0. TF_info_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "professional-paragraph",
   "metadata": {},
   "source": [
    "# TF info preprocessing\n",
    "Preprocessing the TF-motif databases from SCENIC to use them as input in CellOracle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fiscal-awareness",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "necessary-chamber",
   "metadata": {},
   "source": [
    "## 1. Load Expression Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "least-currency",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad(\"./inputs/pancreas_data.h5ad\") #this is the pancreas dataset from scvelo (already preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "patent-angel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sntg1', 'Snhg6', 'Ncoa2', ..., 'Ddx3y', 'Eif2s3y', 'Erdr1'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of all the genes in our dataset\n",
    "genes = np.asarray(adata.to_df().columns)\n",
    "genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "functional-diary",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create .txt with the list of Pancreas genes\n",
    "#with open('pancreas_genes.txt', 'w') as f:\n",
    "#    for item in list(adata.to_df().columns):\n",
    "#        f.write(\"%s\\n\" % item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "skilled-voice",
   "metadata": {},
   "source": [
    "## 2. Load List of Transcription Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "foster-citizen",
   "metadata": {},
   "outputs": [],
   "source": [
    "TFs_all = pd.read_csv(\"./inputs/mm_mgi_tfs.txt\", header = None) #list of TFs for mus musculus from SCENIC\n",
    "TFs_pancreas = np.intersect1d(genes, TFs_all, assume_unique=False, return_indices=False) #list of TFs for pancreas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flying-river",
   "metadata": {},
   "source": [
    "## 3. Load Motif - TF table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "british-spiritual",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>gene_name</th>\n",
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       "          #motif_id gene_name\n",
       "0  bergman__EcR_usp     Nr1h4\n",
       "1  bergman__EcR_usp     Nr2f6\n",
       "2  bergman__EcR_usp       Vdr\n",
       "3  bergman__Eip74EF      Egr1\n",
       "4  bergman__Eip74EF       Ehf"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TF-motif list, filter to have only the genes that appear in our dataset\n",
    "TF_info = pd.read_table(\"../scenic/inputs/motifs-v9-nr.mgi-m0.001-o0.0.tbl\", usecols = [\"#motif_id\",'gene_name'])\n",
    "TF_info = TF_info[TF_info['gene_name'].isin(TFs_pancreas)].reset_index(drop = True)\n",
    "TF_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coated-nation",
   "metadata": {},
   "source": [
    "## 4. Load Ranking databases (Motif - genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "linear-tribute",
   "metadata": {},
   "outputs": [
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       "   0610007C21Rik  0610007L01Rik  0610007P08Rik  0610007P14Rik  0610007P22Rik  \\\n",
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Motif ranking database\n",
    "feather1=pd.read_feather(\"../scenic/inputs/mm9-500bp-upstream-10species.mc9nr.feather\")\n",
    "feather1 = feather1[feather1.features.isin(TF_info['#motif_id'].unique())].reset_index(drop = True)\n",
    "names1 = feather1.features\n",
    "feather1 = (feather1.drop([\"features\"], axis = 1) <= 1500).astype(int).drop(feather1.columns[feather1.sum()==0], axis=1)\n",
    "feather1['features'] = np.asarray(names1)\n",
    "feather1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "heated-circumstances",
   "metadata": {},
   "source": [
    "## 5. Merge everything to obtain dictionary with target genes and transcription factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "boolean-eating",
   "metadata": {},
   "outputs": [],
   "source": [
    "#map region with transcription factor\n",
    "feather1 =TF_info.merge(feather1, left_on=\"#motif_id\", right_on=\"features\", how=\"right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "functioning-rebate",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep only transcription factors, region was only for the mapping\n",
    "TF_genes = feather1.groupby(\"gene_name\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "collect-audience",
   "metadata": {},
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       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>210</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>Zfp710</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp90</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp949</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfpm1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zscan26</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 1857 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0610010F05Rik  0610011F06Rik  0610030E20Rik  1110002L01Rik  \\\n",
       "gene_name                                                               \n",
       "A1cf                   0              0              0              0   \n",
       "Arg1                   0              0              0              0   \n",
       "Arid3a                 2             31              0              0   \n",
       "Arid5b                 0              7              0              0   \n",
       "Arx                   11             12              0              0   \n",
       "...                  ...            ...            ...            ...   \n",
       "Zfp710                 0              0              0              0   \n",
       "Zfp90                  0              0              0              0   \n",
       "Zfp949                 0              1              0              0   \n",
       "Zfpm1                  0              0              0              0   \n",
       "Zscan26                0              1              0              0   \n",
       "\n",
       "           1110034G24Rik  1500009L16Rik  1700011H14Rik  1700026L06Rik  \\\n",
       "gene_name                                                               \n",
       "A1cf                   0              0              0              0   \n",
       "Arg1                   0              0              0              0   \n",
       "Arid3a                21              7             16             89   \n",
       "Arid5b                 0              1              0              1   \n",
       "Arx                    4              9            210              6   \n",
       "...                  ...            ...            ...            ...   \n",
       "Zfp710                 0              0              1              0   \n",
       "Zfp90                  0              3              0              2   \n",
       "Zfp949                 0              1              0              0   \n",
       "Zfpm1                  2              0              0              0   \n",
       "Zscan26                0              0              0              0   \n",
       "\n",
       "           1700086L19Rik  1810041L15Rik  ...  Zfp804a  Zfp90  Zfp949  Zfpm1  \\\n",
       "gene_name                                ...                                  \n",
       "A1cf                   0              0  ...        0      0       0      0   \n",
       "Arg1                   0              0  ...        0      0       0      0   \n",
       "Arid3a                 7              0  ...       10      0       0      0   \n",
       "Arid5b                 0              0  ...        7      0       0      0   \n",
       "Arx                    0              1  ...       58      0       0      0   \n",
       "...                  ...            ...  ...      ...    ...     ...    ...   \n",
       "Zfp710                 0              1  ...        0      0       0      1   \n",
       "Zfp90                  0              0  ...        0      0       0      0   \n",
       "Zfp949                 0              0  ...        0      0       0      0   \n",
       "Zfpm1                  0              0  ...        0      0       0      0   \n",
       "Zscan26                2              0  ...        0      0       0      0   \n",
       "\n",
       "           Zfr2  Zim1  Zmym5  Znrf2  Zswim5  Zwint  \n",
       "gene_name                                           \n",
       "A1cf          0     0      0      0       0      0  \n",
       "Arg1          0     0      0      1       0      0  \n",
       "Arid3a        6     0      0    201      26      0  \n",
       "Arid5b        0     0      0      3       1      0  \n",
       "Arx           0     0      1    191       0      0  \n",
       "...         ...   ...    ...    ...     ...    ...  \n",
       "Zfp710        0     0      0      2       1      0  \n",
       "Zfp90         0     0      0      1       3      0  \n",
       "Zfp949        0     0      0      0       0      0  \n",
       "Zfpm1         0     0      0      0       0      0  \n",
       "Zscan26       0     0      0      0       1      0  \n",
       "\n",
       "[177 rows x 1857 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#only select the ones that are in our dataset\n",
    "TF_genes = (TF_genes.T[TF_genes.T.index.isin(genes)]).T\n",
    "TF_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "impaired-fence",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the dictionary of TFs and target genes\n",
    "TF_targets = {}\n",
    "for i, col in enumerate(TF_genes.columns):\n",
    "    TF_targets[col] = list(TF_genes[col][TF_genes[col]>0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "spectacular-national",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('TF_targets_pancreas.json', 'w') as fp:\n",
    "    json.dump(TF_targets, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "infrared-burst",
   "metadata": {},
   "outputs": [],
   "source": [
    "#path = '../scenic/inputs/'\n",
    "#datasets = {'500-10':'mm9-500bp-upstream-10species.mc9nr.feather', \n",
    "#            '500-7':'mm9-500bp-upstream-7species.mc9nr.feather',\n",
    "#            '10-10':'mm9-tss-centered-10kb-10species.mc9nr.feather',\n",
    "#            '10-7':'mm9-tss-centered-10kb-7species.mc9nr.feather',\n",
    "#            '5-10':'mm9-tss-centered-5kb-10species.mc9nr.feather',\n",
    "#            '5-7':'mm9-tss-centered-5kb-7species.mc9nr.feather'}"
   ]
  }
 ],
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