Cluster'].values==11]='tab:red'\n",
    "colors_new[adata_exut.obs['Cluster'].values==15]='tab:orange'\n",
    "colors_new[adata_exut.obs['Cluster'].values==16]='tab:blue'\n",
    "colors_new[adata_exut.obs['Cluster'].values==17]='tab:green'\n",
    "\n",
    "adata_exut.obs['colors'] = colors_new\n",
    "\n",
    "adata_exut.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26bd6e06-b381-472b-927c-aa32ab875d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6205, 19588)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preprocess as Chari & Pachter \n",
    "sc.pp.filter_cells(adata_exut, min_counts=0)\n",
    "sc.pp.filter_genes(adata_exut, min_counts=0)\n",
    "sc.pp.highly_variable_genes(adata_exut,n_top_genes=2000)\n",
    "adata_exut.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1059886-c9c1-498b-8ea4-4cea603c2447",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10000. 10000. 10000. ... 10000. 10000. 10000.]\n"
     ]
    }
   ],
   "source": [
    "#re-create anndata with needed fields only\n",
    "adata_exut_allgenes = anndata.AnnData(X=adata_exut.X.copy(),\n",
    "                               obs=dict(clusterlabels=adata_exut.obs['Cluster'].values,\n",
    "                                        clusternames=[celltype_dict[str(x)] for x in adata_exut.obs['Cluster'].values],\n",
    "                                        colors=colors_new,\n",
    "                                        n_count_rna = adata_exut.obs['n_count_rna'].values),\n",
    "                               var=dict(highly_variable=adata_exut.var['highly_variable'].values),\n",
    "                               uns=dict(dataset='exut-allgenes'))\n",
    "\n",
    "### undo log1p\n",
    "adata_exut_allgenes.layers['normalized_counts'] = np.expm1(adata_exut_allgenes.X.copy())\n",
    "### show that counts were normalized to sum to 10,000 per cell\n",
    "print(adata_exut_allgenes.layers['normalized_counts'].sum(axis=1))\n",
    "### un-do log1p and normalization with scaling by 10.000 using the seq depths from the metadata\n",
    "adata_exut_allgenes.layers['raw'] = (adata_exut_allgenes.obs['n_count_rna'].values*(np.expm1(adata_exut_allgenes.X.copy())/10000).T).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f46dbd2-ff33-467e-ba9b-d9a51583d3c0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22398/3848050230.py:3: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  adata_exut_hvgs.uns['dataset']='exut'\n"
     ]
    }
   ],
   "source": [
    "#apply hvg selection\n",
    "adata_exut_hvgs = adata_exut_allgenes[:,adata_exut_allgenes.var['highly_variable']]\n",
    "adata_exut_hvgs.uns['dataset']='exut'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78b2b8ad-c541-4837-991d-40018ff19b64",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6205, 2000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Chari&Pachter scaling\n",
    "adata_exut_hvgs.layers['X_scaled'] = sc.pp.scale(adata_exut_hvgs.X.copy())\n",
    "adata_exut_hvgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b4d6530-4da6-483e-bb89-6080dd226d71",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#save\n",
    "adata_exut_hvgs.write_h5ad('../data/preprocessed/exut_adata.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c80a94f-26e9-4ff6-b98a-1638d65adc6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6205 × 2000\n",
       "    obs: 'clusterlabels', 'clusternames', 'colors', 'n_count_rna'\n",
       "    var: 'highly_variable'\n",
       "    uns: 'dataset'\n",
       "    layers: 'normalized_counts', 'raw', 'X_scaled'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_exut_hvgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "184e531b-b6a4-40a4-907b-641e3264cfb8",
   "metadata": {
    "tags": []
   },
   "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>clusterlabels</th>\n",
       "      <th>clusternames</th>\n",
       "      <th>colors</th>\n",
       "      <th>n_count_rna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Somitic Mesoderm</td>\n",
       "      <td>#D34FEA</td>\n",
       "      <td>12663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Blood</td>\n",
       "      <td>#70D4CE</td>\n",
       "      <td>14587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>Neural Tube</td>\n",
       "      <td>#87BD7E</td>\n",
       "      <td>9380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Somitic Mesoderm</td>\n",
       "      <td>#D34FEA</td>\n",
       "      <td>9267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Pharyngeal Mesoderm</td>\n",
       "      <td>#CCD0BC</td>\n",
       "      <td>2002</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>6200</th>\n",
       "      <td>0</td>\n",
       "      <td>Blood</td>\n",
       "      <td>#70D4CE</td>\n",
       "      <td>6036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>1</td>\n",
       "      <td>Somitic Mesoderm</td>\n",
       "      <td>#D34FEA</td>\n",
       "      <td>12097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6202</th>\n",
       "      <td>0</td>\n",
       "      <td>Blood</td>\n",
       "      <td>#70D4CE</td>\n",
       "      <td>14804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6203</th>\n",
       "      <td>6</td>\n",
       "      <td>Presomitic/Mixed Mesoderm</td>\n",
       "      <td>#81A483</td>\n",
       "      <td>12619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6204</th>\n",
       "      <td>15</td>\n",
       "      <td>Amnion</td>\n",
       "      <td>tab:orange</td>\n",
       "      <td>8243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6205 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      clusterlabels               clusternames      colors  n_count_rna\n",
       "0                 1           Somitic Mesoderm     #D34FEA        12663\n",
       "1                 0                      Blood     #70D4CE        14587\n",
       "2                13                Neural Tube     #87BD7E         9380\n",
       "3                 1           Somitic Mesoderm     #D34FEA         9267\n",
       "4                 9        Pharyngeal Mesoderm     #CCD0BC         2002\n",
       "...             ...                        ...         ...          ...\n",
       "6200              0                      Blood     #70D4CE         6036\n",
       "6201              1           Somitic Mesoderm     #D34FEA        12097\n",
       "6202              0                      Blood     #70D4CE        14804\n",
       "6203              6  Presomitic/Mixed Mesoderm     #81A483        12619\n",
       "6204             15                     Amnion  tab:orange         8243\n",
       "\n",
       "[6205 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_exut_hvgs.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c322a140-4a18-4dfa-8f47-8f000a62febe",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Prepare simulated ex-utero data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6107c10-68c2-43ae-98d0-b1f277679b84",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#simulate data\n",
    "clusters,clustersizes = np.unique(adata_exut_allgenes.obs['clusterlabels'].values,return_counts=True)\n",
    "\n",
    "np.random.seed(seed=42)\n",
    "theta=10\n",
    "X_sim=[]\n",
    "clusterlabels_sim = []\n",
    "X_real=[]\n",
    "for i,(cluster,size) in enumerate(zip(clusters,clustersizes)):\n",
    "    \n",
    "    cluster_idx = adata_exut_allgenes.obs['clusterlabels'].values == cluster\n",
    "    clustercounts_raw = adata_exut_allgenes.layers['raw'][cluster_idx,:]\n",
    "    X_real += [clustercounts_raw]\n",
    "    \n",
    "    #compute marginals per cluster\n",
    "    ns = np.sum(clustercounts_raw,axis=1)\n",
    "    ps = np.sum(clustercounts_raw,axis=0)\n",
    "    ps = ps / np.sum(ps)\n",
    "    mus = np.outer(ns,ps)\n",
    "    \n",
    "    #simulate\n",
    "    if theta == np.Inf: #Poisson case\n",
    "        X_sim += [np.random.poisson(mus)]\n",
    "    else:\n",
    "        nb_p = theta / (theta + mus)           \n",
    "        X_sim += [np.random.negative_binomial(np.ones_like(nb_p)*theta, nb_p)]\n",
    "     \n",
    "    clusterlabels_sim += [cluster]*size\n",
    "\n",
    "clusterlabels_sim=np.array(clusterlabels_sim)\n",
    "#colors for re-ordered simulated data\n",
    "colors_sim = [color_dict[celltype_dict[str(cl)]] for cl in clusterlabels_sim]\n",
    "colors_new_sim = np.array(list(colors_sim.copy()),dtype='<U15')\n",
    "colors_new_sim[clusterlabels_sim==11]='tab:red'\n",
    "colors_new_sim[clusterlabels_sim==15]='tab:orange'\n",
    "colors_new_sim[clusterlabels_sim==16]='tab:blue'\n",
    "colors_new_sim[clusterlabels_sim==17]='tab:green'\n",
    "\n",
    "adata_sim = anndata.AnnData(X=np.concatenate(X_sim),\n",
    "                            obs=dict(clusterlabels=clusterlabels_sim,\n",
    "                                     colors=colors_new_sim),\n",
    "                            uns=dict(dataset=f'exut-sim-theta-{theta}-real-seqdepths'),\n",
    "                            layers=dict(X_real=np.concatenate(X_real)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51359ce9-0e3e-4434-86fc-aa904d704e2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6205 × 19588\n",
       "    obs: 'clusterlabels', 'colors', 'n_counts'\n",
       "    var: 'n_counts'\n",
       "    uns: 'dataset', 'log1p'\n",
       "    layers: 'X_real', 'raw'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preprocess like before\n",
    "adata_sim.layers['raw'] = adata_sim.X.copy()\n",
    "sc.pp.normalize_total(adata_sim,target_sum=10000)\n",
    "sc.pp.log1p(adata_sim)\n",
    "sc.pp.filter_cells(adata_sim, min_counts=0)\n",
    "sc.pp.filter_genes(adata_sim, min_counts=0)\n",
    "adata_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "626335d1-cc9c-4a67-acd9-5cacb26197f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 6205 × 2000\n",
       "    obs: 'clusterlabels', 'colors', 'n_counts'\n",
       "    var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'dataset', 'log1p', 'hvg'\n",
       "    layers: 'X_real', 'raw'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select HVGs\n",
    "sc.pp.highly_variable_genes(adata_sim,n_top_genes=2000)\n",
    "adata_sim = adata_sim[:,adata_sim.var['highly_variable']]\n",
    "adata_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4490ef1b-5f3c-4580-b17a-5889d3c6a1fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22398/45398618.py:1: ImplicitModificationWarning: Setting element `.layers['X_scaled']` of view, initializing view as actual.\n",
      "  adata_sim.layers['X_scaled'] = sc.pp.scale(adata_sim.X.copy())\n"
     ]
    }
   ],
   "source": [
    "adata_sim.layers['X_scaled'] = sc.pp.scale(adata_sim.X.copy())\n",
    "adata_sim.write_h5ad(f'../data/preprocessed/{adata_sim.uns[\"dataset\"]}_adata.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5f9f1a4-e7c2-43da-b214-1e87c1c71430",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6205 × 2000\n",
       "    obs: 'clusterlabels', 'colors', 'n_counts'\n",
       "    var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'dataset', 'log1p', 'hvg'\n",
       "    layers: 'X_real', 'raw', 'X_scaled'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f5b330b-5ece-4847-b876-115219261034",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22398/1111178664.py:67: UserWarning: The figure layout has changed to tight\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1053x607.5 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "largest_gene_idx = np.argsort(np.mean(adata_sim.layers['raw'],axis=0))[-5:]\n",
    "\n",
    "with plt.style.context('../mpl_style.txt'):\n",
    "    \n",
    "    ## PREPARE\n",
    "    nrows_grid = 3\n",
    "    ncols_grid = 6\n",
    "    fig = plt.figure(figsize=(7.8,4.5))\n",
    "    axes_sim = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(0, col), colspan=1,rowspan=1) for col in range(ncols_grid)]\n",
    "    axes_real = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(1, col), colspan=1,rowspan=1) for col in range(ncols_grid)]\n",
    "    ax_legend = plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(2, 0), colspan=5,rowspan=1)    \n",
    "    ax_legend_small = plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(2, 5), colspan=1,rowspan=1)    \n",
    "    \n",
    "    plt.suptitle(\"High-expression genes: Distributions per cluster\\n(x-axis limited to 99th percentile)\")\n",
    "    \n",
    "    ## CLUSTER DISTRIBUTIONS\n",
    "    for clm,(gene_idx) in enumerate(largest_gene_idx):\n",
    "        \n",
    "        p99count_sim = np.percentile(adata_sim.layers['raw'][:,gene_idx],99)\n",
    "        p99count_real = np.percentile(adata_sim.layers['X_real'][:,gene_idx],99)\n",
    "        p99count = np.max([p99count_sim,p99count_real])\n",
    "        bins=np.linspace(0,p99count+1,num=25)\n",
    "\n",
    "        for cluster in range(19):\n",
    "\n",
    "            cluster_idx = adata_sim.obs['clusterlabels'].values == cluster\n",
    "            color = np.unique(adata_sim[cluster_idx].obs['colors'])[0]\n",
    "\n",
    "            X_cl_sim = adata_sim.layers['raw'][cluster_idx,:]\n",
    "            X_cl_real = adata_sim.layers['X_real'][cluster_idx,:]\n",
    "\n",
    "            plt.sca(axes_sim[clm])\n",
    "            # plt.yscale('log')\n",
    "            plt.title(f'Gene {gene_idx}')\n",
    "            counts_sim=X_cl_sim[:,gene_idx] \n",
    "            plt.hist(counts_sim,bins=bins,color=color)\n",
    "            plt.xlabel('Counts')\n",
    "            if clm==0:\n",
    "                  plt.ylabel('SIMULATED\\n')\n",
    "\n",
    "\n",
    "            plt.sca(axes_real[clm])\n",
    "            # plt.yscale('log')\n",
    "            counts_real=X_cl_real[:,gene_idx]    \n",
    "            plt.hist(counts_real,bins=bins,color=color,label=f'{celltype_dict[str(cluster)]} ({cluster})')\n",
    "            plt.xlabel('Counts')\n",
    "            if clm==0:\n",
    "                plt.ylabel('REAL\\n(EX UTERO DATA)\\n')\n",
    "                \n",
    "                h, l = axes_real[clm].get_legend_handles_labels()\n",
    "                ax_legend.legend(h,l,frameon=False,title='Cell type (cluster ID)',ncol=3,loc='center')\n",
    "                ax_legend.axis(\"off\")\n",
    "    \n",
    "    ## TOTAL COUNTS\n",
    "    plt.sca(axes_sim[5])\n",
    "    _,bins,_ = plt.hist(np.sum(adata_sim.layers['raw'],axis=1),bins=25,color='tab:blue',label='Simulated\\ntotal counts')\n",
    "    plt.xlabel('Total count per cell\\n(after HVG selection)')\n",
    "    h1, l1 = axes_sim[5].get_legend_handles_labels()\n",
    "\n",
    "    plt.sca(axes_real[5])\n",
    "    plt.hist(np.sum(adata_sim.layers['X_real'],axis=1),bins=bins,color='tab:orange',label='Real Ex Utero\\ntotal counts')\n",
    "    plt.xlabel('Total count per cell\\n(after HVG selection)')\n",
    "    h2, l2 = axes_real[5].get_legend_handles_labels()\n",
    "    ax_legend_small.legend(h1+h2,l1+l2,frameon=False,loc='center')\n",
    "    ax_legend_small.axis(\"off\")\n",
    "\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "fig.savefig(f'../results/figures/supplementary/{adata_sim.uns[\"dataset\"]}_real_vs_sim_cluster_distributions.pdf', dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)\n",
    "fig.savefig(f'../results/figures/supplementary/{adata_sim.uns[\"dataset\"]}_real_vs_sim_cluster_distributions.png', dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0e2755-d55e-40fe-981e-555e2d24d070",
   "metadata": {},
   "source": [
    "### Prepare MERFISH data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03f80d30-9c31-46ce-8b34-13d82e9a8bbe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9785B [00:02, 3825.16B/s]                                                                 \n",
      "59361B [00:38, 1544.21B/s]                                                                \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#download and unpack\n",
    "#metadata.csv\n",
    "download_file('10.22002/D1.2063','.gz')\n",
    "#counts.h5ad\n",
    "download_file('10.22002/D1.2064','.gz')\n",
    "\n",
    "os.system(\"gunzip D1.2063.gz\")\n",
    "os.system(\"gunzip D1.2064.gz\")\n",
    "os.system(\"mv D1.2063 ../data/raw/merfish_metadata.csv\")\n",
    "os.system(\"mv D1.2064 ../data/raw/merfish_counts.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5a96b9e1-9072-45f7-bbc3-d32de37be932",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280327, 254)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load full MERFISH data\n",
    "counts_merfish = anndata.read_h5ad('../data/raw/merfish_counts.h5ad')\n",
    "cellMeta_merfish = pd.read_csv('../data/raw/merfish_metadata.csv')\n",
    "counts_merfish.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e5e2870-17a1-4c59-98b2-96147cf96add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6963, 254)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#process clusternames and colors\n",
    "counts_merfish.obs['slice'] = pd.Categorical(cellMeta_merfish.slice_id)\n",
    "counts_merfish.obs['clusternames'] = pd.Categorical(cellMeta_merfish.subclass)\n",
    "_,clusterlabels_merfish = np.unique(counts_merfish.obs['clusternames'],return_inverse=True)\n",
    "counts_merfish.obs['clusterlabels'] = clusterlabels_merfish\n",
    "\n",
    "cdict = {'Microglia':'#A778BC',\n",
    "         'L6_IT':'#EA95C1',\n",
    "         'Astrocytes':'#B6657E','Oligodendrocytes':'#B49599',\n",
    "         'L23_IT':'#628EA2','OPC':'#72B9A5','L45_IT':'#71B7A2','Endothelial':'#7794C3','Pvalb':'#FD8085',\n",
    "         'L6_IT_Car3':'#FAD1FC','L56_NP':'#93B09B','PVM':'#ECBDD2','L5_IT':'#ABA8DF',\n",
    "         'L5_PT':'#D36486','Sst':'#8FE0B1','L6_CT':'#9589C4','Vip':'#CD83AC','VLMC':'#BBDBF2',\n",
    "         'SMC':'#7A849E','other':'#A382BC','L6b':'#64D7F0','Pericytes':'#95BA98',\n",
    "         'Lamp5':'#A3E169','Sncg':'#92B1DB','Sst_Chodl':'#81FAC9'}\n",
    "counts_merfish.obs['colors'] = [cdict[i] for i in counts_merfish.obs['clusternames']]\n",
    "\n",
    "\n",
    "#subset MERFISH data to same slide as Chari & Pachter\n",
    "slice_choice = np.unique(cellMeta_merfish.slice_id)[7]\n",
    "\n",
    "subset_merfish = counts_merfish[counts_merfish.obs['slice'].isin([slice_choice])]\n",
    "subset_merfish.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a3f3e93-ae81-4665-bddf-2b5b6bec6436",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jan/anaconda3/envs/elephant_analysis_env/lib/python3.11/site-packages/scanpy/preprocessing/_simple.py:372: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n"
     ]
    }
   ],
   "source": [
    "#preprocess as Chari&Pachter\n",
    "sc.pp.log1p(subset_merfish)\n",
    "subset_merfish.uns['dataset'] = 'merfish'\n",
    "#Chari&Pachter scaling\n",
    "subset_merfish.layers['X_scaled'] = sc.pp.scale(subset_merfish.X.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "541f5204-436f-4fd6-b099-0f2dd1791051",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#save\n",
    "subset_merfish.write_h5ad('../data/preprocessed/merfish_adata.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "974686e0-96a4-4b8b-940f-55735cd4a306",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6963 × 254\n",
       "    obs: 'slice', 'clusternames', 'clusterlabels', 'colors'\n",
       "    uns: 'log1p', 'dataset'\n",
       "    layers: 'X_scaled'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_merfish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4ba218c-00d1-4029-aa4a-33b5346e6d12",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>slice</th>\n",
       "      <th>clusternames</th>\n",
       "      <th>clusterlabels</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100028810813183819239792138762506953059</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>Microglia</td>\n",
       "      <td>12</td>\n",
       "      <td>#A778BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100042292675978612289781789372249900646</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>L6_IT</td>\n",
       "      <td>8</td>\n",
       "      <td>#EA95C1</td>\n",
       "    </tr>\n",
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       "      <th>100093902545314506370058151058375627035</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>Astrocytes</td>\n",
       "      <td>0</td>\n",
       "      <td>#B6657E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100140206101682663983686904841717501025</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>Oligodendrocytes</td>\n",
       "      <td>14</td>\n",
       "      <td>#B49599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100176313983591085952263714025726530686</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>L23_IT</td>\n",
       "      <td>2</td>\n",
       "      <td>#628EA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>99658397536625795119891455351124847928</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>L6_IT_Car3</td>\n",
       "      <td>9</td>\n",
       "      <td>#FAD1FC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99716035693339658241621650353650240200</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>Astrocytes</td>\n",
       "      <td>0</td>\n",
       "      <td>#B6657E</td>\n",
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       "      <th>99757070583384140112761609672322877368</th>\n",
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       "      <td>3</td>\n",
       "      <td>#71B7A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99847388990809554042272739785717376750</th>\n",
       "      <td>mouse1_slice162</td>\n",
       "      <td>L45_IT</td>\n",
       "      <td>3</td>\n",
       "      <td>#71B7A2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6963 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   slice      clusternames  \\\n",
       "index                                                                        \n",
       "100028810813183819239792138762506953059  mouse1_slice162         Microglia   \n",
       "100042292675978612289781789372249900646  mouse1_slice162             L6_IT   \n",
       "100093902545314506370058151058375627035  mouse1_slice162        Astrocytes   \n",
       "100140206101682663983686904841717501025  mouse1_slice162  Oligodendrocytes   \n",
       "100176313983591085952263714025726530686  mouse1_slice162            L23_IT   \n",
       "...                                                  ...               ...   \n",
       "99658397536625795119891455351124847928   mouse1_slice162        L6_IT_Car3   \n",
       "99716035693339658241621650353650240200   mouse1_slice162        Astrocytes   \n",
       "99757070583384140112761609672322877368   mouse1_slice162            L23_IT   \n",
       "99840731649735034456729605869484432530   mouse1_slice162            L45_IT   \n",
       "99847388990809554042272739785717376750   mouse1_slice162            L45_IT   \n",
       "\n",
       "                                         clusterlabels   colors  \n",
       "index                                                            \n",
       "100028810813183819239792138762506953059             12  #A778BC  \n",
       "100042292675978612289781789372249900646              8  #EA95C1  \n",
       "100093902545314506370058151058375627035              0  #B6657E  \n",
       "100140206101682663983686904841717501025             14  #B49599  \n",
       "100176313983591085952263714025726530686              2  #628EA2  \n",
       "...                                                ...      ...  \n",
       "99658397536625795119891455351124847928               9  #FAD1FC  \n",
       "99716035693339658241621650353650240200               0  #B6657E  \n",
       "99757070583384140112761609672322877368               2  #628EA2  \n",
       "99840731649735034456729605869484432530               3  #71B7A2  \n",
       "99847388990809554042272739785717376750               3  #71B7A2  \n",
       "\n",
       "[6963 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_merfish.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2132a5c9-64f0-438b-bd2f-38196ae559c3",
   "metadata": {},
   "source": [
    "### Prepare SmartSeq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "41ebde16-0e99-4f8c-b39b-57d01817776d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "65B [00:00, 170.96B/s]                                                                    \n",
      "26701B [00:37, 706.21B/s]                                                                 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download and unpack\n",
    "#metadata.csv\n",
    "download_file('10.22002/D1.2067','.gz')\n",
    "#smartseq.mtx (log counts)\n",
    "download_file('10.22002/D1.2071','.gz')\n",
    "\n",
    "os.system(\"gunzip D1.2067.gz\")\n",
    "os.system(\"gunzip D1.2071.gz\")\n",
    "\n",
    "os.system(\"mv D1.2067 ../data/raw/smartseq_metadata.csv\")\n",
    "os.system(\"mv D1.2071 ../data/raw/smartseq.mtx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3a4d920-e7d5-4902-804b-adb136bca184",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3850, 1999)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load data\n",
    "counts_smartseq = mmread('../data/raw/smartseq.mtx')\n",
    "meta_smartseq = pd.read_csv('../data/raw/smartseq_metadata.csv')\n",
    "counts_smartseq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55a02559-3ca1-4de4-822c-b41c24bc4c43",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#add metadata\n",
    "adata_smartseq = anndata.AnnData(X=counts_smartseq)\n",
    "adata_smartseq.obs['clusternames'] = [s for s in meta_smartseq.smartseq_cluster]\n",
    "_,smartseq_clusterlabels = np.unique(meta_smartseq.smartseq_cluster,return_inverse=True)\n",
    "adata_smartseq.obs['clusterlabels'] = smartseq_clusterlabels\n",
    "adata_smartseq.obs['colors'] = [c for c in meta_smartseq.smartseq_cluster_color]\n",
    "adata_smartseq.uns['dataset']='smartseq'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02e0d8ab-5564-4649-9161-fea681003dea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Chari&Pachter scaling\n",
    "adata_smartseq.layers['X_scaled'] = sc.pp.scale(adata_smartseq.X.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c688940f-6ddf-4e56-b731-c8710adc31db",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "adata_smartseq.write_h5ad('../data/preprocessed/smartseq_adata.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e79feb36-bd90-444c-89cb-95836d45556d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3850 × 1999\n",
       "    obs: 'clusternames', 'clusterlabels', 'colors'\n",
       "    uns: 'dataset'\n",
       "    layers: 'X_scaled'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_smartseq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "453c403c-05c8-4c8d-9a5d-c31749aed4dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Nr5a1_9|11 Rorb</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nr5a1_9|11 Rorb</td>\n",
       "      <td>18</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>3848</th>\n",
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       "      <td>19</td>\n",
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       "      <th>3849</th>\n",
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       "      <td>19</td>\n",
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       "</table>\n",
       "<p>3850 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              clusternames  clusterlabels   colors\n",
       "0          Nr5a1_9|11 Rorb             18  #6C39A6\n",
       "1          Nr5a1_9|11 Rorb             18  #6C39A6\n",
       "2          Nr5a1_9|11 Rorb             18  #6C39A6\n",
       "3         Nr5a1_4|7 Glipr1             16  #763BFF\n",
       "4         Nr5a1_4|7 Glipr1             16  #763BFF\n",
       "...                    ...            ...      ...\n",
       "3845  Nr5a1_Foxp2_1 Prdm13             19  #D92AAB\n",
       "3846  Nr5a1_Foxp2_1 Prdm13             19  #D92AAB\n",
       "3847  Nr5a1_Foxp2_1 Prdm13             19  #D92AAB\n",
       "3848  Nr5a1_Foxp2_1 Prdm13             19  #D92AAB\n",
       "3849  Nr5a1_Foxp2_1 Prdm13             19  #D92AAB\n",
       "\n",
       "[3850 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_smartseq.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55fc15c-09c1-44a1-b837-23b3f4236cf7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Prepare MNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ff065ec-6efe-4555-b1c4-9d33be6a131c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jan/anaconda3/envs/elephant_analysis_env/lib/python3.11/site-packages/sklearn/datasets/_openml.py:1002: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "import sklearn.datasets\n",
    "#download\n",
    "x_mnist,y_mnist = sklearn.datasets.fetch_openml('mnist_784',version=1,return_X_y=True)\n",
    "\n",
    "#adjust datatypes\n",
    "y_mnist = y_mnist.values.astype(int)\n",
    "x_mnist = x_mnist.values\n",
    "\n",
    "#metadata\n",
    "colors_tab10 = np.array(['tab:blue','tab:orange','tab:green','tab:red','tab:purple','tab:brown','tab:pink','tab:gray','tab:olive','tab:cyan'])\n",
    "colors_mnist = colors_tab10[y_mnist]\n",
    "\n",
    "#make adata and save\n",
    "adata_mnist = anndata.AnnData(X=x_mnist,\n",
    "                               obs=dict(clusterlabels=y_mnist,\n",
    "                                        clusternames=[str(y) for y in y_mnist],\n",
    "                                        colors=colors_mnist),\n",
    "                               uns=dict(dataset='mnist'))\n",
    "adata_mnist.layers['X_scaled'] = sc.pp.scale(adata_mnist.X.copy())\n",
    "adata_mnist.write_h5ad('../data/preprocessed/mnist_adata.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c190c2ea-155a-4343-8969-0c46fb750a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 70000 × 784\n",
       "    obs: 'clusterlabels', 'clusternames', 'colors'\n",
       "    uns: 'dataset'\n",
       "    layers: 'X_scaled'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2cfc9b9b-c3bb-46f1-abe9-28e05ac7015b",
   "metadata": {
    "tags": []
   },
   "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>clusterlabels</th>\n",
       "      <th>clusternames</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>tab:brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>tab:purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>tab:cyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69995</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>tab:green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69996</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>tab:red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69997</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>tab:purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69998</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>tab:brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69999</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>tab:pink</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       clusterlabels clusternames      colors\n",
       "0                  5            5   tab:brown\n",
       "1                  0            0    tab:blue\n",
       "2                  4            4  tab:purple\n",
       "3                  1            1  tab:orange\n",
       "4                  9            9    tab:cyan\n",
       "...              ...          ...         ...\n",
       "69995              2            2   tab:green\n",
       "69996              3            3     tab:red\n",
       "69997              4            4  tab:purple\n",
       "69998              5            5   tab:brown\n",
       "69999              6            6    tab:pink\n",
       "\n",
       "[70000 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_mnist.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe2269e-67d4-4ae1-b5d2-15f23d480fb8",
   "metadata": {},
   "source": [
    "### Copyright notice\n",
    "\n",
    "This notebook uses code adapted from https://github.com/pachterlab/CP_2023, which is subject to the following licence:\n",
    "\n",
    "```\n",
    "BSD 2-Clause License\n",
    "\n",
    "Copyright (c) 2021, Pachter Lab\n",
    "All rights reserved.\n",
    "\n",
    "Redistribution and use in source and binary forms, with or without\n",
    "modification, are permitted provided that the following conditions are met:\n",
    "\n",
    "1. Redistributions of source code must retain the above copyright notice, this\n",
    "   list of conditions and the following disclaimer.\n",
    "\n",
    "2. Redistributions in binary form must reproduce the above copyright notice,\n",
    "   this list of conditions and the following disclaimer in the documentation\n",
    "   and/or other materials provided with the distribution.\n",
    "\n",
    "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n",
    "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n",
    "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n",
    "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n",
    "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n",
    "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n",
    "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n",
    "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n",
    "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n",
    "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba1dd0c-6378-49d8-b0b8-bcb1b795dd03",
   "metadata": {},
   "source": [
    "### Package versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "93adc729-7db8-4ad9-859d-af6c8de62809",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.24.3'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "68e09f56-bde1-4c02-8c92-8bb51e3c0ab0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.3'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "497e142a-40fd-4087-9388-60d71030bca1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.10.3'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "57f410e4-87b5-44e5-a008-5690d402aacd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.9.6'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8a9aeaa3-0027-48ee-b323-63873f0f4d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.11.1'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy; scipy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "729d6c83-8db5-4869-92ad-1612cc10410d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.31.0'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requests.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8403c1e0-f065-4d49-ada6-0ff6760c09d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.66.2'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tqdm; tqdm.__version__"
   ]
  }
 ],
 "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": "3.11.5"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}