midbrain_development/midbrain_development.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "import sys\n",
    "from tqdm import tqdm\n",
    "import psix\n",
    "\n",
    "cm = 1/2.54\n",
    "\n",
    "plt.rcParams[\"axes.edgecolor\"] = \"black\"\n",
    "plt.rcParams[\"axes.linewidth\"] = 1\n",
    "plt.rcParams[\"axes.facecolor\"] = 'white'\n",
    "\n",
    "mpl.rcParams[\"mathtext.fontset\"] = \"stix\"\n",
    "mpl.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running Psix in mouse midbrain neurons\n",
    "\n",
    "Here we analyse a smart-seq2 dataset from [Tiklova et al (2019)](https://www.nature.com/articles/s41467-019-08453-1) that follows the development of brain neurons at different stages of development: E13.5, E15.5, E18.5, P0, P7, P15 and P90.\n",
    "\n",
    "This dataset originally consists of 1699 cells. However, after quality control and normalization, we removed many cells that either had poor coverage, or that were found to be non-neural cells (mostly oligodendrocytes). This left a total of 1067 single neurons. After preprocessing, we observe a clear trajectory of neuron development in this dataset:\n",
    "\n",
    "<img src=\"psix_output/plots/PCA_neurogenesis3D_legend.png\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The alignment data necessary to run Psix is provided in ```'data/'```: \n",
    "\n",
    "* ```data/SJ_tables/``` contains the SJ.out.tab produced directly from STAR alignment of the raw reads to the mm10 genome.\n",
    "* ```data/mm10_introns.tab.gz``` is the cassette exon intron annotation, downloaded from https://github.com/lareaulab/psix\n",
    "* ```data/tpm.tab.gz``` is a TPM file obtained with running RSEM with STAR alignment to the mm10 genome.\n",
    "\n",
    "We preprocessed the splice junctions and TPM counts with the following command:\n",
    "\n",
    "```python\n",
    "psix_object = psix.Psix()\n",
    "psix_object.junctions2psi(\n",
    "        'data/SJ_tables/',\n",
    "        'data/mm10_introns.tab.gz',\n",
    "        'data/tpm.tab.gz',\n",
    "        save_files_in='psix_output/psix_object/'\n",
    "    )\n",
    "```\n",
    "\n",
    "This results in two tables:\n",
    "* ```psix_output/psix_object/psi.tab.gz```, the $\\hat{\\Psi}$ of observed cassette exons in the dataset.\n",
    "* ```psix_output/psix_object/mrna.tab.gz```, the estimated mRNA counts per exon observed in the dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting splice junctions....\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [03:02<00:00,  5.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining PSI tables...\n",
      "Reading TPM and transforming to mRNA counts...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:48<00:00, 22.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully processed RNA-seq data\n"
     ]
    }
   ],
   "source": [
    "psix_object = psix.Psix()\n",
    "psix_object.junctions2psi(\n",
    "        'data/SJ_tables/',\n",
    "        'data/mm10_introns.tab.gz',\n",
    "        'data/tpm.tab.gz',\n",
    "        save_files_in='psix_output/psix_object/'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have produced the $\\hat{\\Psi}$ and mRNA tables, we can run Psix. Next time we run Psix, we can also skip the latest step by creating a Psix object directly from the $\\hat{\\Psi}$ and mRNA tables as follows (this step is not necessary after running ```junctions2psi```):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "psix_object = psix.Psix(psi_table = 'psix_output/psix_object/psi.tab.gz',\n",
    "                        mrna_table = 'psix_output/psix_object/mrna.tab.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying cell-state associated exons with Psix\n",
    "\n",
    "For running Psix, we need a metric that indicates the phenotypic of the individual cells. In this case, we use the PCA of the normalized gene expression, which is located at ```data/pc3_rd.tab.gz```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 3086.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:19<00:00, 106.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [08:48<00:00, 21.16s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n"
     ]
    }
   ],
   "source": [
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Psix results\n",
    "\n",
    "Running Psix creates ```psix_object.psix_results```, a dataframe with the Psix score ($\\Psi_\\xi$) of every exon, its empirical $p$-value, and it's $q$-value after Benjamini-Hochberg correction. These are the top scoring exons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>psix_score</th>\n",
       "      <th>pvals</th>\n",
       "      <th>qvals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mapt_1</th>\n",
       "      <td>2.709381</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ndrg4_1</th>\n",
       "      <td>2.359093</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dbn1_1</th>\n",
       "      <td>2.302729</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mapt_3</th>\n",
       "      <td>1.964202</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabrg2_1</th>\n",
       "      <td>1.896363</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadm1_10</th>\n",
       "      <td>1.576869</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ncam1_6</th>\n",
       "      <td>1.456006</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dst_5</th>\n",
       "      <td>1.449287</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadm1_3</th>\n",
       "      <td>1.411169</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morf4l2_7</th>\n",
       "      <td>1.363965</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           psix_score   pvals     qvals\n",
       "Mapt_1       2.709381  0.0005  0.001879\n",
       "Ndrg4_1      2.359093  0.0005  0.001879\n",
       "Dbn1_1       2.302729  0.0005  0.001879\n",
       "Mapt_3       1.964202  0.0005  0.001879\n",
       "Gabrg2_1     1.896363  0.0005  0.001879\n",
       "Cadm1_10     1.576869  0.0005  0.001879\n",
       "Ncam1_6      1.456006  0.0005  0.001879\n",
       "Dst_5        1.449287  0.0005  0.001879\n",
       "Cadm1_3      1.411169  0.0005  0.001879\n",
       "Morf4l2_7    1.363965  0.0005  0.001879"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psix_object.psix_results.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the top scoring exons, and indicate the score of some cassette exons that are known to change during neurogenesis (this plot is not a Psix feature)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2087/2087 [00:12<00:00, 171.04it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 226.772x141.732 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psix_results = psix_object.psix_results\n",
    "psix_results['bins'] = ['']*len(psix_results.index)\n",
    "\n",
    "for mean in psix_object.bins.keys():\n",
    "    for var in psix_object.bins[mean].keys():\n",
    "        for exon in psix_object.bins[mean][var]:\n",
    "            psix_results.loc[exon, 'bins'] = mean +'_' + var\n",
    "            \n",
    "fig = plt.figure(figsize=(8*cm, 5*cm))\n",
    "ax  = plt.subplot(1,1,1)\n",
    "i = 1\n",
    "\n",
    "ax.scatter([100], [-0.05], c='gray', label='random exons', s=10)\n",
    "\n",
    "for mean_var in tqdm(psix_results.sort_values('psix_score')[::-1].bins):\n",
    "    mean = mean_var[:6]\n",
    "    var = mean_var[7:]\n",
    "    subset = np.random.choice(psix_object.random_scores[mean][var], 50, replace=False)\n",
    "    ax.scatter([i]*50, subset, c='gray', alpha=0.02, s=10, edgecolors='none')\n",
    "    i += 1\n",
    "\n",
    "\n",
    "plt.text(100, psix_results.loc['Mapt_1'].psix_score-0.05, 'Mapt, exon 10', c='black', fontsize=7)   \n",
    "ax.plot([20, 80], [psix_results.loc['Mapt_1'].psix_score, psix_results.loc['Mapt_1'].psix_score],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(101, psix_results.loc['Mapt_1'].psix_score-0.25, 'Ndrg4', c='black', fontsize=7)   \n",
    "ax.plot([20, 81], [psix_results.loc['Ndrg4_1'].psix_score, psix_results.loc['Mapt_1'].psix_score-0.2],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(102, psix_results.loc['Mapt_1'].psix_score-0.45, 'Dbn1', c='black', fontsize=7)   \n",
    "ax.plot([20, 82], [psix_results.loc['Dbn1_1'].psix_score, psix_results.loc['Mapt_1'].psix_score-0.4],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(103, psix_results.loc['Mapt_1'].psix_score-0.65, 'Mapt, exon 2', c='black', fontsize=7)    \n",
    "ax.plot([24, 83], [psix_results.loc['Mapt_3'].psix_score, psix_results.loc['Mapt_1'].psix_score-0.6],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(104, psix_results.loc['Mapt_1'].psix_score-0.85, 'Gabrg2', c='black', fontsize=7)   \n",
    "ax.plot([25, 84], [psix_results.loc['Gabrg2_1'].psix_score, psix_results.loc['Mapt_1'].psix_score-0.8],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(105, psix_results.loc['Mapt_1'].psix_score-1.05, 'Cadm1', c='black', fontsize=7)    \n",
    "ax.plot([24, 85], [psix_results.loc['Cadm1_10'].psix_score, psix_results.loc['Mapt_1'].psix_score-1],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(106, psix_results.loc['Mapt_1'].psix_score-1.25, 'Ncam', c='black', fontsize=7)   \n",
    "ax.plot([25, 86], [psix_results.loc['Ncam1_6'].psix_score, psix_results.loc['Mapt_1'].psix_score-1.2],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(107, psix_results.loc['Mapt_1'].psix_score-1.45, 'Dst', c='black', fontsize=7)    \n",
    "ax.plot([24, 87], [psix_results.loc['Dst_5'].psix_score, psix_results.loc['Mapt_1'].psix_score-1.4],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(109, psix_results.loc['Mapt_1'].psix_score-1.65, 'Morf4l2', c='black', fontsize=7)   \n",
    "ax.plot([25, 89], [psix_results.loc['Morf4l2_7'].psix_score, psix_results.loc['Mapt_1'].psix_score-1.6],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(129, psix_results.loc['Mapt_1'].psix_score-1.85, 'Cadps, exon 23', c='black', fontsize=7)   \n",
    "ax.plot([55, 109], [psix_results.loc['Cadps_7'].psix_score, psix_results.loc['Mapt_1'].psix_score-1.8],c='black', linewidth=0.7)\n",
    "\n",
    "\n",
    "plt.text(177, psix_results.loc['Mapt_1'].psix_score-2.05, 'Rpn2', c='black', fontsize=7)   \n",
    "ax.plot([65, 157], [psix_results.loc['Rpn2_3'].psix_score, psix_results.loc['Mapt_1'].psix_score-2],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(214, psix_results.loc['Mapt_1'].psix_score-2.25, 'Cadps, exon 20', c='black', fontsize=7)   \n",
    "ax.plot([105, 194], [psix_results.loc['Cadps_2'].psix_score, psix_results.loc['Mapt_1'].psix_score-2.2],c='black', linewidth=0.7)\n",
    "\n",
    "plt.text(331, psix_results.loc['Mapt_1'].psix_score-2.45, 'Tecr', c='black', fontsize=7)   \n",
    "ax.plot([125, 311], [psix_results.loc['Tecr_6'].psix_score, psix_results.loc['Mapt_1'].psix_score-2.4],c='black', linewidth=0.7)\n",
    "\n",
    "ax.scatter(np.arange(1, len(psix_results.index)+1), psix_results.sort_values('psix_score')[::-1].psix_score, c = 'navy',\n",
    "          label='exons', s=10, zorder=200, edgecolors='none')\n",
    "    \n",
    "plt.xlabel('Psix rank', fontsize=12)\n",
    "plt.ylabel(r'exon score', fontsize=12)\n",
    "\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.tick_params(labelsize=12, length=5)\n",
    "plt.legend(frameon=False, fontsize=8)\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/psix_scores.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "# plt.savefig('psix_output/plots/psix_scores.png', bbox_inches='tight', res=20000, dpi =2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene ontology enrichment analysis \n",
    "\n",
    "We rank the genes that contain the top scoring exons, and run a GO enrichment analysis on Panther."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2087it [00:00, 5803.16it/s]\n"
     ]
    }
   ],
   "source": [
    "psix_results = psix_object.psix_results\n",
    "from tqdm import tqdm\n",
    "gene_list = []\n",
    "score_list = []\n",
    "for idx, row in tqdm(psix_results.iterrows()):\n",
    "    gene = idx.split('_')[0]\n",
    "    if gene in gene_list:\n",
    "        continue\n",
    "    else:\n",
    "        gene_list.append(gene)\n",
    "        score_list.append(row.psix_score)\n",
    "        \n",
    "with open('psix_output/go_analysis/psix.rnk', 'w') as fh:\n",
    "    for i in range(len(gene_list)):\n",
    "        line = gene_list[i] + '\\t' + str(score_list[i]) + '\\n'\n",
    "        fh.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "go_bio = pd.read_csv('psix_output/go_analysis/analysis_go_biological_function.txt', \n",
    "                     sep='\\t', index_col=0, skiprows=4).sort_values('fdr')[::-1]\n",
    "idx = [x.split(' (')[0] for x in go_bio.index]\n",
    "go_bio.index = idx\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6/2.4, 6/2.4))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.barh(go_bio.index, -np.log10(go_bio.fdr))\n",
    "ax.set_xlabel('-log10 q values', size=8)\n",
    "# ax.set_ylabel('PC 2', size=18)\n",
    "ax.set_title('Biological process', size=8)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.tick_params(axis='x', labelsize=8, length=5)\n",
    "ax.tick_params(axis='y', labelsize=8, length=5)\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/GO_biological_function.png', bbox_inches='tight', res=20000, dpi =2000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying modules of correlated exons with Psix\n",
    "\n",
    "Once we have identified the cell-state associated exons, we can obtain modules of exons correlated across the cell-state space. If we chose, we can also generate a heatmap of the correlation between exons, associated in modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x270 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psix_object.compute_modules(plot = True, plot_name = '/mnt/lareaulab/cfbuenabadn/plots_pdf/exon_modules.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x270 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psix_object.compute_modules(plot = True, plot_name = 'psix_output/plots/exon_modules.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The modules for each cell-state associated exon can be found at ```psix_object.modules```. Each module is assigned an integer. Exons that are not assigned to any module are labeled with -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Slc30a9_1        1\n",
       "Slc30a9_2        1\n",
       "Cnot2_nmdSE_1    1\n",
       "Cnot2_nmdSE_2    1\n",
       "Xpa_nmdSE_1      1\n",
       "                ..\n",
       "Mrpl55_1        -1\n",
       "Trub2_2         -1\n",
       "Ndufaf6_2       -1\n",
       "Ncald_7         -1\n",
       "Slc39a9_2       -1\n",
       "Name: Modules, Length: 798, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psix_object.modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting module exon usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psix_object.plot_modules('psix_output/plots/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving a Psix object\n",
    "\n",
    "Finally, we can save the results from Psix for future uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psix_object.save_psix_object(psix_dir = 'psix_output/psix_object/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next time we need to run Psix, we can load the results of the previous run (including the $\\hat{\\Psi}$ and mRNA tables, the Psix scores, and modules) by creating a new Psix object as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "psix_object = psix.Psix(psix_object = 'psix_output/psix_object/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splicing factor binding and correlation\n",
    "\n",
    "Next we investigated what are the potential regulators of the cell-state associated exons. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import hypergeom\n",
    "from scipy.stats.mstats import zscore\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "import seaborn as sns\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "def hyper_test(M, n, N, k):\n",
    "    '''\n",
    "    Calculates the hypergeometric test.\n",
    "    \n",
    "    Input:\n",
    "      M: Population size (total exons tested)\n",
    "      n: Successess in population (exons with p-value <= x)\n",
    "      N: Sample size (exons selected)\n",
    "      k: Successes in sample (selected exons with p-value <= x)\n",
    "    Output:\n",
    "      p_depleted: p-value of depletion from the hypergeometric test\n",
    "      p_enriched: p-value of enrichment from the hypergeometric test\n",
    "    '''\n",
    "    hpd = hypergeom(M, n, N) # Hypergeometric distribution\n",
    "    p_depleted = hpd.cdf(k)  # Get cummulative distribution up to k\n",
    "    p_enriched = hpd.sf(k-1) # Survival function up to k-1 (1 - cdf(k-1))\n",
    "    return p_depleted, p_enriched\n",
    "\n",
    "\n",
    "def test_enrichment(binding, exon_cluster):\n",
    "    M = len(binding)\n",
    "    n = binding.sum()\n",
    "    N = len(exon_cluster)\n",
    "    k = binding.loc[exon_cluster].sum()\n",
    "    p_depleted, p_enriched = hyper_test(M, n, N, k)\n",
    "    \n",
    "    enrichment = (k/N)/(n/M)\n",
    "    \n",
    "    if enrichment == 0:\n",
    "        log_enrich = -3\n",
    "    else:\n",
    "        log_enrich = np.log2(enrichment)\n",
    "        \n",
    "    \n",
    "    return p_enriched, p_depleted, log_enrich\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "psix_object.latent = pd.read_csv('data/pc3_rd.tab.gz', sep='\\t', index_col=0)\n",
    "psix_results = psix_object.psix_results\n",
    "norm_expression = pd.read_csv('data/preprocess/scone_norm_tpm_extended.tab.gz', sep='\\t', index_col=0)[psix_object.latent.index]\n",
    "# norm_expression = pd.read_csv('data/preprocess/scone_norm_tpm.tab.gz', sep='\\t', index_col=0)[psix_object.latent.index]\n",
    "psi = psix_object.adata.uns['psi'].T\n",
    "modules = psix_object.modules\n",
    "clip_dir = 'data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in finding which splicing factors regulate the cell-state associated exons that we identified with Psix. In order to do that, we use CLIP-seq data for the splicing factors of interest. We indicate if a splicing factor is bound to the exon, if we observe a CLIP-seq peak overlaping with any of the following four regions:\n",
    "\n",
    "\n",
    "* E1 region: 100 nt upstream and $i_1$ nt downstream from the splice junction of the upstream flanking exon. Where $i_1$ is the minimum of 100 and half the distance between the upstream flanking exon and the cassette exon.\n",
    "* S1 region: $i_1$ nt upstream and $e$ nt downstream from the 5' splice junction of the cassette exon. Where $e$ is the minimum of 100 and half the distance between the two splice junctions of the cassette exon.\n",
    "* S2 region: $e$ nt upstream and $i_2$ nt downstream from the 3' splice junction of the cassette exon. Where $i_2$ is the minimum of 100 and half the distance between the cassette exon and the downstream flanking exon.\n",
    "* E2 region: $i_2$ nt upstream and 100 nt downstream from the splice junction of the downstream flanking exon.\n",
    "\n",
    "This data is available in ```data/rbp_binding/peaks_clip_tags_overlap_len.tab.gz```. \n",
    "\n",
    "We use a hypergeometric test to test if the set of cell-state associated exons are enriched for each splicing factor, compared with the total exons we tested: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significantly bound splicing factors:\n",
      "\n",
      "CELF4\n",
      "FUS\n",
      "MBNL1MBNL2\n",
      "MBNL2\n",
      "NOVA1\n",
      "NOVA2\n",
      "PTBP2\n",
      "RBFOX1\n",
      "RBFOX2\n",
      "RBFOX3\n",
      "SRSF3\n",
      "SRSF4\n",
      "SRSF5\n",
      "SRSF6\n",
      "TARDBP\n",
      "U2AF2\n"
     ]
    }
   ],
   "source": [
    "# clip_dir = '~/Network/Mouse/SplicingNetwork/psix_events/clip_seq/'\n",
    "\n",
    "mouse_clip_peaks = (pd.read_csv('data/rbp_binding/peaks_clip_tags_overlap_len.tab.gz', sep='\\t', index_col=0) > 0).astype(int)\n",
    "s1_exons = [x for x in mouse_clip_peaks.index if x[-3:]=='_s1']\n",
    "s2_exons = [x for x in mouse_clip_peaks.index if x[-3:]=='_s2']\n",
    "e1_exons = [x for x in mouse_clip_peaks.index if x[-3:]=='_e1']\n",
    "e2_exons = [x for x in mouse_clip_peaks.index if x[-3:]=='_e2']\n",
    "\n",
    "new_idx = [x[:-3] for x in mouse_clip_peaks.index if x[-3:] == '_e1']\n",
    "mouse_clip_peaks_e1 = mouse_clip_peaks.loc[[x for x in mouse_clip_peaks.index if x[-3:] == '_e1']]\n",
    "mouse_clip_peaks_s1 = mouse_clip_peaks.loc[[x for x in mouse_clip_peaks.index if x[-3:] == '_s1']]\n",
    "mouse_clip_peaks_s2 = mouse_clip_peaks.loc[[x for x in mouse_clip_peaks.index if x[-3:] == '_s2']]\n",
    "mouse_clip_peaks_e2 = mouse_clip_peaks.loc[[x for x in mouse_clip_peaks.index if x[-3:] == '_e2']]\n",
    "\n",
    "mouse_clip_peaks_e1.index = new_idx\n",
    "mouse_clip_peaks_s1.index = new_idx\n",
    "mouse_clip_peaks_s2.index = new_idx\n",
    "mouse_clip_peaks_e2.index = new_idx\n",
    "\n",
    "mouse_clip_peaks_any = ((mouse_clip_peaks_e1 + mouse_clip_peaks_s1 + mouse_clip_peaks_s2 + mouse_clip_peaks_e2) > 0).astype(int)\n",
    "\n",
    "\n",
    "# We exclude a few RNA binding proteins that are not splicing factors \n",
    "\n",
    "remove = ['EZH2', 'AGO2', 'LIN28A', 'CIRBP', 'FMR1', 'TAF15', 'UPF1']\n",
    "splicing_factors =  [x for x in mouse_clip_peaks_any.columns if x not in remove]\n",
    "\n",
    "mouse_clip_peaks_any[splicing_factors]\n",
    "\n",
    "sig_exons = psix_results.loc[(psix_results.qvals <= 0.05) & (psix_results.psix_score > 0)].index\n",
    "\n",
    "pval_hyper = []\n",
    "enrichment_hyper = []\n",
    "\n",
    "for RBP in splicing_factors:\n",
    "        \n",
    "    region_exons = mouse_clip_peaks_any.index\n",
    "\n",
    "    hyper = test_enrichment(mouse_clip_peaks_any.loc[psix_results.index, RBP], sig_exons)\n",
    "    pval_hyper.append(hyper[0])\n",
    "    enrichment_hyper.append(hyper[2])\n",
    "    \n",
    "print('Significantly bound splicing factors:\\n')\n",
    "for sf in sorted(np.array(splicing_factors)[multipletests(pval_hyper, method='fdr_bh')[0]]):\n",
    "    print(sf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we check the correlation between the expression of the splicing factors, and the $\\hat{\\Psi}$ of the exons in each module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 56.21it/s]\n"
     ]
    }
   ],
   "source": [
    "module_avg = pd.DataFrame()\n",
    "\n",
    "\n",
    "sf_list = ['Celf4', 'Cirbp', 'Fus', 'Lin28a', 'Mbnl1', 'Mbnl2', 'Nova1', 'Nova2', 'Ptbp1', 'Ptbp2',\n",
    "           'Rbfox1', 'Rbfox2', 'Rbfox3', 'Srsf1', 'Srsf2', 'Srsf3', 'Srsf4', 'Srsf6', 'Srsf7', 'Tardbp', 'U2af2']\n",
    "\n",
    "sf_list = [x for x in sf_list if x in norm_expression.index]\n",
    "\n",
    "for mod in range(1, 11):\n",
    "    module_avg['module_' + str(mod)] = np.nanmean(zscore(psi.loc[modules.index[modules == mod]], axis=1, \n",
    "                                             nan_policy='omit'), axis=0)\n",
    "    \n",
    "module_avg.index = psi.columns\n",
    "module_avg = module_avg.T\n",
    "\n",
    "\n",
    "module_avg_corr = pd.DataFrame(np.zeros((len(sf_list), len(module_avg.index))), \n",
    "                               index = sf_list, columns = module_avg.index)\n",
    "for sf in tqdm(sf_list):\n",
    "    for mod in module_avg.index:\n",
    "        module_avg_corr.loc[sf, mod] = pearsonr(module_avg.loc[mod],\n",
    "                                                norm_expression.loc[sf, module_avg.columns]\n",
    "        )[0]\n",
    "        \n",
    "mod_order = ['module_1', 'module_2', 'module_3', 'module_4', 'module_5',\n",
    "             'module_6', 'module_7', 'module_8', 'module_9', 'module_10']\n",
    "\n",
    "mod_cor = module_avg_corr.loc[sf_list, mod_order]\n",
    "mod_cor.columns = ['Mod 1', 'Mod 2', 'Mod 3', 'Mod 4', 'Mod 5',\n",
    "             'Mod 6', 'Mod 7', 'Mod 8', 'Mod 9', 'Mod 10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "module_avg_corr.columns = ['Mod 1', 'Mod 2', 'Mod 3', 'Mod 4', 'Mod 5',\n",
    "             'Mod 6', 'Mod 7', 'Mod 8', 'Mod 9', 'Mod 10']\n",
    "\n",
    "sf_list = [x for x in module_avg_corr.index if x in ['Celf4', 'Fus', 'Mbnl1', 'Mbnl2', 'Nova1', 'Nova2', 'Ptbp2',\n",
    "           'Rbfox1', 'Rbfox2', 'Rbfox3', 'Srsf3', 'Srsf4', 'Srsf5', 'Srsf6', 'Tardp', 'U2af2']]\n",
    "\n",
    "module_avg_corr = module_avg_corr.loc[sf_list]\n",
    "\n",
    "sns.clustermap(module_avg_corr.loc[module_avg_corr.abs().max(axis=1) >= 0.25], \n",
    "               col_cluster=False,\n",
    "               row_cluster=True, vmin=-0.75, vmax=0.75, cmap='RdBu_r', figsize=(8/2.4, 8/2.4))\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/module_correlations.pdf', bbox_inches='tight', res=20000, dpi =2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exon modules in adult midbrain cells\n",
    "\n",
    "We run Psix in the subset of cells corresponding to the postnatal day 90 (adult) midbrain. We can do thins by passing the argument ```cell_list``` when creating a Psix object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting splice junctions....\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 290/290 [01:04<00:00,  4.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining PSI tables...\n",
      "Reading TPM and transforming to mRNA counts...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 290/290 [00:13<00:00, 22.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully processed RNA-seq data\n"
     ]
    }
   ],
   "source": [
    "p90_cells = []\n",
    "with open('data/p90_cells.txt', 'r') as fh:\n",
    "    for cell in fh:\n",
    "        p90_cells.append(cell.rstrip())\n",
    "        \n",
    "psix_p90 = psix.Psix()\n",
    "psix_p90.junctions2psi(\n",
    "        '/mnt/lareaulab/cfbuenabadn/RNASeq/Mouse/Tiklova/SJ_tables/midbrain_neurons/',\n",
    "        '/mnt/lareaulab/cfbuenabadn/Genomes/pipeline_files/mm10_introns.tab',\n",
    "        '/mnt/lareaulab/cfbuenabadn/RNASeq/Mouse/Tiklova/tpm.tab.gz',\n",
    "        cell_list = p90_cells,\n",
    "        save_files_in='psix_output/psix_object_p90/'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reduce the neighborhood size to increase sensitivity, in the smaller group of cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 290/290 [00:00<00:00, 20018.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2115 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2115/2115 [00:07<00:00, 269.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [02:34<00:00,  6.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n"
     ]
    }
   ],
   "source": [
    "psix_p90.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, \n",
    "                                n_random_exons=2000, n_neighbors=30)#, pvals_bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 290/290 [00:00<00:00, 19259.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2115 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2115/2115 [00:10<00:00, 196.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [02:34<00:00,  6.19s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n"
     ]
    }
   ],
   "source": [
    "psix_p90.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, \n",
    "                                n_random_exons=2000, n_neighbors=30)#, pvals_bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules of exons in P90 cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x270 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psix_p90.compute_modules(z_threshold=0.1, \n",
    "                         plot = True,\n",
    "                         plot_name = '/mnt/lareaulab/cfbuenabadn/plots_pdf/exon_modules_p90.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.46409476329943783\n"
     ]
    }
   ],
   "source": [
    "sig_exons = psix_p90.psix_results.index[(psix_p90.psix_results.qvals <= 0.05) & (psix_p90.psix_results.psix_score >= 0)]\n",
    "psix_p90.adata.uns['neighbors_psi'][sig_exons]\n",
    "p90_eig = (np.linalg.eig(psix_p90.adata.uns['neighbors_psi'][sig_exons].corr().fillna(0))[0])\n",
    "print(p90_eig[0]/np.sum(p90_eig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.46409476329943783\n"
     ]
    }
   ],
   "source": [
    "sig_exons = psix_p90.psix_results.index[(psix_p90.psix_results.qvals <= 0.05) & (psix_p90.psix_results.psix_score >= 0)]\n",
    "psix_p90.adata.uns['neighbors_psi'][sig_exons]\n",
    "p90_eig = (np.linalg.eig(psix_p90.adata.uns['neighbors_psi'][sig_exons].corr().fillna(0))[0])\n",
    "print(p90_eig[0]/np.sum(p90_eig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "psix_p90.save_psix_object(psix_dir = 'psix_output/psix_object_p90/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psix_p90 = psix.Psix(psix_object = 'psix_output/psix_object_p90/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splicing in earlier stages of development\n",
    "\n",
    "We use Psix to get the $\\hat{\\Psi}$ tables in the postnatal day 1 (P1) and embryonic day 13.5 (E13.5) cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting splice junctions....\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 242/242 [00:51<00:00,  4.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining PSI tables...\n",
      "Reading TPM and transforming to mRNA counts...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 242/242 [00:10<00:00, 23.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully processed RNA-seq data\n"
     ]
    }
   ],
   "source": [
    "p1_cells = []\n",
    "with open('data/p1_cells.txt', 'r') as fh:\n",
    "    for cell in fh:\n",
    "        p1_cells.append(cell.rstrip())\n",
    "        \n",
    "psix_p1 = psix.Psix()\n",
    "psix_p1.junctions2psi(\n",
    "        '/mnt/lareaulab/cfbuenabadn/RNASeq/Mouse/Tiklova/SJ_tables/midbrain_neurons/',\n",
    "        '/mnt/lareaulab/cfbuenabadn/Genomes/pipeline_files/mm10_introns.tab',\n",
    "        '/mnt/lareaulab/cfbuenabadn/RNASeq/Mouse/Tiklova/tpm.tab.gz',\n",
    "        cell_list = p1_cells,\n",
    "        save_files_in='psix_output/psix_object_p1/',\n",
    "        minPsi = 0.001,\n",
    "        min_observed = 0.001,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting splice junctions....\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 240/240 [00:42<00:00,  5.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining PSI tables...\n",
      "Reading TPM and transforming to mRNA counts...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 240/240 [00:08<00:00, 26.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully processed RNA-seq data\n"
     ]
    }
   ],
   "source": [
    "e13_cells = []\n",
    "with open('data/e13_cells.txt', 'r') as fh:\n",
    "    for cell in fh:\n",
    "        e13_cells.append(cell.rstrip())\n",
    "        \n",
    "psix_e13 = psix.Psix()\n",
    "psix_e13.junctions2psi(\n",
    "        '/mnt/lareaulab/cfbuenabadn/RNASeq/Mouse/Tiklova/SJ_tables/midbrain_neurons/',\n",
    "        '/mnt/lareaulab/cfbuenabadn/Genomes/pipeline_files/mm10_introns.tab',\n",
    "        '/mnt/lareaulab/cfbuenabadn/RNASeq/Mouse/Tiklova/tpm.tab.gz',\n",
    "        cell_list = e13_cells,\n",
    "        save_files_in='psix_output/psix_object_e13/',\n",
    "        minPsi = 0.001,\n",
    "        min_observed = 0.001,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to see the splicing patterns of the two P90 modules, in the earlier stages of development. We compute the neighbor $\\bar{\\Psi}$, but we do not need to run Psix on these stages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 242/242 [00:00<00:00, 18807.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 240/240 [00:00<00:00, 15901.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed neighbors\n",
      "Successfully computed cell-cell metric\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed neighbors\n"
     ]
    }
   ],
   "source": [
    "psix_p1.compute_neighbors_psi(latent='data/pc3_rd.tab.gz', n_neighbors=30)\n",
    "psix_e13.compute_neighbors_psi(latent='data/pc3_rd.tab.gz', n_neighbors=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26962071236388335\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x270 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '/mnt/lareaulab/cfbuenabadn/psix_project/analysis_psix/utils/')\n",
    "from misc_utils import *\n",
    "\n",
    "\n",
    "psix_results = psix_p90.psix_results\n",
    "\n",
    "background_psi = psix_p90.adata.uns['neighbors_psi'].mask(psix_p90.adata.uns['psi'].isna()).T\n",
    "exon_list = psix_results.loc[(psix_results.psix_score > 0) & (psix_results.qvals <= 0.05)].index\n",
    "\n",
    "bwow_p90 = compute_modules(background_psi.loc[exon_list].T.corr().fillna(0), \n",
    "                min_gene_threshold=20, fdr_threshold=None, z_threshold=0.1, core_only=False)\n",
    "\n",
    "\n",
    "exon_list = exon_list & psix_p1.adata.uns['psi'].columns\n",
    "\n",
    "background_psi = psix_p1.adata.uns['neighbors_psi'].mask(psix_p1.adata.uns['psi'].isna()).T\n",
    "\n",
    "bwow_p1 = compute_modules(background_psi.loc[exon_list].T.corr().fillna(0), \n",
    "                min_gene_threshold=20, fdr_threshold=None, z_threshold=0.1, core_only=False)\n",
    "\n",
    "aver = local_correlation_plot(\n",
    "            background_psi.loc[exon_list].T.corr().fillna(0), bwow_p90[0].loc[bwow_p1[0].index], bwow_p1[1],\n",
    "            mod_cmap='tab10', vmin=-1, vmax=1,\n",
    "            z_cmap='RdBu_r', yticklabels=False,\n",
    "    plot_name = '/mnt/lareaulab/cfbuenabadn/plots_pdf/exon_modules_p1.pdf', shrink_by=1#/0.5\n",
    ")\n",
    "\n",
    "p1_eig = (np.linalg.eig(background_psi.loc[exon_list].T.corr().fillna(0))[0])\n",
    "print(p1_eig[0]/np.sum(p1_eig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23612222421547782\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x270 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '/mnt/lareaulab/cfbuenabadn/psix_project/analysis_psix/utils/')\n",
    "from misc_utils import *\n",
    "\n",
    "\n",
    "psix_results = psix_p90.psix_results\n",
    "\n",
    "background_psi = psix_p90.adata.uns['neighbors_psi'].mask(psix_p90.adata.uns['psi'].isna()).T\n",
    "exon_list = psix_results.loc[(psix_results.psix_score > 0) & (psix_results.qvals <= 0.05)].index\n",
    "\n",
    "bwow_p90 = compute_modules(background_psi.loc[exon_list].T.corr().fillna(0), \n",
    "                min_gene_threshold=20, fdr_threshold=None, z_threshold=0.1, core_only=False)\n",
    "\n",
    "\n",
    "exon_list = exon_list & psix_e13.adata.uns['psi'].columns\n",
    "\n",
    "background_psi = psix_e13.adata.uns['neighbors_psi'].mask(psix_e13.adata.uns['psi'].isna()).T\n",
    "\n",
    "bwow_e13 = compute_modules(background_psi.loc[exon_list].T.corr().fillna(0), \n",
    "                min_gene_threshold=20, fdr_threshold=None, z_threshold=0.1, core_only=False)\n",
    "\n",
    "aver = local_correlation_plot(\n",
    "            background_psi.loc[exon_list].T.corr().fillna(0), bwow_p90[0].loc[bwow_e13[0].index], bwow_e13[1],\n",
    "            mod_cmap='tab10', vmin=-1, vmax=1,\n",
    "            z_cmap='RdBu_r', yticklabels=False,\n",
    "    plot_name = '/mnt/lareaulab/cfbuenabadn/plots_pdf/exon_modules_e13.pdf', shrink_by=1#/0.5\n",
    ")\n",
    "\n",
    "e13_eig = (np.linalg.eig(background_psi.loc[exon_list].T.corr().fillna(0))[0])\n",
    "print(e13_eig[0]/np.sum(e13_eig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 165x69 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "latent = psix_p90.latent\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(5.5/2.4, 2.3/2.4))\n",
    "gs = GridSpec(1,2)\n",
    "gs.update(wspace=0.1, hspace=0.7)\n",
    "\n",
    "    \n",
    "ax_1 = fig.add_subplot(gs[0, 0])\n",
    "ax_2 = fig.add_subplot(gs[0, 1])\n",
    "\n",
    "ax_1.scatter(psix_p90.latent.PC_3, psix_p90.latent.PC_1,\n",
    "           c = zscore(psix_p90.adata.uns['neighbors_psi'].T.loc[psix_p90.modules.loc[psix_p90.modules == 1].index],\n",
    "       axis=1, nan_policy='omit').mean(axis=0), s=8, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax_1.set_ylabel('PC 1', fontsize=8)\n",
    "ax_1.set_xlabel('PC 3', fontsize=8)\n",
    "\n",
    "ax_1.set_xticks([])\n",
    "ax_1.set_yticks([])\n",
    "\n",
    "ax_1.spines[\"right\"].set_visible(False)\n",
    "ax_1.spines[\"top\"].set_visible(False)\n",
    "ax_1.spines['left'].set_linewidth(0.75)\n",
    "ax_1.spines['bottom'].set_linewidth(0.75)\n",
    "ax_1.set_title('Module A', fontsize=8)\n",
    "\n",
    "\n",
    "ax_2.scatter(psix_p90.latent.PC_3, psix_p90.latent.PC_1,\n",
    "           c = zscore(psix_p90.adata.uns['neighbors_psi'].T.loc[psix_p90.modules.loc[psix_p90.modules == 2].index],\n",
    "       axis=1, nan_policy='omit').mean(axis=0), s=8, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "# ax_2.set_ylabel('PC 1', fontsize=12)\n",
    "ax_2.set_xlabel('PC 3', fontsize=8)\n",
    "ax_2.set_xticks([])\n",
    "\n",
    "ax_2.set_yticks([])\n",
    "ax_2.spines[\"right\"].set_visible(False)\n",
    "ax_2.spines[\"top\"].set_visible(False)\n",
    "ax_2.spines['left'].set_linewidth(0.75)\n",
    "ax_2.spines['bottom'].set_linewidth(0.75)\n",
    "ax_2.set_title('Module B', fontsize=8)\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/p90_modules_scatterplot.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 165x69 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "latent = psix_p1.latent\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(5.5/2.4, 2.3/2.4))\n",
    "gs = GridSpec(1,2)\n",
    "gs.update(wspace=0.1, hspace=0.7)\n",
    "\n",
    "    \n",
    "ax_1 = fig.add_subplot(gs[0, 0])\n",
    "ax_2 = fig.add_subplot(gs[0, 1])\n",
    "\n",
    "idx_1 = psix_p90.modules.loc[psix_p90.modules == 1].index & psix_p1.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "ax_1.scatter(latent.PC_3, latent.PC_1,\n",
    "           c = np.nanmean(zscore(psix_p1.adata.uns['neighbors_psi'].T.loc[idx_1],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=8, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax_1.set_ylabel('PC 1', fontsize=8)\n",
    "ax_1.set_xlabel('PC 3', fontsize=8)\n",
    "\n",
    "ax_1.set_xticks([])\n",
    "ax_1.set_yticks([])\n",
    "\n",
    "ax_1.spines[\"right\"].set_visible(False)\n",
    "ax_1.spines[\"top\"].set_visible(False)\n",
    "ax_1.spines['left'].set_linewidth(0.75)\n",
    "ax_1.spines['bottom'].set_linewidth(0.75)\n",
    "ax_1.set_title('Module A', fontsize=8)\n",
    "\n",
    "\n",
    "\n",
    "idx_2 = psix_p90.modules.loc[psix_p90.modules == 2].index & psix_p1.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "ax_2.scatter(latent.PC_3, latent.PC_1,\n",
    "           c = np.nanmean(zscore(psix_p1.adata.uns['neighbors_psi'].T.loc[idx_2],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=8, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "# ax_2.set_ylabel('PC 1', fontsize=12)\n",
    "ax_2.set_xlabel('PC 3', fontsize=8)\n",
    "ax_2.set_xticks([])\n",
    "\n",
    "ax_2.set_yticks([])\n",
    "ax_2.spines[\"right\"].set_visible(False)\n",
    "ax_2.spines[\"top\"].set_visible(False)\n",
    "ax_2.spines['left'].set_linewidth(0.75)\n",
    "ax_2.spines['bottom'].set_linewidth(0.75)\n",
    "ax_2.set_title('Module B', fontsize=8)\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/p1_modules_scatterplot.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 165x69 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "latent = psix_e13.latent\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(5.5/2.4, 2.3/2.4))\n",
    "gs = GridSpec(1,2)\n",
    "gs.update(wspace=0.1, hspace=0.7)\n",
    "\n",
    "    \n",
    "ax_1 = fig.add_subplot(gs[0, 0])\n",
    "ax_2 = fig.add_subplot(gs[0, 1])\n",
    "\n",
    "idx_1 = psix_p90.modules.loc[psix_p90.modules == 1].index & psix_e13.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "ax_1.scatter(latent.PC_3, latent.PC_1,\n",
    "           c = np.nanmean(zscore(psix_e13.adata.uns['neighbors_psi'].T.loc[idx_1],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=8, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax_1.set_ylabel('PC 1', fontsize=8)\n",
    "ax_1.set_xlabel('PC 3', fontsize=8)\n",
    "\n",
    "ax_1.set_xticks([])\n",
    "ax_1.set_yticks([])\n",
    "\n",
    "ax_1.spines[\"right\"].set_visible(False)\n",
    "ax_1.spines[\"top\"].set_visible(False)\n",
    "ax_1.spines['left'].set_linewidth(0.75)\n",
    "ax_1.spines['bottom'].set_linewidth(0.75)\n",
    "ax_1.set_title('Module A', fontsize=8)\n",
    "\n",
    "\n",
    "\n",
    "idx_2 = psix_p90.modules.loc[psix_p90.modules == 2].index & psix_e13.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "ax_2.scatter(latent.PC_3, latent.PC_1,\n",
    "           c = np.nanmean(zscore(psix_e13.adata.uns['neighbors_psi'].T.loc[idx_2],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=8, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "# ax_2.set_ylabel('PC 1', fontsize=12)\n",
    "ax_2.set_xlabel('PC 3', fontsize=8)\n",
    "ax_2.set_xticks([])\n",
    "\n",
    "ax_2.set_yticks([])\n",
    "ax_2.spines[\"right\"].set_visible(False)\n",
    "ax_2.spines[\"top\"].set_visible(False)\n",
    "ax_2.spines['left'].set_linewidth(0.75)\n",
    "ax_2.spines['bottom'].set_linewidth(0.75)\n",
    "ax_2.set_title('Module B', fontsize=8)\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/e13_modules_scatterplot.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kw5n21SMoFovc+bMHSv1PqoKiKoydPIaW9a1YpkWiJcGUEybxpbOmAXDp9OvcPqhQLIQvOLi6dnfGQp1xxhl9HhNCPNDXsT3e5ZN5HVlhsD3d80FyjxOLxaioqHAjbvX19UQiETKZDKlUyg0sSAskryvdtZaWFlpbW+ns7CSbzeI4DoZhoGmaG/jQdZ1IJEJlZSWaprkBD5mw7erq6hGIyOfzpdB4UOfocw6nojbKAV8cx8QZ+7vi6Z4IlnSPMhYKBT54ZSWBsB/Dp3PUNw7ll//+GYoCqY40yY40137lRrdu78q//YipJx3MwV+eyC3z/jeaNrATudvjJXZ3AXJfI0PP3d0z27bdCF/3mrlcLudG0fx+v2tNisUiiUQCy7JIJpP4/X63KsJxHPcxUqSdnZ1EIhF0Xcfn81FbW+smeVVVJRwOYxgGdXV1bmRRRv0Mw3BF197ejq7rBINBtr7fwcZ3t3LIiQdy2hUnYfhKj5OCleLtDcMw2O/wsWxcvhlFVfnSWdNp29COXXTc3Nmm1VvZsGIT+x48hiFjGvjFY1fttveq3HiC2gXISmNFUchkMhiGQSqVcj90hmG4Ll42m6VYLGJZVo8SolAohGmahMNhLMsquVfbwtty3yOEIJ1OE4vFXKHJ6Fw0GqVYLKIoihtVlB982xQkk1mG7dPoJmRlJDAUKnXKVlRUUCgU0DSdu26cg205rHlnPftOGcOBX57gVk9UV1e7e7be8Pl8nH/jdzjsxCkEwn4e+fM83n15OeGaCpLtaYRwME0b/yDZI30anqB2ATJvk8/nUVXVjcrpuk40GnW/3UOhkOu+OY6DpmlugCIUCqFpGoFAgFgsRiAQcCsT4vE4mqbh8/nceQ7yg51IJNyqB7nBlUEI27bp2pzm1xfdSyFrcszMQ5l56fEYhiGz8QQCATdxrOt6KSfl1ygWijioBCMBNzwvn0N+CfQlKkVRmDh9f5YsXM47C94HwBYQG1Zbsqy6wV9ueIxDv/wFTjln2sfKqwYTXunRLqBQKGBZlpvz6ezsJJ1O097eTjqdprOzE7/f70bn2traaG1tJZPJ4DgOqqq61gYgHA5TW1tLVVUV4XAYTdPcc6XbZVkWgUCA2tpa4vG4++GW7p7jOEQiET54fR35TClMv+ipd9x9XCQSYdiwYVRWVrr9PJFIBL/fz+nXf4UDvjyeU684kTFTRvSoOJdi6i1Mvz0NI2rwbat8qG6IM+P0w9H9Pkw01q5sYc4dL/DS0+/2OEcIwe23PMX3v/Y7/vrbZ8r5Nu0SdkZQc+fO5YILLuj1mKIotyuKcmJvx/ZoC2WapptfUhTFjZilUqkeNV65XI5gMEg6ne6xh4nFYkQiETcnJH8vI4aKohAKhfD7S7MULMsin8/3KC9yHIdgMIhhGG7VeDAYxO/3c/DRE5j71xco5E0OOHKsu1cTQhAIBNxrBQIBIpEI1dXV1NfXc9C0iQghyGQyhMNhdF0nHA67+zDpMnZn1dZ2Xl+zgail8+DDrxEJ+Tn3t/+Dre9v5pjTDqWqIc43Lz6en19wN8nOUoVHcbv6vLWrtvLiMyWR/edf73Dc1w9i+OiBVxQLJfHvjKB+85vfcMMNN/R1+F7gBuCp7Q8MekF13+QLIcjn864AZFRKfvhLcxfUHh2cgUCAVCrFli1b3A+vZVnouu7ulfx+v+sWSmEEAgG3RV1WO+i63uM5u1sMGRpPJBJEo1GCwSBDx9Zz/YM/pGVjG+MOGu3mwhzHIZlMuhOM/H4/VVVVPXJSstRJht4BV9jydZFuZtYW/Pi+x8mZFs7KLCFHI5HM8qs/zyOcsukwBRdcdgINw2v4yU3f5NG7FtA0qpZpJ07s8VpXVkcJBn3kciahsJ/K6ggDlXw+7waDdoT33nuPadOm9XX4JWBCbwcGdS2frCIACAaD5HI5t4UiHA674pBuVnt7O4VCASEEkUjErc5ev349mUwGy7KIx+OEQiFs2yYajbof6mg0it/vJ5/PE41GMQzDdbVkyZHMW8nnkOuTbRvJZNLdD0UiEVfo6XS6VAGxLbSfyWTQdd0tyDUMg9raWtLptHuspqYGv9/vhuK3R1pLgOZEmgvvnUshaeJsKKAXBIaqEks7RCwwDI17nrj0M73m61e3sOSNtUw6ZBTDRtWW423cJWzZsoVJkyaxdevWHTpf1u/JOszuKIoSAzYKIT5m/ga1hXIcx/1/WY/X1taGaZruN3g4HHbD2bZtuxUMgJvbaW1tpVgsEo1G3eBCTU0NDQ0NdHV19ehvku5Vd/FAKY9l27ZbqS6R/U7SNZPPqygKwWAQRVGwLMsNlMh9mwzxy4CHaZoYhkEkEiEWi7nnAmxIJNBVlYZub75MUAOMa6xj//paXt68FjWkIDSwFGjoEoDCwUfs85lf8+Gj6wasm9ednQ1IHHTQQcyZM4dzzjmnt8OnAm/3dmBQC6q7WyaToYBbKpRMJqmpqcHn85FKpdzaLFkRUSgU3A93Pp9390mhUMhNskrByBB59z0T4J4jWzqkKwgf9UXJhkSZA9N13bWIUjC2bbsWUAovGo26LqBlWW4QRHYGh8NhHl66jF8vfAlNVZl17Jc5evQo97ml6AHq9BBRw0fGMikGFEQA0sEwN//wq+z3haZd/2btZnZWUNdccw0zZ86ks7OT0047jcbGRjZv3szDDz8M8Dug11KKQS0owP32LuVpNHc/IZv6UqmU66L5fD63Pk92xMo5DcVi0d0j5XI5HMdxqyI0TSObzdLU1ERFRUWPPZisLpf7H5mwBdzWC+kuBoNBN7Aha/kcx3FLmGRhbfcwuHRNLcviP6+t4LnXPmS/UXWcefwkTNNk3upSvZ3tOLywerUrqO68uHw1a/MJ8CsE0CkWbSqEn4p4iHETmnp1GQc7Oyuo448/njvvvJPLL7+cK664wv2SHDZsGMB5Qoh/93beoBCU3I/ILljbtt0PGuDuIwKBAFVVVaxfv97N91RUVGCaJvF4nLq6OndWQyaTIRaLuaVG0gJEo1HXdZMCkZUWsgZPWhxZxycrL+TPEllSBKWAQTqdJpVKEQ6H8fv9dHR04Pf73bC+DJoYhoFpmu6GWlEUBCp3PPwqRUewcl0r40fUcOC4IRzW2MjbmzajKgpThwxx92iSZC7PDY//h6LjoEQ1fvmdE1HyDu+v2cqxU8ehaypZy+SBlW/j13S+NXYShjq4Sox6oxw5qNNPP53TTz+d5cuX097eXqroHzcO4OG+zhkUgpL1cLI6wbZtt/FPulBSVDJULotdfT4fwWCQQCDgdth2dHRgmqYbLg+Hw0QiEWpqatwEcPdSHl3X3dYO2SQo90TBYNAVc3fkPkru4WSNnRRnKBSioqLCjVDKUcrV1dUEAgG3UkNV1VJFOwV0TcUqWqAoBHwll/Wr+4zh0KFNaIpC47YQ//YW1HIccpaJrqqsyXWyeOtm1rW289Lf1vC16V/gb63vsC7TQTCok7FMLpgwlaLjYAyy2r3u7Kygstkss2bNYunSpUyePJmrr766h6vfFwNeUNJSAK5LpyiK+7tsNktXV5f7bS8LSQuFAuFw2HXjZBhaVoXLtg2ZS4rFYm6EMBQKEQqFiMfjwEfDJWXNX2+hWNmeLsPmXV1daJpGPB4nn8+TSqVcyycrLqSFVVWVzs5ON/ckXVC5VlVViYSDXDzzEOa9tpIv7DOESeOHuXWDo7eJ1rIs7lnwDi8uW8O0/UfxoxOPwBYCJyYoJGw6QyazXn0R23YQDvhDCguXbMYGFAHCgQ+7Ojj5n/fSlc9x1dQZfG2f8bvhXS4/OyuoH/3oR7zxxhuceOKJzJkzh/b2dv74xz9+6nmDImwuI3QyqSotlKzu7ujoYPPmzRQKBaLRqDuoxLZtamtrXbdLWrfKykqCwSDt7e3kcjk3mCBLlDRNY+jQocTjcbe2T/5XVqTL4lXp4sneJvlPNioGg0F3L1csFqmrq8MwDKLRaI+WDmkZpWi791FJ11FG7uQXhfybTNOkK1vgzXVb+cMTC7e5iAJ/pcHadALVAtWvkA7bCGl0HAekURWAqRAJ6wyvrmBtewKEwph4FXNP+85ufKfLx4033kgqleKmm27aofMbGxtZvHgxjY2NNDc3M336dNasWSMPD+5RzHIGg0TTNLcrVu5p5AcYoKqqinw+TyKRcN1EKcZQKEQwGKSystK1XN3dPBmokENYALffSZYVybb27siiW5lT6u5+yoCJ/Bts26azs5NMJkM6ncbn81FdXU0sFkNVVZLJpDs6rKuryx0PFgqFiEaj7nNnCia2ZdGZznLx3U+SzJlkcgWioQBZu0jLpgwqYEVA63JQg+CoIBQB3b25bUOONB+sTXeQVYuoBZ32T5jsNNDZ2TsXZjIZt+192LBhPVpiPolBIai+kNagoqKCWCzWYzxxV1eXu+eROR/HcdwKCvlNr2kaLS0tWJblhqmLxSLJZNKdKS4DIN2totx/Sbo3J+ZyObdR0DRNN2ck92NSMHJt8otAilnTNMLhMIlEgmQyiWEYrlWTonxg0dvc8Z/XqIoE+c7USaTyJqqqEAwYfG3KeF5fs4F3NmxFQaDlwawW2AYgFLAUNEegJxSsiMAxQBEKmqbiCIGGQkTz0xgYuFNhP41kMikjcjtEsVjk+eef79HrJn/+0pe+dAyAEOI/2583qAUlrVKhUCAYDLqjizdv3kwymSQajVJfX+/ulSorK91oYTqd7pGklZXmDQ0N5PN5MpmMG7nbPrckN/7Sosnf+3w+t+Wiu8WTQykLhQIdHR1ubisWi7lV7Iqi0N7e7gpKdvLKfqqmpqYed+P4x6ulmrqOdI6OvMnYxhpWbWln0rAhLHprLR2FHCoKjhDoOUBTyIUEKAJFEQS3aiDA6ILsEIGByrh4LS25NKMqqyg6FpNGRFnStZaJ8ZG7/b3dWXbWQtXV1XHuuee6P1dXV3f/+U5KjvLo7c8b1IKSyVlZXiRzODI5KuczyM2/bOCTvUiBQMCNAiYSCbd1vXtRrEyiymSvbKvoLXcTCoVcl1CGr6VYu4fa5QBLWRFhmiaZTMa1eoFAgIqKChobG+no6CAYDLrlS5IDhjUw/4M1aKrCgSMbGdNYxS+fns87bVvQ0wJbcchV2mBDoB18XQqFSnB8otRiILZtBIRCnQghIoIVXe2YjknUrxKr6uKFjiwLOt/mt5PPZ0R44FdHdGdngxJr1679pMMfT/ZtY1AKSiY9ZWmRjLDJ8iEZGZO1fDKgkU6n3YEssi1Cun4yEJDNZt0aOemCyWoHuRcC3B6p7siuYHnXDPmcPp+PQCDQY4BmIBBw3Uf4qIBW/leOEhsyZEivvU3XnXIMr6xqpjEeYUxdNd++40FS+Tx5u4imQSZWpBgGxYYCENwCQi+5L7YOVtxBz6iMHVnF9PH7kDEKPPDhYopOkQ9TLQwLpwnoBRTF5N9b5nP+mNN37ZtaZnb3vXUlgzJFLnub5AddBhpkgrW6upohQ4a4+yXpUqXTadLptOv+yT2Q3GN1LwOSpUGywqK7eycFtz3yfFmZLq2SvHmaDMfLdcmSIr/fTygUorKykurqare+Dz6aiZFOp3vMxDA0jWnjRrJPfQ2FQoEh0XBpdrmuoQRUHEWgZRXUgoJtgFXlgN9GUUAVKlaDQ2FClhU1a3kwsYBTx+9PbUQl6HdoqjEZGYmjKFmgyEtti8gWc7v+jS0j/dFcCIPUQnWP+sm8lByzJf9fumbSOsmZEXISkawCl66UnGQkqxSgZAm718PJkWF9dbHKXJV022RZlBSUzHEFg0Gy2aw7jszn89HQ0OAGQ7pPiJXjoGUfVW83DzMMg5/MOIT/rFzLyo4k81evR8mbqKLk1zk+BWOEj9oKHSevsTWZxnYUjIANiiBTzHP1Ww9wxIhqFne9A2gMCQyjzSyFiU2niE/deyYe7QyDUlDSonSnN7dIRsfgIxdN5pSk+9V9kquM4snckfzwdu+56ktMjuPQ0dHhXjcej7th/WQy6SaoZUW5DPvLinfZTChnlvv9fjKZjDuvwufzuS7n9mvQNI3hTUM4u2kISzdt5ZV1G1EVFUUIBAJFc8h3FmnpsAnGDYQCRlFDOKCooCgOObGJpal1xHx5FPoVKB8AABMxSURBVAU25FrxqREcUeSQqgPRB1k5Un8JalAkdsuNtBrSPYOPEqTdi1vl72VrhXTXesNxHFpaWtyyIhmClxUZsihWBiikFZJ7M+muyv2c4ziYpulWuMtIpmEYPdbXG1sSKRZu/IAn3l3O4vb15PI+lIKCqoJmqGgVOkV/loqqDoZEuzBUB1QFBUHU2DbkE4UjqxsYHTmdCdGhPLrxahJWmnEVp3FC47fL9E7sOvx+v9sIugsY3IndctN9xrhE1gR2j97J4f/dAwkSGQHsfl5NTY0rAummya5dWXkuw+bhcJh4PO4GIqR1k0Naurq63JInudZEIuG6fTKKuH192YubVjOneR5bimvpqLAZ3ZSibUOcjuY4CoJ4PMvwpjBriklGVHTSEEySsv0UHB1DsUs5XkUQUgu05N5lWHAf5jZfg48EtZrCh4n7SVQfT4Wvehe9OzuPbPD8LLV35WavFFRfbB9okHkfGXrvLkK5z5JCgY/2djJgArhuXSgUcqcdyTFl0WiUyspKVFWlo6MDwzBcS9l98qtsNYFSy77cD5qm2eND8/yWt/nl0odBz2LaGls7I8TCKWqHdhI0THBU6ps6qAnqGDmTiD+PUARhzcQSGo5Q0BQbn2pTa6RRFZvN+XX4lU5UpTQbPUyeoD5wW9/hI3evPyY2eYL6FGQQYfs3R7pvve3dZIW4zHfJqJ1sRpSheJno9fl8VFZWIoRwW66l+yf3Yd3LoOScwO4BirSV4aHmhwiH0igIUqaP4bEEE2MbsGyNrcPjlDwVG8dJURUsdesKFHxqkYhWIKBaDPN3UKFlyQsfqgJ58zWqNQsLDQWYGDsSn7r7v/k/D/21fwJPUJ+J3r7pPs03334QjGxClLkpWfcXiUTI5/PEYjG3BlEGIWRRrLzru8xxyYhfd4uasbKAQ1A3sGyb42uOZmj0Q9Zm30c18kRFlkzRj67aFByDnOMDBIbiMMLXRtrx41eLKDioiiCkmKg4+JUMKoIKTcGvhhlTcWq5XtZdhieoQYhMynaPJH4Scm8mhSDzY/L3mqa58wBlr5W0aLLEKpfLuS5oIpEgHA67/VgREeIrtcfwZnIJB8T2Y1p8Eg9tvhddKc3oC6gOqpGnKHQCah7ddrCFSpWeokpPU6HkECgUHY207cdQFOr0TuqNBIqAsO9gmip/QkXgwNLf7xRIWuuJGcPQ1YE1adYT1CBEfsg/aZZ4X8jEbyQSwbZtOjo63MCHTEDLzmHZIyWjh/K5ZRheNiGqqsox1UdyXP2MUvAjswUVgdPteYVQUHEIqwUCSoGME6Qg/LTaMSr1kkupqzaVSgGhBBli5NGwQQHTep01bdcRHfIwqhJh3qbLSZjrifmG8cXG/4dPjQyYKbM7W8e3M3iC2kHc0ci9JFo/je53LJS3/ZS5NbnnkhZKRv5kd7IM9ctz5PPLPZu820Z1tBGj9TA68gsJqwVUIQgoxVIeRCntphxKofKs7SOu5VAQqIqDrkBcTbCuEGG0/6O9my2SFIqbKRIkYa4HoKOwgtlrziJqNHFc0y8I6zXleHl3iv60UIOy9GggIMc370yeQ0YDfT6fe6vPqqoqt2pDDu+Uo5W7u4ky2dyd7onnD7OrWJ7+kK1mBR/matG0kZw09H9T7a/fFooAQykiUDAUB8tR2WxFKNgGE/2tjPN1MDHQTrNZhYOGg5948DjyjsqK5DMEtFI3sy0cEBppaytrUwt2+LUoJ/1VxweehdopdsbFkZOaFEWhoqICwzDcELgc3CK7eWUhryxfkrktmefa3ko6wmFj9gMgBWjoioqijOTF1lVsydcSVLZSoeUIqia2ozDU18kQPYlPsSk4PoKKg4rAp9iss2pZXxyOjWAUIczUL7BFDiHgqPorWZmcx+bcEhRFpdr/2ef77Uq8PdReSPc5GbLtQ1ZIAD0SzN3n+3W/11X3LuDuvNbxLIva/0VEdwgKG0cEWZHZDGITlkgzMWxjKNuieZpFXMvjV0oi9asF2uxK4mqaFBGG+vKss0p3R9yQW4aPDJFt5U9+vYKjG6+mOfMaUaOBmsDY3fDKfTqeoPZCZKmRdN/krEB55w7ZeSyHzsgBl/DRHmz7nqyt+bW83fkc6zJvElDaUbQ4hmog7HbiWgs5J0qrZeAIBZmFsoF2O0hczW3bV2mscg5guNpMQOliqL6RNkshRRgQ2ARpL2YYGT6A+sABKIrCqGifM8D7hWQy2eOu7bsTT1C7ATnUUiZlZeBAunjdb3Yghdb9Zgd+vx/TNN1qeinE7gjh8M/mX5G0WrGcHFHNIqjrFIWNUBPYQiWotTE6YKGIAioQVB18FLBReT07nBo9Q6tdy3f3uYv3Nk8nZ+dBEQRVi5RdakzUFBVdjdFptg+YqN72eBZqD0dWRvS155FVEX21hnyWXJdAYDl5kAEHNcjI0L60F9aTMDtRFIGKQ43ehaIIFBQ0BEI4GDjE9Swb7KFMrjwZVfVTGz2LDYk/k7F1uuwwKhDW4lgijcChPrhv+V6gMuMJag9HFrPK6Ul9sTPf+KqiccKQC3i9bS4Fp40GfxWH1Z4HKCzcfAlJq5OA6pSEpRgYShgVgem0gwCBSlQrsC79NJX+oexbcSmV4e+yKvkClc7TdFqt5J0OJlQcS1NoEiMiB+/wWnc1nqD2cOT4sl3NvtFD2Dd6yMd+f/KIv5MyVxMymlif/Act2XlUBw+jMXQsr2z+HpZIkxZhDLVkBS0nDUBAr+YLVafhKAavtd8PwJrMK0yr7/3OfgOFLVu2uI2kuxtPUHs4lp2kPf8WMf++GGqEMfFzGBP/6BYtx458hc7CetLWRlYk/0lQq2Js7Os9rlEX2BdlW91FQ2DgTpK1bZtZs2axZs0ad7LU7mavbDDcW3CExUubf0DG2oChhjmi8XaCer17vGC30Zy4D0OrZFjs26hK3/u0jsI6ktZWhoUPQvuEx/UXW7Zs4ayzzkIIwf3337+ro3x9+uZepcQejGknyVgbALCcDBszz7O47fc0p18AYHn7/2Vj+p+sTdzJxtQ/PvFaVf4RjIwcOiDF9NxzzzF58mSmTZvGs88+228hc/Bcvj2agF5NY/iLbM48T8w3hmWds3GEybrUv4kYTRSdjPvY4rZ902CiWCzy85//nLvuuov77ruPY445pr+X5Ll8ewOWk8FxbJ5s/jbyLT2q4UYCqp9VXX/Ap1ayb9VVGFr/RMZ2hE2bNnHmmWfi8/m47777qK+v//STykefLp8nqL2IdalnWZN6itrARCZUnd3fy9lhnnnmGc4++2wuuugirrnmmk9MRewiPEF5DH6KxSLXXXcd9957L/fffz8zZszor6V4U488BjcbNmzgzDPPJBQKsXjxYurqBuasdS/K5zHgefLJJ5kyZQpf+cpXeOqppwasmMCzUB4DGMuyuPbaa3nooYeYM2cORx11VH8v6VPxBOUxIFm/fj3f+ta3qKysZPHixdTU9H9r/WfBc/k8Bhxz587lkEMO4Rvf+AZz584dNGICz0J5DCBM0+Tqq69mzpw5PProoxx++OH9vaTPjScojwHB2rVrOeOMM6ivr+ett96iqqqqv5e0Q3gun0e/8+ijj3LooYdyxhln8Nhjjw1aMcEAsFDZbJaLL76YadOmMW3aNEaPHj1gW6s9ykuhUOCnP/0pjz/+OHPnzmXq1Kn9vaSdpt8tlOM4HHzwwTz99NNMnz6dpqYmzjjjDP70pz+xZMkSdwqQx57F6tWrOfLII1m/fj2LFy/eI8QEA6z0SAjBmjVrWLBgAQsWLGD+/Pm0trZy5JFHuhZsypQpfd70zGNwMGfOHC666CJ+9rOfcfHFFw9Gj2Tw1vJt2bKFhQsXuiJbsWIFhxxyiCuwww8/vN/anT0+H/l8nssvv5ynnnqK2bNnc8ghH2/XHyQMXkFtTyKR4OWXX3YF9tZbb7H//vu7AjvqqKMGVd5ib2HVqlXMnDmT0aNH89e//pV4PN7fS9oZ9hxBbU8+n+e1115zBbZo0SKGDh3qCmzatGkMHz68v5e5VzN79mwuvvhirr/+ei666KLB6OJtz54rqO0pFou88847rsAWLFhAMBhk+vTprsDGjx+/J7ypA55cLsell17KvHnzmD17NpMnT+7vJZWLvUdQ2yOEYMWKFcyfP98VWDqd7mHBDjzwwB26LY1H3yxfvpyZM2ey3377cccdd/TbnLxdxN4rqN5obm7uYcHWr1/PYYcd5lqxQw89lGAw2N/LHLTcf//9XHLJJcyaNYsf/OAHe6I34Anqk2hvb+ell15yQ/VLly7lwAMPZNq0aUyfPp0jjjhisG+idwvZbJaf/OQnvPjii/z973/nwAMP7O8l7So8QX0eMpkMr7zyiiuw119/nTFjxvRwE/tzVNVA5P3332fmzJlMnDiRv/zlL+7d7PdQPEHtDKZpsnjxYtdFXLhwIdXV1T0ENmbMmD3RtflM3HPPPVx++eXcdNNNfP/7398bXgdPUOXEcRyWLVvWYx/mOE4PgR1wwAEfu+XMnkYmk+HHP/4xr7zyCn//+9854IAD+ntJuwtPULuS7UumFixYQEtLyx5dMrVs2TJmzpzJlClTuPXWW/e2ahVPULubPbVkSgjB3XffzU9/+lNuueUWzj777P5eUn/Qt08rbwTWxz+PMtHV1SWefPJJcfXVV4ujjjpKhMNhMWXKFHHZZZeJRx55RLS2tvb3Ej+VVColvvOd74j9999fLFu2rGzXHTFihAgEAiIcDou6ujrxve99T6RSKff4008/LaZNmyYikYioqakR06dPF4899liv17r77rvF5MmTRTQaFU1NTeLKK68UlmWVba3b6FMznqD6iVwuJ+bPny9mzZoljj/+eBGLxcR+++0nLrjgAnHfffeJdevW9fcSe7BkyRIxfvx4ce6554pMJlPWa48YMUI8++yzQgghNmzYICZMmCCuuuoqIYQQ//jHP0Q0GhX/9V//Jbq6uoRt2+KFF14Q5513Xq/Xuu2228T8+fNFoVAQGzZsEJMnTxY33XRTWdcrPEENfCzLEm+++ab47W9/K0499VRRW1srhg8fLs466yxx++23i/fee084jrPb1+U4jrjjjjtETU2NuOeee3bJc3QXlBBCXHHFFeKkk04SjuOIYcOGiZtvvnmHr/3rX/9anHzyyeVYZnf61IxXbzNA0HWdyZMnM3nyZC655BK3ZEruwW666SbS6TRHHXWUm3De1SVTqVSKCy64gHfffZcFCxYwfvyuv9lac3MzTz75JKeeeirLly+nubmZ008/fYevN3/+fCZMmFDGFX4Kn6S2csvaY+dobm4WDzzwgLjwwgvFhAkTRDQaFccee6y44YYbxPPPPy+y2WzZnuutt94SY8eOFeeff35Zr9sbI0aMEOFwWFRUVIjhw4eLCy+8UGSzWbFw4UIBiFwut0PXvfPOO0VTU9Ou2J96Lt+eSFtbm3jsscfEFVdcIaZOnSpCoZA44ogjxFVXXSWeeOIJ0dnZ+bmv6TiOuO2220RNTY24//77d8GqP872Lp/k/fffF4BYvXr1577mI488Iurq6sSSJUvKscTt8QS1N5BOp8Vzzz0nrr/+enHMMceISCQiJk2aJH784x+L2bNni02bNn3i+V1dXWLmzJli0qRJYvny5btp1X0LSu6hbrnlls91vaeeekrU1NSIV199tVxL3B5PUHsjhUJBLFq0SNx8883iq1/9qqisrBT77LOPOOecc8Rdd90lVq5c6QY63njjDTFmzBjxwx/+cIddrB2lL0EJUYryxWIxcdddd4lEIiFs2xYLFiwQ559/fq+PnzdvnqiqqhIvvvjirlyyJygPIWzbFkuWLBG33nqr+Na3viWamppEY2OjOPjgg0U8HhezZ8/ul3V9kqCEKFkcmburqakRM2bMEE888USvjz366KOFpmkiHA67/0444YRyL7lPzXiVEnsxQgjWrl3LH//4R0444QSOO+64/l7SYMErPfLwKCN9CmrPLof28NjN7DWCGjlyJMFgkEgkQn19PWeffTbpdNo9/swzzzB9+nSi0Si1tbXMmDGDxx9/vNdrLV26lOOPP56ampq9offH43Ow1wgKSvcdSqfTLF68mDfeeINZs2YBpUmm3/zmN/nud7/Lhg0b2Lp1KzfccANz587t9TqGYTBz5kzuvPPO3bl8j0HAXll61NTUxIknnsjSpUsRQnDZZZdx3XXXcd5557mPmTFjRp93GR83bhzjxo1j1apVu2vJHoOEvcpCSWS92EEHHVSWejEPD8leZaFOOeUUdF2noqKCk046iWuuuYbFixcDeENXPMrCXiWoRx99lC9/+cs9flddXQ3A5s2bGTVqVH8sy2MP4tPyUHsMiqKsBc4TQjy33e8VYB3wByHErz7nNfcBVgohvFCfB7CX7qG6I0rfKJcB1ymKco6iKDFFUVRFUY5SFOWO3s5RSgQA37afA4qi+Hfjsj0GKHuVy9cXQog5iqKkgWuBPwI5YBlwSx+njADWdPs5R8nKjdyFy/QYBOw1Lp+Hx+5gr3f5PDzKiScoD48y4gnKw6OM/H8mUkdOK7r8XQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 255.118x191.339 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm, 6.75*cm))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "non_p90 = [x for x in psix_object.latent.index if x not in psix_p90.latent.index]\n",
    "\n",
    "ax.scatter(psix_object.latent.loc[non_p90].PC_1, psix_object.latent.loc[non_p90].PC_2, psix_object.latent.loc[non_p90].PC_3,\n",
    "           c = 'gray', s=8, linewidth=0, alpha=0.1, vmin=-1, vmax=1)\n",
    "\n",
    "ax.scatter(psix_p90.latent.PC_1, psix_p90.latent.PC_2, psix_p90.latent.PC_3,\n",
    "           c = zscore(psix_p90.adata.uns['neighbors_psi'].T.loc[psix_p90.modules.loc[psix_p90.modules == 1].index],\n",
    "       axis=1, nan_policy='omit').mean(axis=0), s=12, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xlabel('PC 1', fontsize=12,labelpad=-5)\n",
    "ax.set_ylabel('PC 2', fontsize=12,labelpad=-5)\n",
    "ax.set_zlabel('PC 3', fontsize=12,labelpad=-8, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/P90_moduleA.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x191.339 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm, 6.75*cm))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "non_p90 = [x for x in psix_object.latent.index if x not in psix_p90.latent.index]\n",
    "\n",
    "ax.scatter(psix_object.latent.loc[non_p90].PC_1, psix_object.latent.loc[non_p90].PC_2, psix_object.latent.loc[non_p90].PC_3,\n",
    "           c = 'gray', s=8, linewidth=0, alpha=0.1, vmin=-1, vmax=1)\n",
    "\n",
    "ax.scatter(psix_p90.latent.PC_1, psix_p90.latent.PC_2, psix_p90.latent.PC_3,\n",
    "           c = zscore(psix_p90.adata.uns['neighbors_psi'].T.loc[psix_p90.modules.loc[psix_p90.modules == 2].index],\n",
    "       axis=1, nan_policy='omit').mean(axis=0), s=12, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xlabel('PC 1', fontsize=12,labelpad=-5)\n",
    "ax.set_ylabel('PC 2', fontsize=12,labelpad=-5)\n",
    "ax.set_zlabel('PC 3', fontsize=12,labelpad=-8, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/P90_moduleB.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x191.339 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm, 6.75*cm))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "non_p1 = [x for x in psix_object.latent.index if x not in psix_p1.latent.index]\n",
    "\n",
    "\n",
    "ax.scatter(psix_object.latent.loc[non_p1].PC_1, psix_object.latent.loc[non_p1].PC_2, psix_object.latent.loc[non_p1].PC_3,\n",
    "           c = 'gray', s=8, linewidth=0, alpha=0.1, vmin=-1, vmax=1)\n",
    "\n",
    "\n",
    "idx_1 = psix_p90.modules.loc[psix_p90.modules == 1].index & psix_p1.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "\n",
    "ax.scatter(psix_p1.latent.PC_1, psix_p1.latent.PC_2, psix_p1.latent.PC_3,\n",
    "           c = np.nanmean(zscore(psix_p1.adata.uns['neighbors_psi'].T.loc[idx_1],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=12, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xlabel('PC 1', fontsize=12,labelpad=-5)\n",
    "ax.set_ylabel('PC 2', fontsize=12,labelpad=-5)\n",
    "ax.set_zlabel('PC 3', fontsize=12,labelpad=-8, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/P1_moduleA.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x191.339 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm, 6.75*cm))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "non_p1 = [x for x in psix_object.latent.index if x not in psix_p1.latent.index]\n",
    "\n",
    "\n",
    "ax.scatter(psix_object.latent.loc[non_p1].PC_1, psix_object.latent.loc[non_p1].PC_2, psix_object.latent.loc[non_p1].PC_3,\n",
    "           c = 'gray', s=8, linewidth=0, alpha=0.1, vmin=-1, vmax=1)\n",
    "\n",
    "\n",
    "idx_1 = psix_p90.modules.loc[psix_p90.modules == 2].index & psix_p1.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "\n",
    "ax.scatter(psix_p1.latent.PC_1, psix_p1.latent.PC_2, psix_p1.latent.PC_3,\n",
    "           c = np.nanmean(zscore(psix_p1.adata.uns['neighbors_psi'].T.loc[idx_1],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=12, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xlabel('PC 1', fontsize=12,labelpad=-5)\n",
    "ax.set_ylabel('PC 2', fontsize=12,labelpad=-5)\n",
    "ax.set_zlabel('PC 3', fontsize=12,labelpad=-8, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/P1_moduleB.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x191.339 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm, 6.75*cm))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "non_e13 = [x for x in psix_object.latent.index if x not in psix_e13.latent.index]\n",
    "\n",
    "\n",
    "ax.scatter(psix_object.latent.loc[non_e13].PC_1, psix_object.latent.loc[non_e13].PC_2, psix_object.latent.loc[non_e13].PC_3,\n",
    "           c = 'gray', s=8, linewidth=0, alpha=0.1, vmin=-1, vmax=1)\n",
    "\n",
    "\n",
    "idx_1 = psix_p90.modules.loc[psix_p90.modules == 1].index & psix_e13.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "ax.scatter(psix_e13.latent.PC_1, psix_e13.latent.PC_2, psix_e13.latent.PC_3,\n",
    "           c = np.nanmean(zscore(psix_e13.adata.uns['neighbors_psi'].T.loc[idx_1],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=12, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xlabel('PC 1', fontsize=12,labelpad=-5)\n",
    "ax.set_ylabel('PC 2', fontsize=12,labelpad=-5)\n",
    "ax.set_zlabel('PC 3', fontsize=12,labelpad=-8, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/E13_moduleA.pdf', bbox_inches='tight', res=20000, dpi =2000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x191.339 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm, 6.75*cm))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "non_e13 = [x for x in psix_object.latent.index if x not in psix_e13.latent.index]\n",
    "\n",
    "\n",
    "ax.scatter(psix_object.latent.loc[non_e13].PC_1, psix_object.latent.loc[non_e13].PC_2, psix_object.latent.loc[non_e13].PC_3,\n",
    "           c = 'gray', s=8, linewidth=0, alpha=0.1, vmin=-1, vmax=1)\n",
    "\n",
    "\n",
    "idx_1 = psix_p90.modules.loc[psix_p90.modules == 2].index & psix_e13.adata.uns['neighbors_psi'].columns\n",
    "\n",
    "\n",
    "ax.scatter(psix_e13.latent.PC_1, psix_e13.latent.PC_2, psix_e13.latent.PC_3,\n",
    "           c = np.nanmean(zscore(psix_e13.adata.uns['neighbors_psi'].T.loc[idx_1],\n",
    "       axis=1, nan_policy='omit'), axis = 0), s=12, linewidth=0, alpha=0.9, vmin=-1, vmax=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xlabel('PC 1', fontsize=12,labelpad=-5)\n",
    "ax.set_ylabel('PC 2', fontsize=12,labelpad=-5)\n",
    "ax.set_zlabel('PC 3', fontsize=12,labelpad=-8, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/E13_moduleB.pdf', bbox_inches='tight', res=20000, dpi =2000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene markers in P90 cells\n",
    "\n",
    "Expression of gene markers used in Tiklova et al., 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x240 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "latent = psix_object.latent.loc[p90_cells]\n",
    "\n",
    "fig = plt.figure(figsize=(15/2.4, 8/2.4))\n",
    "gs = GridSpec(2,3)\n",
    "gs.update(wspace=0.7, hspace=0.7)\n",
    "\n",
    "    \n",
    "ax_1 = fig.add_subplot(gs[0, 0])\n",
    "ax_2 = fig.add_subplot(gs[0, 1])\n",
    "ax_3 = fig.add_subplot(gs[0, 2])\n",
    "ax_4 = fig.add_subplot(gs[1, 0])\n",
    "ax_5 = fig.add_subplot(gs[1, 1])\n",
    "ax_6 = fig.add_subplot(gs[1, 2])\n",
    "\n",
    "def plot_ax(ax, gene):\n",
    "    ax.scatter(latent.PC_3, latent.PC_1,\n",
    "           c = norm_expression.loc[gene, p90_cells], s=10, edgecolor=None, alpha=0.9, cmap='inferno')\n",
    "    ax.set_ylabel('PC 1', fontsize=12)\n",
    "    ax.set_xlabel('PC 3', fontsize=12)\n",
    "\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.set_title(gene, fontsize=12)\n",
    "    \n",
    "plot_ax(ax_1, 'Aldh1a1')\n",
    "plot_ax(ax_2, 'Gad2')\n",
    "plot_ax(ax_3, 'Nxph4')\n",
    "plot_ax(ax_4, 'Slc6a3')\n",
    "plot_ax(ax_5, 'Th')\n",
    "plot_ax(ax_6, 'Vip')\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/p90_lineage_expression.pdf', bbox_inches='tight', res=20000, dpi =2000)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrichment of splicing factor binding in cell-state associated exons in P90 cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significantly bound splicing factors:\n",
      "\n",
      "CELF4\n",
      "FUS\n",
      "MBNL2\n",
      "NOVA1\n",
      "NOVA2\n",
      "RBFOX1\n",
      "RBFOX2\n"
     ]
    }
   ],
   "source": [
    "psix_results = psix_p90.psix_results\n",
    "\n",
    "sig_exons_p90 = psix_results.loc[(psix_results.qvals <= 0.05) & (psix_results.psix_score > 0)].index\n",
    "\n",
    "pval_hyper = []\n",
    "enrichment_hyper = []\n",
    "\n",
    "for RBP in splicing_factors:\n",
    "        \n",
    "    region_exons = mouse_clip_peaks_any.index\n",
    "\n",
    "    hyper = test_enrichment(mouse_clip_peaks_any.loc[psix_results.index, RBP], sig_exons_p90)\n",
    "    pval_hyper.append(hyper[0])\n",
    "    enrichment_hyper.append(hyper[2])\n",
    "    \n",
    "print('Significantly bound splicing factors:\\n')\n",
    "for sf in sorted(np.array(splicing_factors)[multipletests(pval_hyper, method='fdr_bh')[0]]):\n",
    "    print(sf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation of bound splicing factors, with P90 modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nan_pearsonr(x, y):\n",
    "    non_nan = y.dropna().index\n",
    "    if len(non_nan) < 15:\n",
    "        return (np.nan, 1)\n",
    "    return pearsonr(x.loc[non_nan], y.loc[non_nan])\n",
    "\n",
    "p90_modules = psix_p90.modules\n",
    "\n",
    "from scipy.stats import ranksums\n",
    "def get_corr(rbp_name):\n",
    "    mod1_p90_cor = []\n",
    "    mod1_p1_cor = []\n",
    "    mod1_e13_cor = []\n",
    "    for exon in p90_modules.loc[p90_modules == 1].index:\n",
    "        mod1_p90_cor.append(\n",
    "            nan_pearsonr(norm_expression.loc[rbp_name, p90_cells], psix_p90.adata.uns['psi'].loc[p90_cells, exon])[0]\n",
    "        )\n",
    "\n",
    "        if exon in psix_p1.adata.uns['psi'].columns:\n",
    "            mod1_p1_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, p1_cells], psix_p1.adata.uns['psi'].loc[p1_cells, exon])[0]\n",
    "            )\n",
    "\n",
    "        if exon in psix_e13.adata.uns['psi'].columns:\n",
    "            mod1_e13_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, e13_cells], psix_e13.adata.uns['psi'].loc[e13_cells, exon])[0]\n",
    "            )\n",
    "\n",
    "    mod2_p90_cor = []\n",
    "    mod2_p1_cor = []\n",
    "    mod2_e13_cor = []\n",
    "    for exon in p90_modules.loc[p90_modules == 2].index:\n",
    "        mod2_p90_cor.append(\n",
    "            nan_pearsonr(norm_expression.loc[rbp_name, p90_cells], psix_p90.adata.uns['psi'].loc[p90_cells, exon])[0]\n",
    "        )\n",
    "\n",
    "        if exon in psix_p1.adata.uns['psi'].columns:\n",
    "            mod2_p1_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, p1_cells], psix_p1.adata.uns['psi'].loc[p1_cells, exon])[0]\n",
    "            )\n",
    "\n",
    "        if exon in psix_e13.adata.uns['psi'].columns:\n",
    "            mod2_e13_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, e13_cells], psix_e13.adata.uns['psi'].loc[e13_cells, exon])[0]\n",
    "            )    \n",
    "        \n",
    "    \n",
    "    modules_corr = pd.DataFrame()\n",
    "    modules_corr['E13.5'] = [np.nanmean(mod1_e13_cor), np.nanmean(mod2_e13_cor)]\n",
    "    modules_corr['P1'] = [np.nanmean(mod1_p1_cor), np.nanmean(mod2_p1_cor)]\n",
    "    modules_corr['P90'] = [np.nanmean(mod1_p90_cor), np.nanmean(mod2_p90_cor)]\n",
    "    \n",
    "    modules_corr.index = ['Mod1', 'Mod2']\n",
    "    \n",
    "    wx_rank = []\n",
    "    \n",
    "    wx_rank.append(ranksums(mod1_e13_cor, mod2_e13_cor)[1])\n",
    "    wx_rank.append(ranksums(mod1_p1_cor, mod2_p1_cor)[1])\n",
    "    wx_rank.append(ranksums(mod1_p90_cor, mod2_p90_cor)[1])\n",
    "    \n",
    "    #list(p90_modules.loc[p90_modules == 1].index) + list(p90_modules.loc[p90_modules == 2].index)\n",
    "    \n",
    "    return modules_corr, wx_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "wilcoxon_df = pd.DataFrame()\n",
    "diff_df = pd.DataFrame()\n",
    "for rbp in ['Celf4', 'Fus', 'Mbnl2', 'Nova1', 'Nova2', 'Rbfox1', 'Rbfox2']:\n",
    "    rbp_module_corr, wilcoxon = get_corr(rbp)\n",
    "    \n",
    "    rbp_module_diff = rbp_module_corr.loc['Mod1'] - rbp_module_corr.loc['Mod2']\n",
    "    \n",
    "    diff_df[rbp] = rbp_module_diff\n",
    "    \n",
    "    wilcoxon_df[rbp] = wilcoxon\n",
    "    \n",
    "wilcoxon_df.index = ['E13.5', 'P1', 'P90']\n",
    "\n",
    "wilcoxon_df = wilcoxon_df.T\n",
    "diff_df = diff_df.T.abs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Significant differences in splicing factor-module correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E13.5</th>\n",
       "      <th>P1</th>\n",
       "      <th>P90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mbnl2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nova1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rbfox1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        E13.5     P1   P90\n",
       "Mbnl2   False  False  True\n",
       "Nova1   False   True  True\n",
       "Rbfox1  False  False  True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilcoxon_corrected = pd.DataFrame(multipletests(np.array(wilcoxon_df).reshape(21, ), method='fdr_bh')[0].reshape(7, 3),\n",
    "             index = wilcoxon_df.index, columns = wilcoxon_df.columns)\n",
    "\n",
    "significant_df = wilcoxon_corrected & (diff_df >= 0.1)\n",
    "\n",
    "significant_df.loc[significant_df.sum(axis=1)>=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rbp_ax_uniform(rbp_name, ax):\n",
    "    mod1_p90_cor = []\n",
    "    mod1_p1_cor = []\n",
    "    mod1_e13_cor = []\n",
    "    \n",
    "    for exon in p90_modules.loc[p90_modules == 1].index:\n",
    "        mod1_p90_cor.append(\n",
    "            nan_pearsonr(norm_expression.loc[rbp_name, p90_cells], psix_p90.adata.uns['psi'].loc[p90_cells, exon])[0]\n",
    "        )\n",
    "\n",
    "        if exon in psix_p1.adata.uns['psi'].columns:\n",
    "            mod1_p1_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, p1_cells], psix_p1.adata.uns['psi'].loc[p1_cells, exon])[0]\n",
    "            )\n",
    "\n",
    "        if exon in psix_e13.adata.uns['psi'].columns:\n",
    "            mod1_e13_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, e13_cells], psix_e13.adata.uns['psi'].loc[e13_cells, exon])[0]\n",
    "            )\n",
    "\n",
    "    mod2_p90_cor = []\n",
    "    mod2_p1_cor = []\n",
    "    mod2_e13_cor = []\n",
    "    \n",
    "    nova_colors = []\n",
    "    \n",
    "    for exon in p90_modules.loc[p90_modules == 2].index:\n",
    "        \n",
    "        if exon == 'Nova1_1':\n",
    "            nova_colors.append('red')\n",
    "        else:\n",
    "            nova_colors.append(None)\n",
    "        \n",
    "        \n",
    "        mod2_p90_cor.append(\n",
    "            nan_pearsonr(norm_expression.loc[rbp_name, p90_cells], psix_p90.adata.uns['psi'].loc[p90_cells, exon])[0]\n",
    "        )\n",
    "\n",
    "        if exon in psix_p1.adata.uns['psi'].columns:\n",
    "            mod2_p1_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, p1_cells], psix_p1.adata.uns['psi'].loc[p1_cells, exon])[0]\n",
    "            )\n",
    "\n",
    "        if exon in psix_e13.adata.uns['psi'].columns:\n",
    "            mod2_e13_cor.append(\n",
    "                nan_pearsonr(norm_expression.loc[rbp_name, e13_cells], psix_e13.adata.uns['psi'].loc[e13_cells, exon])[0]\n",
    "            )    \n",
    "        \n",
    "    abs_max = np.max((np.nanmax(np.abs(mod1_e13_cor)), 0))\n",
    "    abs_max = np.max((np.nanmax(np.abs(mod2_e13_cor)), abs_max))\n",
    "    abs_max = np.max((np.nanmax(np.abs(mod1_p1_cor)), abs_max))\n",
    "    abs_max = np.max((np.nanmax(np.abs(mod2_p1_cor)), abs_max))\n",
    "    abs_max = np.max((np.nanmax(np.abs(mod1_p90_cor)), abs_max))\n",
    "    abs_max = np.max((np.nanmax(np.abs(mod2_p90_cor)), abs_max))\n",
    "        \n",
    "        \n",
    "    colors = list(plt.get_cmap('tab10').colors)\n",
    "    ax.grid(False)\n",
    "\n",
    "    ax.plot([-0.5, 2.5], [0, 0], 'r--')\n",
    "    \n",
    "    ax.set_ylim((-0.75, 0.75))\n",
    "    \n",
    "    ax.scatter(np.linspace(0,0.25, len(mod1_e13_cor)), mod1_e13_cor, \n",
    "               c=[colors[0]]*len(mod1_e13_cor), alpha=0.75, linewidth=0, s=12)\n",
    "    ax.scatter(np.linspace(-0.25, 0, len(mod2_e13_cor)), mod2_e13_cor, \n",
    "               c=[colors[1]]*len(mod2_e13_cor), alpha=0.75, linewidth=0, s=12)\n",
    "\n",
    "    ax.scatter(np.linspace(1, 1.25, len(mod1_p1_cor)), mod1_p1_cor, \n",
    "               c=[colors[0]]*len(mod1_p1_cor), alpha=0.75, linewidth=0, s=12)\n",
    "    ax.scatter(np.linspace(0.75, 1, len(mod2_p1_cor)), mod2_p1_cor, \n",
    "               c=[colors[1]]*len(mod2_p1_cor), alpha=0.75, linewidth=0, s=12)\n",
    "\n",
    "    ax.scatter(np.linspace(2, 2.25, len(mod1_p90_cor)), mod1_p90_cor, \n",
    "               c=[colors[0]]*len(mod1_p90_cor), alpha=0.75, linewidth=0, s=12)\n",
    "    ax.scatter(np.linspace(1.75, 2, len(mod2_p90_cor)), mod2_p90_cor, \n",
    "               c=[colors[1]]*len(mod2_p90_cor), alpha=0.75, linewidth=0, s=12)\n",
    "    \n",
    "    ax.set_xticks([0, 1, 2])\n",
    "    ax.set_xticklabels(['E13.5', 'P1', 'P90'], size=8, rotation=45)\n",
    "\n",
    "    ax.set_ylabel('Pearson r', size=8)\n",
    "\n",
    "    ax.tick_params(labelsize=8, length=5)\n",
    "    ax.set_title(rbp_name, fontsize=8)\n",
    "\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    \n",
    "    ax.set_ylim((-abs_max - 0.05, abs_max + 0.05))    \n",
    "    \n",
    "    modules_corr = pd.DataFrame()\n",
    "    modules_corr['E13.5'] = [np.nanmean(mod1_e13_cor), np.nanmean(mod2_e13_cor)]\n",
    "    modules_corr['P1'] = [np.nanmean(mod1_p1_cor), np.nanmean(mod2_p1_cor)]\n",
    "    modules_corr['P90'] = [np.nanmean(mod1_p90_cor), np.nanmean(mod2_p90_cor)]\n",
    "    \n",
    "    modules_corr.index = ['Mod1', 'Mod2']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x195 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "\n",
    "rbp_list = ['Rbfox1', 'Nova1', 'Mbnl1']\n",
    "\n",
    "fig = plt.figure(figsize=(8/2.4, 6.5/2.4))\n",
    "gs = GridSpec(2,2)\n",
    "gs.update(wspace=1.3, hspace=1)\n",
    "\n",
    "i = 0\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "ax3 = fig.add_subplot(gs[1, 0])\n",
    "# ax4 = fig.add_subplot(gs[1, 1])\n",
    "plot_rbp_ax_uniform('Rbfox1', ax1)\n",
    "plot_rbp_ax_uniform('Nova1', ax2)\n",
    "plot_rbp_ax_uniform('Mbnl2', ax3)\n",
    "# plot_rbp_ax_uniform('Mbnl2', ax4)\n",
    "\n",
    "ax2.scatter([-0.25, 0.75, 1.75],\n",
    "           [nan_pearsonr(norm_expression.loc['Nova1', e13_cells], psix_object.adata.uns['psi'].loc[e13_cells, 'Nova1_1'])[0],\n",
    "           nan_pearsonr(norm_expression.loc['Nova1', p1_cells], psix_object.adata.uns['psi'].loc[p1_cells, 'Nova1_1'])[0],\n",
    "           nan_pearsonr(norm_expression.loc['Nova1', p90_cells], psix_object.adata.uns['psi'].loc[p90_cells, 'Nova1_1'])[0]],\n",
    "           color=\"none\", edgecolor=\"red\")\n",
    "#            c = 'darkred', linewidth=1, color=None)\n",
    "\n",
    "# plt.show()  \n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/rbp_p90_module_correlations.pdf', bbox_inches='tight', res=20000, dpi =2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nova1 expression and splicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 191.339x143.504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.gridspec import GridSpec\n",
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(9*cm*0.75, 6.75*cm*0.75))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "# gs = GridSpec(1,5)\n",
    "# gs.update(wspace=0.05, hspace=0.10)\n",
    "# ax = fig.add_subplot(gs[0,0:4], projection='3d')\n",
    "# ax_2 = fig.add_subplot(gs[0,4])\n",
    "ax.grid(False)\n",
    "ax.scatter(psix_object.latent.PC_1, psix_object.latent.PC_2, psix_object.latent.PC_3, c=psi.loc['Nova1_1'], s=10, linewidth=0, alpha=1)\n",
    "# ax.scatter(psix_object.latent.PC_1, psix_object.latent.PC_3, c=colores)\n",
    "# ax.set_yticks([-10, -5, 0, 5, 10])\n",
    "# ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "\n",
    "\n",
    "# ax.set_title('Mouse neurogenesis', fontsize=12)\n",
    "ax.set_xlabel('PC 1', fontsize=8,labelpad=-13)\n",
    "ax.set_ylabel('PC 2', fontsize=8,labelpad=-13)\n",
    "ax.set_zlabel('PC 3', fontsize=8,labelpad=-15, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.set_title('Nova1, exon 4', fontsize=12)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "# Now set color to white (or whatever is \"invisible\")\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "#     plt.show()\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/PCA_Nova1_1.pdf', bbox_inches='tight', res=20000, dpi =2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_PC3_gene(gene):\n",
    "\n",
    "    from matplotlib.gridspec import GridSpec\n",
    "    plt.tight_layout()\n",
    "    fig = plt.figure(figsize=(9*cm*0.75, 6.75*cm*0.75))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    # gs = GridSpec(1,5)\n",
    "    # gs.update(wspace=0.05, hspace=0.10)\n",
    "    # ax = fig.add_subplot(gs[0,0:4], projection='3d')\n",
    "    # ax_2 = fig.add_subplot(gs[0,4])\n",
    "    ax.grid(False)\n",
    "    ax.scatter(psix_object.latent.PC_1, psix_object.latent.PC_2, psix_object.latent.PC_3, c=norm_expression.loc[gene], s=10, linewidth=0, alpha=1, cmap='inferno',)\n",
    "    # ax.scatter(psix_object.latent.PC_1, psix_object.latent.PC_3, c=colores)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_zticks([])\n",
    "\n",
    "\n",
    "    # ax.set_title('Mouse neurogenesis', fontsize=12)\n",
    "    ax.set_xlabel('PC 1', fontsize=8,labelpad=-13)\n",
    "    ax.set_ylabel('PC 2', fontsize=8,labelpad=-13)\n",
    "    ax.set_zlabel('PC 3', fontsize=8,labelpad=-15, rotation=90)\n",
    "\n",
    "\n",
    "    ax.set_title(gene, fontsize=8)\n",
    "#     ax.set_xlabel('PC 1', fontsize=8,labelpad=-5)\n",
    "#     ax.set_ylabel('PC 2', fontsize=8,labelpad=-5)\n",
    "#     ax.set_zlabel('PC 3', fontsize=8,labelpad=-8, rotation=90)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.set_title(gene + ', gene', fontsize=12)\n",
    "\n",
    "    ax.xaxis.pane.fill = False\n",
    "    ax.yaxis.pane.fill = False\n",
    "    ax.zaxis.pane.fill = False\n",
    "    \n",
    "#     ax.set_xticks([])\n",
    "#     ax.set_yticks([])\n",
    "#     ax.set_zticks([])\n",
    "\n",
    "    # Now set color to white (or whatever is \"invisible\")\n",
    "    ax.xaxis.pane.set_edgecolor('w')\n",
    "    ax.yaxis.pane.set_edgecolor('w')\n",
    "    ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "    plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/PCA_' + gene + '.pdf', bbox_inches='tight', res=20000, dpi =2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 191.339x143.504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_PC3_gene('Nova1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 191.339x143.504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_PC3_gene('Rbfox1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 191.339x143.504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_PC3_gene('Mbnl2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 191.339x143.504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_PC3_gene('Aldh1a1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of assumed capture efficiency in Psix score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 7551.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:19<00:00, 106.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [08:40<00:00, 20.80s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n",
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 8171.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:22<00:00, 91.53it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [08:44<00:00, 20.97s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n",
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 7963.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:21<00:00, 99.11it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [08:44<00:00, 20.99s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n",
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 8175.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:22<00:00, 93.43it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [08:47<00:00, 21.12s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n",
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 8099.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:22<00:00, 90.83it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [09:07<00:00, 21.89s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully estimated p-values\n",
      "Computing cell-cell metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 7882.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n",
      "Computing Psix score in 2087 exons\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 2087/2087 [00:23<00:00, 89.86it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed Psix score of exons.\n",
      "Estimating p-values. This might take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/25 [01:03<?, ?it/s]Process ForkPoolWorker-450:\n",
      "Process ForkPoolWorker-448:\n",
      "Process ForkPoolWorker-443:\n",
      "Process ForkPoolWorker-449:\n",
      "Process ForkPoolWorker-447:\n",
      "Process ForkPoolWorker-440:\n",
      "Process ForkPoolWorker-445:\n",
      "Process ForkPoolWorker-438:\n",
      "Process ForkPoolWorker-432:\n",
      "Process ForkPoolWorker-431:\n",
      "Process ForkPoolWorker-446:\n",
      "Process ForkPoolWorker-442:\n",
      "Process ForkPoolWorker-436:\n",
      "Process ForkPoolWorker-441:\n",
      "Process ForkPoolWorker-437:\n",
      "Process ForkPoolWorker-430:\n",
      "Process ForkPoolWorker-426:\n",
      "Process ForkPoolWorker-428:\n",
      "Process ForkPoolWorker-433:\n",
      "Process ForkPoolWorker-444:\n",
      "Process ForkPoolWorker-435:\n",
      "Process ForkPoolWorker-434:\n",
      "Process ForkPoolWorker-439:\n",
      "Process ForkPoolWorker-427:\n",
      "Process ForkPoolWorker-429:\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 733\u001b[0;31m                 \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_items\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    734\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: pop from an empty deque",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-d2d60a415621>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mpsix_scores_15\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpsix_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpsix_results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mpsix_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_psix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'data/pc3_rd.tab.gz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapture_efficiency\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0mpsix_scores_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpsix_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpsix_results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/psix/psix.py\u001b[0m in \u001b[0;36mrun_psix\u001b[0;34m(self, n_jobs, capture_efficiency, min_probability, seed, pvals_bins, n_random_exons, latent, n_neighbors, weight_metric, turbo)\u001b[0m\n\u001b[1;32m    160\u001b[0m                         \u001b[0mn_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_neighbors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m                         \u001b[0mweight_metric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweight_metric\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m                         \u001b[0mturbo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mturbo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m                        )\n\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/psix/score_functions.py\u001b[0m in \u001b[0;36mcompute_psix_scores\u001b[0;34m(self, n_jobs, capture_efficiency, min_probability, seed, pvals_bins, n_random_exons, latent, n_neighbors, weight_metric, turbo)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Estimating p-values. This might take a while...'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m     \u001b[0mcompute_pvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Successfully estimated p-values'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/psix/score_functions.py\u001b[0m in \u001b[0;36mcompute_pvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    134\u001b[0m                 tqdm(\n\u001b[1;32m    135\u001b[0m                     \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_exons_parallel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_buckets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m                     \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_buckets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m                 )\n\u001b[1;32m    138\u001b[0m             )\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1129\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1130\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    735\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_length\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    738\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m                     \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_items\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "psix_object = psix.Psix(psi_table = 'psix_output/psix_object/psi.tab.gz',\n",
    "                        mrna_table = 'psix_output/psix_object/mrna.tab.gz')\n",
    "\n",
    "\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.01)\n",
    "psix_scores_01 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.02)\n",
    "psix_scores_02 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.05)\n",
    "psix_scores_05 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.1)\n",
    "psix_scores_1 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.15)\n",
    "psix_scores_15 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.2)\n",
    "psix_scores_2 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.25)\n",
    "psix_scores_25 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.3)\n",
    "psix_scores_3 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.35)\n",
    "psix_scores_35 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.4)\n",
    "psix_scores_4 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.45)\n",
    "psix_scores_45 = psix_object.psix_results\n",
    "\n",
    "psix_object.run_psix(latent='data/pc3_rd.tab.gz', n_jobs=25, capture_efficiency = 0.5)\n",
    "psix_scores_5 = psix_object.psix_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_df = pd.DataFrame()\n",
    "scores_df['0.5'] = psix_scores_5.psix_score\n",
    "scores_df['0.45'] = psix_scores_45.psix_score\n",
    "scores_df['0.4'] = psix_scores_4.psix_score\n",
    "scores_df['0.35'] = psix_scores_35.psix_score\n",
    "scores_df['0.3'] = psix_scores_3.psix_score\n",
    "scores_df['0.25'] = psix_scores_25.psix_score\n",
    "scores_df['0.2'] = psix_scores_2.psix_score\n",
    "scores_df['0.15'] = psix_scores_15.psix_score\n",
    "scores_df['0.1'] = psix_scores_1.psix_score\n",
    "scores_df['0.05'] = psix_scores_05.psix_score\n",
    "scores_df['0.01'] = psix_scores_01.psix_score\n",
    "\n",
    "scores_df.to_csv('psix_output/scores_different_capture_efficiency.tab.gz', sep='\\t', index=True, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_df = pd.read_csv('psix_output/scores_different_capture_efficiency.tab.gz', sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig = plt.figure(figsize=(8/2.4, 6/2.4))\n",
    "\n",
    "sns.heatmap(scores_df.corr(), cbar_kws={'label': 'Pearson r'})\n",
    "plt.xlabel('Capture efficiency')\n",
    "plt.ylabel('Capture efficiency')\n",
    "\n",
    "plt.title('Psix score correlation')\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/scores_corr.pdf', bbox_inches='tight', res=20000, dpi =2000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1067/1067 [00:00<00:00, 6408.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully computed cell-cell metric\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "psix_object.get_cell_metric(latent='data/pc3_rd.tab.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[   0,  226,  313, ...,  282,  254,  165],\n",
       "        [   1,   41,  266, ...,  278,  104,    6],\n",
       "        [   2,  181,  151, ...,  264,  191,  315],\n",
       "        ...,\n",
       "        [1064, 1024,  877, ...,  760,  856,  919],\n",
       "        [1065,  929,  850, ..., 1060,  916,  933],\n",
       "        [1066,  846,  984, ..., 1057,  978, 1025]]),\n",
       " array([[1.        , 0.94528444, 0.93698675, ..., 0.37574981, 0.37116028,\n",
       "         0.36787944],\n",
       "        [1.        , 0.98181215, 0.95971308, ..., 0.36951322, 0.36904499,\n",
       "         0.36787944],\n",
       "        [1.        , 0.99884255, 0.98368791, ..., 0.39495958, 0.36788525,\n",
       "         0.36787944],\n",
       "        ...,\n",
       "        [1.        , 0.97946941, 0.9653081 , ..., 0.37372245, 0.36813589,\n",
       "         0.36787944],\n",
       "        [1.        , 0.97106629, 0.96046188, ..., 0.37197329, 0.37145342,\n",
       "         0.36787944],\n",
       "        [1.        , 0.92826533, 0.91702335, ..., 0.3730977 , 0.36939013,\n",
       "         0.36787944]]))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psix_object.metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "psix_object.adata.uns['neighbors_psi'][psix_object.modules.loc[psix_object.modules == 1].index]\n",
    "\n",
    "mad_df = pd.DataFrame()\n",
    "for exon in psix_object.modules.index[psix_object.modules==1]:\n",
    "    mad_df[exon] = zscore(psix_object.adata.uns['neighbors_psi'].T.loc[exon])\n",
    "mad_df.index= psix_object.adata.uns['psi'].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SRR7408400   -1.167352\n",
       "SRR7408401   -1.391606\n",
       "SRR7408404   -1.603516\n",
       "SRR7408413   -1.445175\n",
       "SRR7408414   -1.541741\n",
       "                ...   \n",
       "SRR7410092    0.998439\n",
       "SRR7410093    1.299189\n",
       "SRR7410094    0.979144\n",
       "SRR7410096    1.308914\n",
       "SRR7410097    1.152548\n",
       "Length: 1067, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mad_df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 136.346x119.764 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.gridspec import GridSpec\n",
    "fig = plt.figure(figsize=(7.4*cm*0.65, 6.5*cm*0.65))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "# gs = GridSpec(1,5)\n",
    "# gs.update(wspace=0.05, hspace=0.10)\n",
    "# ax = fig.add_subplot(gs[0,0:4], projection='3d')\n",
    "# ax_2 = fig.add_subplot(gs[0,4])\n",
    "ax.grid(False)\n",
    "ax.scatter(psix_object.latent.PC_1, psix_object.latent.PC_2, psix_object.latent.PC_3, c=mad_df.mean(axis=1), \n",
    "           s=6, linewidth=0, alpha=0.9)\n",
    "# ax.scatter(psix_object.latent.PC_1, psix_object.latent.PC_3, c=colores)\n",
    "# ax.set_yticks([-10, -5, 0, 5, 10])\n",
    "# ax.tick_params(labelsize=8, length=5)#, direction=\"in\", pad=-22)\n",
    "# ax.tick_params(axis='y',labelsize=8, length=5, direction=\"in\", pad=-15)\n",
    "# ax.tick_params(axis='x',labelsize=8, length=5, direction=\"out\", pad=-5)\n",
    "# ax.tick_params(axis='z',labelsize=8, length=5, direction=\"out\", pad=-4)\n",
    "\n",
    "\n",
    "# ax.set_title('Mouse neurogenesis', fontsize=12)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_zticks([])\n",
    "\n",
    "\n",
    "# ax.set_title('Mouse neurogenesis', fontsize=12)\n",
    "ax.set_xlabel('PC 1', fontsize=8,labelpad=-13)\n",
    "ax.set_ylabel('PC 2', fontsize=8,labelpad=-13)\n",
    "ax.set_zlabel('PC 3', fontsize=8,labelpad=-15, rotation=90)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.set_title('Module1', fontsize=12)\n",
    "\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "# Now set color to white (or whatever is \"invisible\")\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "plt.tight_layout()\n",
    "\n",
    "#     plt.show()\n",
    "\n",
    "plt.savefig('/mnt/lareaulab/cfbuenabadn/plots_pdf/mod1_with_axes.pdf', bbox_inches='tight', res=20000, dpi =2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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