{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from scipy.stats import multivariate_normal\n",
    "import gpflow\n",
    "import gpflow.multioutput.kernels as mk\n",
    "import gpflow.multioutput.features as mf\n",
    "import pickle\n",
    "import sys\n",
    "sys.path.insert(1, \"../ipsc_gp_clustering\")\n",
    "from splitgpm import SplitGPM\n",
    "from utils import gen_gsea_df\n",
    "import gpflow.training.monitor as mon\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "random.seed(2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hi_res=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions for splitGPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(X):\n",
    "    return X / X.sum(1)[:, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_splitGPM(X, Y, C, G, T, K1, K2, N):\n",
    "    gpflow.reset_default_graph_and_session()\n",
    "    name = 'test'\n",
    "    minibatch_size = 500\n",
    "    W1_init = normalize(np.random.random(size=(C, K1)))\n",
    "    W2_init = normalize(np.random.random(size=(G, K2)))\n",
    "    with gpflow.defer_build():\n",
    "        kernel = mk.SharedIndependentMok(gpflow.kernels.RBF(1, active_dims=[0]), K1 * K2)\n",
    "        Z = np.linspace(0, 1, T)[:, None].astype(np.float64)\n",
    "        feature = gpflow.features.InducingPoints(Z)\n",
    "        feature = mf.SharedIndependentMof(feature)\n",
    "        model = SplitGPM(X, Y, np.log(W1_init + 1e-5), np.log(W2_init + 1e-5), kernel, gpflow.likelihoods.Gaussian(), feat=feature, minibatch_size=minibatch_size, name=name)\n",
    "    model.compile()\n",
    "    model.W1.set_trainable(True)  # learn cell assignments\n",
    "    model.W2.set_trainable(True)  # learn gene assignments\n",
    "    model.feature.set_trainable(True)  # move inducing points\n",
    "    model.kern.set_trainable(True)  # learn kernel parameters\n",
    "    model.likelihood.set_trainable(True)  # learn likelihood parameters\n",
    "    adam = gpflow.train.AdamOptimizer(0.005)\n",
    "    adam.minimize(model, maxiter=10000)\n",
    "    return(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_gsea(m, genedict):\n",
    "    assignments = m.W2.value.argmax(1)\n",
    "    gene_names = np.array(list(genedict.keys()))\n",
    "    confidence = m.W2.value.max(1)\n",
    "    assignments = pd.DataFrame({'gene': gene_names, 'clust_1cell_10gene': assignments, 'conf_1cell_10gene': confidence})\n",
    "    clusters = assignments['clust_1cell_10gene']\n",
    "    gene_names = assignments['gene']\n",
    "    gsea_results = gen_gsea_df(clusters, gene_names, 'gsea_output', threshold=1e-2, rerun=True)\n",
    "    idx = pd.IndexSlice\n",
    "    active_clusters = np.where(np.any(gsea_results.T.loc[:, idx[:, 'bonferonni-adjusted']] < 0.01, axis=0) == 1)[0]\n",
    "    active_results = pd.concat([gsea_results.T.iloc[:, gsea_results.index.get_level_values(0).get_loc(a)] for a in active_clusters], axis=1)\n",
    "    active_gene_sets = np.where(np.any(active_results.loc[:, idx[:, 'bonferonni-adjusted']] < 0.01, axis=1))[0]\n",
    "    active_results = active_results.iloc[active_gene_sets]\n",
    "    return(active_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Endothelial lineage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind_endo = np.loadtxt(\"../data/endo.dpt.ind.X.txt\")\n",
    "    Y_ind_endo = np.loadtxt(\"../data/endo.dpt.ind.Y.txt\")\n",
    "else:\n",
    "    X_ind_endo = np.loadtxt(\"../data/endo.dpt.hires.ind.X.txt\")\n",
    "    Y_ind_endo = np.loadtxt(\"../data/endo.dpt.hires.ind.Y.txt\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=3\n",
    "G=int(max(X_ind_endo[:,2])+1)\n",
    "T=5\n",
    "K1=1\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_ind' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-9b183a58ac9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_splitGPM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_ind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_ind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_ind' is not defined"
     ]
    }
   ],
   "source": [
    "m = run_splitGPM(X_ind, Y_ind, C, G, T, K1, K2, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind = np.linspace(0, 1, 100)[:, None]\n",
    "mu, var = m.predict_f(Xnew_ind)\n",
    "fig, ax = plt.subplots(1,5, figsize=(20,4))\n",
    "mu = mu.T.reshape((K1, K2, -1)).T\n",
    "var = var.T.reshape((K1, K2, -1)).T\n",
    "a1 = m.W1.value.argmax(1)[X_ind[:, 1].astype(int)]\n",
    "a2 = m.W2.value.argmax(1)[X_ind[:, 2].astype(int)]\n",
    "for k in range(K1):\n",
    "    for l in range(K2):\n",
    "        ax[l%5].plot(Xnew_ind, mu[:, l, k])\n",
    "        ax[l%5].set_title(\"Cluster %d\" % l)\n",
    "        ax[l%5].fill_between(\n",
    "            Xnew_ind.flatten(),\n",
    "            mu[:, l, k] - np.sqrt(var[:, l, k])*2,\n",
    "            mu[:, l, k] + np.sqrt(var[:, l, k])*2, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/endo.dpt.ind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/endo.dpt.hires.ind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
       "      <td>1.06848e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000749352</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.81446e-19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00243083</td>\n",
       "      <td>0.48994</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.37115e-18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.184287</td>\n",
       "      <td>9.66338e-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.42229e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <td>0.00420569</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_KRAS_SIGNALING_UP</th>\n",
       "      <td>0.00213519</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             0  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                   1.06848e-05   \n",
       "HALLMARK_MITOTIC_SPINDLE                                     1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_MTORC1_SIGNALING                                    1   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V1                                      1   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_COAGULATION                                0.00420569   \n",
       "HALLMARK_KRAS_SIGNALING_UP                          0.00213519   \n",
       "\n",
       "                                                             1  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1   \n",
       "HALLMARK_MITOTIC_SPINDLE                                     1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_MTORC1_SIGNALING                           0.00243083   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V1                               0.184287   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_COAGULATION                                         1   \n",
       "HALLMARK_KRAS_SIGNALING_UP                                   1   \n",
       "\n",
       "                                                             2  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1   \n",
       "HALLMARK_MITOTIC_SPINDLE                           0.000749352   \n",
       "HALLMARK_G2M_CHECKPOINT                            1.81446e-19   \n",
       "HALLMARK_MTORC1_SIGNALING                              0.48994   \n",
       "HALLMARK_E2F_TARGETS                               1.37115e-18   \n",
       "HALLMARK_MYC_TARGETS_V1                            9.66338e-06   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_COAGULATION                                         1   \n",
       "HALLMARK_KRAS_SIGNALING_UP                                   1   \n",
       "\n",
       "                                                             4  \n",
       "                                           bonferonni-adjusted  \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1  \n",
       "HALLMARK_MITOTIC_SPINDLE                                     1  \n",
       "HALLMARK_G2M_CHECKPOINT                                      1  \n",
       "HALLMARK_MTORC1_SIGNALING                                    1  \n",
       "HALLMARK_E2F_TARGETS                                         1  \n",
       "HALLMARK_MYC_TARGETS_V1                                      1  \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION         5.42229e-07  \n",
       "HALLMARK_COAGULATION                                         1  \n",
       "HALLMARK_KRAS_SIGNALING_UP                                   1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results = view_gsea(m, genedict)\n",
    "active_results.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind = np.loadtxt(\"../data/endo.dpt.batch.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/endo.dpt.batch.Y.txt\")\n",
    "else:\n",
    "    X_ind = np.loadtxt(\"../data/endo.dpt.hires.batch.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/endo.dpt.hires.batch.Y.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=3\n",
    "G=int(max(X_ind[:,2])+1)\n",
    "T=5\n",
    "K1=1\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "m = run_splitGPM(X_ind, Y_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind = np.linspace(0, 1, 100)[:, None]\n",
    "mu, var = m.predict_f(Xnew_ind)\n",
    "fig, ax = plt.subplots(1,5, figsize=(20,4))\n",
    "mu = mu.T.reshape((K1, K2, -1)).T\n",
    "var = var.T.reshape((K1, K2, -1)).T\n",
    "a1 = m.W1.value.argmax(1)[X_ind[:, 1].astype(int)]\n",
    "a2 = m.W2.value.argmax(1)[X_ind[:, 2].astype(int)]\n",
    "for k in range(K1):\n",
    "    for l in range(K2):\n",
    "        ax[l%5].plot(Xnew_ind, mu[:, l, k])\n",
    "        ax[l%5].set_title(\"Cluster %d\" % l)\n",
    "        ax[l%5].fill_between(\n",
    "            Xnew_ind.flatten(),\n",
    "            mu[:, l, k] - np.sqrt(var[:, l, k])*2,\n",
    "            mu[:, l, k] + np.sqrt(var[:, l, k])*2, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/endo.dpt.batch.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/endo.dpt.hires.batch.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
       "      <td>0.000174237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0092255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.46442e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_APOPTOSIS</th>\n",
       "      <td>0.0012492</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_INTERFERON_GAMMA_RESPONSE</th>\n",
       "      <td>0.00968748</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COMPLEMENT</th>\n",
       "      <td>0.000844793</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000307116</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.61959e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370398</td>\n",
       "      <td>4.97298e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00129037</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION</th>\n",
       "      <td>1</td>\n",
       "      <td>6.83434e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_OXIDATIVE_PHOSPHORYLATION</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00763834</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <td>0.000924058</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_IL2_STAT5_SIGNALING</th>\n",
       "      <td>0.00675158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_KRAS_SIGNALING_UP</th>\n",
       "      <td>0.00669579</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             0  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                   0.000174237   \n",
       "HALLMARK_MITOTIC_SPINDLE                                     1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_APOPTOSIS                                   0.0012492   \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE                  0.00968748   \n",
       "HALLMARK_COMPLEMENT                                0.000844793   \n",
       "HALLMARK_MTORC1_SIGNALING                                    1   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V1                                      1   \n",
       "HALLMARK_MYC_TARGETS_V2                                      1   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                           1   \n",
       "HALLMARK_COAGULATION                               0.000924058   \n",
       "HALLMARK_IL2_STAT5_SIGNALING                        0.00675158   \n",
       "HALLMARK_KRAS_SIGNALING_UP                          0.00669579   \n",
       "\n",
       "                                                             1  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1   \n",
       "HALLMARK_MITOTIC_SPINDLE                                     1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_APOPTOSIS                                           1   \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE                           1   \n",
       "HALLMARK_COMPLEMENT                                          1   \n",
       "HALLMARK_MTORC1_SIGNALING                                    1   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V1                                      1   \n",
       "HALLMARK_MYC_TARGETS_V2                                      1   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION         6.83434e-06   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                           1   \n",
       "HALLMARK_COAGULATION                                         1   \n",
       "HALLMARK_IL2_STAT5_SIGNALING                                 1   \n",
       "HALLMARK_KRAS_SIGNALING_UP                                   1   \n",
       "\n",
       "                                                             3  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1   \n",
       "HALLMARK_MITOTIC_SPINDLE                                     1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_APOPTOSIS                                           1   \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE                           1   \n",
       "HALLMARK_COMPLEMENT                                          1   \n",
       "HALLMARK_MTORC1_SIGNALING                          0.000307116   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V1                               0.370398   \n",
       "HALLMARK_MYC_TARGETS_V2                             0.00129037   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                  0.00763834   \n",
       "HALLMARK_COAGULATION                                         1   \n",
       "HALLMARK_IL2_STAT5_SIGNALING                                 1   \n",
       "HALLMARK_KRAS_SIGNALING_UP                                   1   \n",
       "\n",
       "                                                             4  \n",
       "                                           bonferonni-adjusted  \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1  \n",
       "HALLMARK_MITOTIC_SPINDLE                             0.0092255  \n",
       "HALLMARK_G2M_CHECKPOINT                            3.46442e-18  \n",
       "HALLMARK_APOPTOSIS                                           1  \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE                           1  \n",
       "HALLMARK_COMPLEMENT                                          1  \n",
       "HALLMARK_MTORC1_SIGNALING                                    1  \n",
       "HALLMARK_E2F_TARGETS                               3.61959e-22  \n",
       "HALLMARK_MYC_TARGETS_V1                            4.97298e-06  \n",
       "HALLMARK_MYC_TARGETS_V2                                      1  \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1  \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                           1  \n",
       "HALLMARK_COAGULATION                                         1  \n",
       "HALLMARK_IL2_STAT5_SIGNALING                                 1  \n",
       "HALLMARK_KRAS_SIGNALING_UP                                   1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results = view_gsea(m, genedict)\n",
    "active_results.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by batch and individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind_endo = np.loadtxt(\"../data/endo.dpt.batchind.X.txt\")\n",
    "    Y_ind_endo = np.loadtxt(\"../data/endo.dpt.batchind.Y.txt\")\n",
    "else:\n",
    "    X_ind_endo = np.loadtxt(\"../data/endo.dpt.hires.batchind.X.txt\")\n",
    "    Y_ind_endo = np.loadtxt(\"../data/endo.dpt.hires.batchind.Y.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=9\n",
    "G=int(max(X_ind_endo[:,2])+1)\n",
    "T=5\n",
    "K1=3\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "WARNING:tensorflow:From /work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /work-zfs/abattle4/josh/ebpilot/myenv/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    }
   ],
   "source": [
    "m_endo = run_splitGPM(X_ind_endo, Y_ind_endo, C, G, T, K1, K2, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How were (cell-line/ batch) pairs clustered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/endo.dpt.batchind.linedict.pickle\", 'rb') as f:\n",
    "        linedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/endo.dpt.hires.batchind.linedict.pickle\", 'rb') as f:\n",
    "        linedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>cell line/batch</th>\n",
       "      <th>assignment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SNG-NA18511--Batch1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SNG-NA18511--Batch2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SNG-NA18511--Batch3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SNG-NA18858--Batch1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SNG-NA18858--Batch2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SNG-NA18858--Batch3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SNG-NA19160--Batch1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SNG-NA19160--Batch2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SNG-NA19160--Batch3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cell line/batch  assignment\n",
       "0  SNG-NA18511--Batch1           0\n",
       "1  SNG-NA18511--Batch2           0\n",
       "2  SNG-NA18511--Batch3           0\n",
       "3  SNG-NA18858--Batch1           2\n",
       "4  SNG-NA18858--Batch2           2\n",
       "5  SNG-NA18858--Batch3           2\n",
       "6  SNG-NA19160--Batch1           0\n",
       "7  SNG-NA19160--Batch2           0\n",
       "8  SNG-NA19160--Batch3           0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_clusts = pd.DataFrame(data=linedict.values(), columns=[\"cell line/batch\"])\n",
    "cell_clusts['assignment'] = m_endo.W1.value.argmax(1)\n",
    "cell_clusts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind_endo = np.linspace(0, 1, 100)[:, None]\n",
    "mu_endo, var_endo = m_endo.predict_f(Xnew_ind_endo)\n",
    "fig_endo, ax_endo = plt.subplots(1,5, figsize=(20,4))\n",
    "mu_endo = mu_endo.T.reshape((K1, K2, -1)).T\n",
    "var_endo = var_endo.T.reshape((K1, K2, -1)).T\n",
    "a1_endo = m_endo.W1.value.argmax(1)[X_ind_endo[:, 1].astype(int)]\n",
    "a2_endo = m_endo.W2.value.argmax(1)[X_ind_endo[:, 2].astype(int)]\n",
    "line_leg = []\n",
    "for k in range(K1):\n",
    "    if sum(a1_endo == k) == 0:\n",
    "        continue  # no need to plot empty modules\n",
    "    else:\n",
    "        line_leg.append(\"individual/replicate cluster \" + str(k))\n",
    "    for l in range(K2):\n",
    "        ax_endo[l%5].plot(Xnew_ind_endo, mu_endo[:, l, k])\n",
    "        ax_endo[l%5].set_title(\"Gene Cluster %d\" % l)\n",
    "        ax_endo[l%5].fill_between(\n",
    "            Xnew_ind_endo.flatten(),\n",
    "            mu_endo[:, l, k] - np.sqrt(var_endo[:, l, k])*2,\n",
    "            mu_endo[:, l, k] + np.sqrt(var_endo[:, l, k])*2, alpha=0.3)\n",
    "ax_endo[l%5].legend(line_leg, bbox_to_anchor=(2, 1))\n",
    "fig_endo.savefig('../figs/endo_gene_clusts.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/endo.dpt.batchind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/endo.dpt.hires.batchind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.10533e-07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.08289e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_APOPTOSIS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000118488</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_APICAL_JUNCTION</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000755343</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COMPLEMENT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5339e-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
       "      <td>1</td>\n",
       "      <td>1.78151e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.35097e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000987191</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION</th>\n",
       "      <td>6.02819e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_INFLAMMATORY_RESPONSE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00462036</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_OXIDATIVE_PHOSPHORYLATION</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00108741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000313316</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_IL2_STAT5_SIGNALING</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00202719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             1  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_APOPTOSIS                                           1   \n",
       "HALLMARK_APICAL_JUNCTION                                     1   \n",
       "HALLMARK_COMPLEMENT                                          1   \n",
       "HALLMARK_MTORC1_SIGNALING                                    1   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V2                                      1   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION         6.02819e-05   \n",
       "HALLMARK_INFLAMMATORY_RESPONSE                               1   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                           1   \n",
       "HALLMARK_COAGULATION                                         1   \n",
       "HALLMARK_IL2_STAT5_SIGNALING                                 1   \n",
       "\n",
       "                                                             2  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_APOPTOSIS                                           1   \n",
       "HALLMARK_APICAL_JUNCTION                                     1   \n",
       "HALLMARK_COMPLEMENT                                          1   \n",
       "HALLMARK_MTORC1_SIGNALING                          1.78151e-05   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V2                            0.000987191   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_INFLAMMATORY_RESPONSE                               1   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                  0.00108741   \n",
       "HALLMARK_COAGULATION                                         1   \n",
       "HALLMARK_IL2_STAT5_SIGNALING                                 1   \n",
       "\n",
       "                                                             3  \\\n",
       "                                           bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                   4.10533e-07   \n",
       "HALLMARK_G2M_CHECKPOINT                                      1   \n",
       "HALLMARK_APOPTOSIS                                 0.000118488   \n",
       "HALLMARK_APICAL_JUNCTION                           0.000755343   \n",
       "HALLMARK_COMPLEMENT                                 6.5339e-06   \n",
       "HALLMARK_MTORC1_SIGNALING                                    1   \n",
       "HALLMARK_E2F_TARGETS                                         1   \n",
       "HALLMARK_MYC_TARGETS_V2                                      1   \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1   \n",
       "HALLMARK_INFLAMMATORY_RESPONSE                      0.00462036   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                           1   \n",
       "HALLMARK_COAGULATION                               0.000313316   \n",
       "HALLMARK_IL2_STAT5_SIGNALING                        0.00202719   \n",
       "\n",
       "                                                             4  \n",
       "                                           bonferonni-adjusted  \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                             1  \n",
       "HALLMARK_G2M_CHECKPOINT                            8.08289e-08  \n",
       "HALLMARK_APOPTOSIS                                           1  \n",
       "HALLMARK_APICAL_JUNCTION                                     1  \n",
       "HALLMARK_COMPLEMENT                                          1  \n",
       "HALLMARK_MTORC1_SIGNALING                                    1  \n",
       "HALLMARK_E2F_TARGETS                               3.35097e-13  \n",
       "HALLMARK_MYC_TARGETS_V2                                      1  \n",
       "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION                   1  \n",
       "HALLMARK_INFLAMMATORY_RESPONSE                               1  \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                           1  \n",
       "HALLMARK_COAGULATION                                         1  \n",
       "HALLMARK_IL2_STAT5_SIGNALING                                 1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results_endo = view_gsea(m_endo, genedict)\n",
    "active_results_endo.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_results_endo.to_csv(\"../results/endo_gsea_batchind.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How consistent is this cell line/batch clustering?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "n_runs = 10\n",
    "similarity_endo = np.zeros([C,C], dtype=int)\n",
    "for i in range(n_runs):\n",
    "    mi = run_splitGPM(X_ind_endo, Y_ind_endo, C, G, T, K1, K2, N)\n",
    "    for j in range(C):\n",
    "        for k in range(j):\n",
    "            if mi.W1.value.argmax(1)[j] == mi.W1.value.argmax(1)[k]:\n",
    "                similarity_endo[j,k] += 1\n",
    "                similarity_endo[k,j] += 1\n",
    "for j in range(C):\n",
    "    similarity_endo[j,j] = n_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = [g[6:].replace(\"Batch\", \"Replicate\") for g in list(linedict.values())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, '18511--Replicate1'),\n",
       " Text(1.5, 0, '18511--Replicate2'),\n",
       " Text(2.5, 0, '18511--Replicate3'),\n",
       " Text(3.5, 0, '18858--Replicate1'),\n",
       " Text(4.5, 0, '18858--Replicate2'),\n",
       " Text(5.5, 0, '18858--Replicate3'),\n",
       " Text(6.5, 0, '19160--Replicate1'),\n",
       " Text(7.5, 0, '19160--Replicate2'),\n",
       " Text(8.5, 0, '19160--Replicate3')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm_endo = sns.heatmap(similarity_endo, cmap=\"OrRd\",\n",
    "           xticklabels=groups,\n",
    "           yticklabels=groups)\n",
    "hm_endo.set_xticklabels(hm_endo.get_xticklabels(), \n",
    "                          rotation=45, \n",
    "                          horizontalalignment='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm_endo.figure.savefig('../figs/endo_cluster_stability.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hepatocyte lineage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by individual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that one sample was removed - the second individual at the last timepoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind = np.loadtxt(\"../data/hep.dpt.ind.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/hep.dpt.ind.Y.txt\")\n",
    "else:\n",
    "    X_ind = np.loadtxt(\"../data/hep.dpt.hires.ind.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/hep.dpt.hires.ind.Y.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=3\n",
    "G=int(max(X_ind[:,2])+1)\n",
    "T=5\n",
    "K1=1\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "m = run_splitGPM(X_ind, Y_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind = np.linspace(0, 1, 100)[:, None]\n",
    "mu, var = m.predict_f(Xnew_ind)\n",
    "fig, ax = plt.subplots(1,5, figsize=(20,4))\n",
    "mu = mu.T.reshape((K1, K2, -1)).T\n",
    "var = var.T.reshape((K1, K2, -1)).T\n",
    "a1 = m.W1.value.argmax(1)[X_ind[:, 1].astype(int)]\n",
    "a2 = m.W2.value.argmax(1)[X_ind[:, 2].astype(int)]\n",
    "for k in range(K1):\n",
    "    for l in range(K2):\n",
    "        ax[l%5].plot(Xnew_ind, mu[:, l, k])\n",
    "        ax[l%5].set_title(\"Cluster %d\" % l)\n",
    "        ax[l%5].fill_between(\n",
    "            Xnew_ind.flatten(),\n",
    "            mu[:, l, k] - np.sqrt(var[:, l, k])*2,\n",
    "            mu[:, l, k] + np.sqrt(var[:, l, k])*2, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/hep.dpt.ind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/hep.dpt.hires.ind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00493268</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_CHOLESTEROL_HOMEOSTASIS</th>\n",
       "      <td>1.66124e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.08719e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.56011e-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
       "      <td>4.71161e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COMPLEMENT</th>\n",
       "      <td>2.61338e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.63956e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.55813e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_XENOBIOTIC_METABOLISM</th>\n",
       "      <td>2.42258e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_FATTY_ACID_METABOLISM</th>\n",
       "      <td>1.05753e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <td>9.67912e-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_BILE_ACID_METABOLISM</th>\n",
       "      <td>2.3321e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_PEROXISOME</th>\n",
       "      <td>0.000592552</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   1                   2  \\\n",
       "                                 bonferonni-adjusted bonferonni-adjusted   \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                   1          0.00493268   \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS         1.66124e-06                   1   \n",
       "HALLMARK_MITOTIC_SPINDLE                           1                   1   \n",
       "HALLMARK_G2M_CHECKPOINT                            1                   1   \n",
       "HALLMARK_ADIPOGENESIS                    4.71161e-05                   1   \n",
       "HALLMARK_COMPLEMENT                      2.61338e-05                   1   \n",
       "HALLMARK_E2F_TARGETS                               1                   1   \n",
       "HALLMARK_MYC_TARGETS_V1                            1                   1   \n",
       "HALLMARK_XENOBIOTIC_METABOLISM           2.42258e-09                   1   \n",
       "HALLMARK_FATTY_ACID_METABOLISM           1.05753e-05                   1   \n",
       "HALLMARK_COAGULATION                     9.67912e-14                   1   \n",
       "HALLMARK_BILE_ACID_METABOLISM             2.3321e-06                   1   \n",
       "HALLMARK_PEROXISOME                      0.000592552                   1   \n",
       "\n",
       "                                                   4  \n",
       "                                 bonferonni-adjusted  \n",
       "HALLMARK_TNFA_SIGNALING_VIA_NFKB                   1  \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS                   1  \n",
       "HALLMARK_MITOTIC_SPINDLE                 4.08719e-10  \n",
       "HALLMARK_G2M_CHECKPOINT                  2.56011e-28  \n",
       "HALLMARK_ADIPOGENESIS                              1  \n",
       "HALLMARK_COMPLEMENT                                1  \n",
       "HALLMARK_E2F_TARGETS                     1.63956e-36  \n",
       "HALLMARK_MYC_TARGETS_V1                  2.55813e-09  \n",
       "HALLMARK_XENOBIOTIC_METABOLISM                     1  \n",
       "HALLMARK_FATTY_ACID_METABOLISM                     1  \n",
       "HALLMARK_COAGULATION                               1  \n",
       "HALLMARK_BILE_ACID_METABOLISM                      1  \n",
       "HALLMARK_PEROXISOME                                1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results = view_gsea(m, genedict)\n",
    "active_results.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind = np.loadtxt(\"../data/hep.dpt.batch.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/hep.dpt.batch.Y.txt\")\n",
    "else:\n",
    "    X_ind = np.loadtxt(\"../data/hep.dpt.hires.batch.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/hep.dpt.hires.batch.Y.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=3\n",
    "G=int(max(X_ind[:,2])+1)\n",
    "T=5\n",
    "K1=1\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "m = run_splitGPM(X_ind, Y_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind = np.linspace(0, 1, 100)[:, None]\n",
    "mu, var = m.predict_f(Xnew_ind)\n",
    "fig, ax = plt.subplots(1,5, figsize=(20,4))\n",
    "mu = mu.T.reshape((K1, K2, -1)).T\n",
    "var = var.T.reshape((K1, K2, -1)).T\n",
    "a1 = m.W1.value.argmax(1)[X_ind[:, 1].astype(int)]\n",
    "a2 = m.W2.value.argmax(1)[X_ind[:, 2].astype(int)]\n",
    "for k in range(K1):\n",
    "    for l in range(K2):\n",
    "        ax[l%5].plot(Xnew_ind, mu[:, l, k])\n",
    "        ax[l%5].set_title(\"Cluster %d\" % l)\n",
    "        ax[l%5].fill_between(\n",
    "            Xnew_ind.flatten(),\n",
    "            mu[:, l, k] - np.sqrt(var[:, l, k])*2,\n",
    "            mu[:, l, k] + np.sqrt(var[:, l, k])*2, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/hep.dpt.batch.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/hep.dpt.hires.batch.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_CHOLESTEROL_HOMEOSTASIS</th>\n",
       "      <td>4.92928e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39438e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.63421e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
       "      <td>4.38593e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ANDROGEN_RESPONSE</th>\n",
       "      <td>0.00111495</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COMPLEMENT</th>\n",
       "      <td>3.40796e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.07518e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.36638e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00442956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_XENOBIOTIC_METABOLISM</th>\n",
       "      <td>3.81339e-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_FATTY_ACID_METABOLISM</th>\n",
       "      <td>1.63731e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <td>9.3214e-13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_BILE_ACID_METABOLISM</th>\n",
       "      <td>4.55067e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_PEROXISOME</th>\n",
       "      <td>0.000691368</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0                   1  \\\n",
       "                                 bonferonni-adjusted bonferonni-adjusted   \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS         4.92928e-05                   1   \n",
       "HALLMARK_MITOTIC_SPINDLE                           1                   1   \n",
       "HALLMARK_G2M_CHECKPOINT                            1                   1   \n",
       "HALLMARK_ADIPOGENESIS                    4.38593e-06                   1   \n",
       "HALLMARK_ANDROGEN_RESPONSE                0.00111495                   1   \n",
       "HALLMARK_COMPLEMENT                      3.40796e-05                   1   \n",
       "HALLMARK_E2F_TARGETS                               1                   1   \n",
       "HALLMARK_MYC_TARGETS_V1                            1                   1   \n",
       "HALLMARK_MYC_TARGETS_V2                            1          0.00442956   \n",
       "HALLMARK_XENOBIOTIC_METABOLISM           3.81339e-11                   1   \n",
       "HALLMARK_FATTY_ACID_METABOLISM           1.63731e-07                   1   \n",
       "HALLMARK_COAGULATION                      9.3214e-13                   1   \n",
       "HALLMARK_BILE_ACID_METABOLISM            4.55067e-07                   1   \n",
       "HALLMARK_PEROXISOME                      0.000691368                   1   \n",
       "\n",
       "                                                   2  \n",
       "                                 bonferonni-adjusted  \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS                   1  \n",
       "HALLMARK_MITOTIC_SPINDLE                 1.39438e-11  \n",
       "HALLMARK_G2M_CHECKPOINT                  1.63421e-27  \n",
       "HALLMARK_ADIPOGENESIS                              1  \n",
       "HALLMARK_ANDROGEN_RESPONSE                         1  \n",
       "HALLMARK_COMPLEMENT                                1  \n",
       "HALLMARK_E2F_TARGETS                     9.07518e-30  \n",
       "HALLMARK_MYC_TARGETS_V1                  3.36638e-08  \n",
       "HALLMARK_MYC_TARGETS_V2                            1  \n",
       "HALLMARK_XENOBIOTIC_METABOLISM                     1  \n",
       "HALLMARK_FATTY_ACID_METABOLISM                     1  \n",
       "HALLMARK_COAGULATION                               1  \n",
       "HALLMARK_BILE_ACID_METABOLISM                      1  \n",
       "HALLMARK_PEROXISOME                                1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results = view_gsea(m, genedict)\n",
    "active_results.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by both batch and individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind_hep = np.loadtxt(\"../data/hep.dpt.batchind.X.txt\")\n",
    "    Y_ind_hep = np.loadtxt(\"../data/hep.dpt.batchind.Y.txt\")\n",
    "else:\n",
    "    X_ind_hep = np.loadtxt(\"../data/hep.dpt.hires.batchind.X.txt\")\n",
    "    Y_ind_hep = np.loadtxt(\"../data/hep.dpt.hires.batchind.Y.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=9\n",
    "G=int(max(X_ind_hep[:,2])+1)\n",
    "T=5\n",
    "K1=3\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "m_hep = run_splitGPM(X_ind_hep, Y_ind_hep, C, G, T, K1, K2, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>cell line/batch</th>\n",
       "      <th>assignment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SNG-NA18511--Batch1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SNG-NA18511--Batch2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SNG-NA18511--Batch3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SNG-NA18858--Batch1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SNG-NA18858--Batch2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SNG-NA18858--Batch3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SNG-NA19160--Batch1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SNG-NA19160--Batch2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SNG-NA19160--Batch3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cell line/batch  assignment\n",
       "0  SNG-NA18511--Batch1           2\n",
       "1  SNG-NA18511--Batch2           2\n",
       "2  SNG-NA18511--Batch3           2\n",
       "3  SNG-NA18858--Batch1           2\n",
       "4  SNG-NA18858--Batch2           1\n",
       "5  SNG-NA18858--Batch3           1\n",
       "6  SNG-NA19160--Batch1           2\n",
       "7  SNG-NA19160--Batch2           2\n",
       "8  SNG-NA19160--Batch3           2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/hep.dpt.batchind.linedict.pickle\", 'rb') as f:\n",
    "        linedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/hep.dpt.hires.batchind.linedict.pickle\", 'rb') as f:\n",
    "        linedict = pickle.load(f)\n",
    "        f.close()\n",
    "cell_clusts_hep = pd.DataFrame(data=linedict.values(), columns=[\"cell line/batch\"])\n",
    "cell_clusts_hep['assignment'] = m_hep.W1.value.argmax(1)\n",
    "cell_clusts_hep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b34df8cceb8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind_hep = np.linspace(0, 1, 100)[:, None]\n",
    "mu_hep, var_hep = m_hep.predict_f(Xnew_ind_hep)\n",
    "fig_hep, ax_hep = plt.subplots(1,5, figsize=(20,4))\n",
    "mu_hep = mu_hep.T.reshape((K1, K2, -1)).T\n",
    "var_hep = var_hep.T.reshape((K1, K2, -1)).T\n",
    "a1_hep = m_hep.W1.value.argmax(1)[X_ind_hep[:, 1].astype(int)]\n",
    "a2_hep = m_hep.W2.value.argmax(1)[X_ind_hep[:, 2].astype(int)]\n",
    "line_leg = []\n",
    "for k in range(K1):\n",
    "    if sum(a1_hep == k) == 0:\n",
    "        continue  # no need to plot empty modules\n",
    "    else:\n",
    "        line_leg.append(\"line/replicate cluster \" + str(k))\n",
    "    for l in range(K2):\n",
    "        ax_hep[l%5].plot(Xnew_ind_hep, mu_hep[:, l, k])\n",
    "        ax_hep[l%5].set_title(\"Gene Cluster %d\" % l)\n",
    "        ax_hep[l%5].fill_between(\n",
    "            Xnew_ind_hep.flatten(),\n",
    "            mu_hep[:, l, k] - np.sqrt(var_hep[:, l, k])*2,\n",
    "            mu_hep[:, l, k] + np.sqrt(var_hep[:, l, k])*2, alpha=0.3)\n",
    "ax_hep[l%5].legend(line_leg, bbox_to_anchor=(2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_hep.savefig('../figs/hep_gene_clusts.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_CHOLESTEROL_HOMEOSTASIS</th>\n",
       "      <td>6.92618e-09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>4.92256e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1.17837e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
       "      <td>4.37992e-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COMPLEMENT</th>\n",
       "      <td>7.02289e-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>2.31674e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>1</td>\n",
       "      <td>6.21581e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_XENOBIOTIC_METABOLISM</th>\n",
       "      <td>4.90832e-10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_FATTY_ACID_METABOLISM</th>\n",
       "      <td>4.23928e-09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <td>8.51378e-14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_BILE_ACID_METABOLISM</th>\n",
       "      <td>2.19004e-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_PEROXISOME</th>\n",
       "      <td>0.000565521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   1                   4\n",
       "                                 bonferonni-adjusted bonferonni-adjusted\n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS         6.92618e-09                   1\n",
       "HALLMARK_MITOTIC_SPINDLE                           1         4.92256e-13\n",
       "HALLMARK_G2M_CHECKPOINT                            1         1.17837e-27\n",
       "HALLMARK_ADIPOGENESIS                    4.37992e-05                   1\n",
       "HALLMARK_COMPLEMENT                      7.02289e-06                   1\n",
       "HALLMARK_E2F_TARGETS                               1         2.31674e-33\n",
       "HALLMARK_MYC_TARGETS_V1                            1         6.21581e-10\n",
       "HALLMARK_XENOBIOTIC_METABOLISM           4.90832e-10                   1\n",
       "HALLMARK_FATTY_ACID_METABOLISM           4.23928e-09                   1\n",
       "HALLMARK_COAGULATION                     8.51378e-14                   1\n",
       "HALLMARK_BILE_ACID_METABOLISM            2.19004e-06                   1\n",
       "HALLMARK_PEROXISOME                      0.000565521                   1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/hep.dpt.batchind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/hep.dpt.hires.batchind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()    \n",
    "active_results_hep = view_gsea(m_hep, genedict)\n",
    "active_results_hep.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_results_hep.to_csv(\"../results/hep_gsea_batchind.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "random.seed(2021)\n",
    "n_runs = 10\n",
    "similarity_hep = np.zeros([C,C], dtype=int)\n",
    "for i in range(n_runs):\n",
    "    mi = run_splitGPM(X_ind_hep, Y_ind_hep, C, G, T, K1, K2, N)\n",
    "    for j in range(C):\n",
    "        for k in range(j):\n",
    "            if mi.W1.value.argmax(1)[j] == mi.W1.value.argmax(1)[k]:\n",
    "                similarity_hep[j,k] += 1\n",
    "                similarity_hep[k,j] += 1\n",
    "for j in range(C):\n",
    "    similarity_hep[j,j] = n_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = [g[6:].replace(\"Batch\", \"Replicate\") for g in list(linedict.values())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, '18511--Replicate1'),\n",
       " Text(1.5, 0, '18511--Replicate2'),\n",
       " Text(2.5, 0, '18511--Replicate3'),\n",
       " Text(3.5, 0, '18858--Replicate1'),\n",
       " Text(4.5, 0, '18858--Replicate2'),\n",
       " Text(5.5, 0, '18858--Replicate3'),\n",
       " Text(6.5, 0, '19160--Replicate1'),\n",
       " Text(7.5, 0, '19160--Replicate2'),\n",
       " Text(8.5, 0, '19160--Replicate3')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm_hep = sns.heatmap(similarity_hep, cmap=\"OrRd\",\n",
    "           xticklabels=groups,\n",
    "           yticklabels=groups)\n",
    "hm_hep.set_xticklabels(hm_hep.get_xticklabels(), \n",
    "                          rotation=45, \n",
    "                          horizontalalignment='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm_hep.figure.savefig('../figs/hep_cluster_stability.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../figs/hep_cluster_stability.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neuronal lineage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind = np.loadtxt(\"../data/neur.dpt.ind.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/neur.dpt.ind.Y.txt\")\n",
    "else:\n",
    "    X_ind = np.loadtxt(\"../data/neur.dpt.hires.ind.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/neur.dpt.hires.ind.Y.txt\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=3\n",
    "G=int(max(X_ind[:,2])+1)\n",
    "T=5\n",
    "K1=1\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "m = run_splitGPM(X_ind, Y_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind = np.linspace(0, 1, 100)[:, None]\n",
    "mu, var = m.predict_f(Xnew_ind)\n",
    "fig, ax = plt.subplots(1,5, figsize=(20,4))\n",
    "mu = mu.T.reshape((K1, K2, -1)).T\n",
    "var = var.T.reshape((K1, K2, -1)).T\n",
    "a1 = m.W1.value.argmax(1)[X_ind[:, 1].astype(int)]\n",
    "a2 = m.W2.value.argmax(1)[X_ind[:, 2].astype(int)]\n",
    "for k in range(K1):\n",
    "    for l in range(K2):\n",
    "        ax[l%5].plot(Xnew_ind, mu[:, l, k])\n",
    "        ax[l%5].set_title(\"Cluster %d\" % l)\n",
    "        ax[l%5].fill_between(\n",
    "            Xnew_ind.flatten(),\n",
    "            mu[:, l, k] - np.sqrt(var[:, l, k])*2,\n",
    "            mu[:, l, k] + np.sqrt(var[:, l, k])*2, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/neur.dpt.ind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/neur.dpt.hires.ind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>2.46875e-11</td>\n",
       "      <td>3.66335e-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ESTROGEN_RESPONSE_EARLY</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00131406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_INTERFERON_GAMMA_RESPONSE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0085254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000503455</td>\n",
       "      <td>0.402503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>2.18594e-11</td>\n",
       "      <td>3.69245e-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>8.22298e-05</td>\n",
       "      <td>0.0628844</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000256203</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     2                   3  \\\n",
       "                                   bonferonni-adjusted bonferonni-adjusted   \n",
       "HALLMARK_G2M_CHECKPOINT                    2.46875e-11         3.66335e-05   \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY                     1                   1   \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE                   1                   1   \n",
       "HALLMARK_MTORC1_SIGNALING                            1         0.000503455   \n",
       "HALLMARK_E2F_TARGETS                       2.18594e-11         3.69245e-06   \n",
       "HALLMARK_MYC_TARGETS_V1                    8.22298e-05           0.0628844   \n",
       "HALLMARK_MYC_TARGETS_V2                              1         0.000256203   \n",
       "\n",
       "                                                     4  \n",
       "                                   bonferonni-adjusted  \n",
       "HALLMARK_G2M_CHECKPOINT                              1  \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY            0.00131406  \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE           0.0085254  \n",
       "HALLMARK_MTORC1_SIGNALING                     0.402503  \n",
       "HALLMARK_E2F_TARGETS                                 1  \n",
       "HALLMARK_MYC_TARGETS_V1                              1  \n",
       "HALLMARK_MYC_TARGETS_V2                              1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results = view_gsea(m, genedict)\n",
    "active_results.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind = np.loadtxt(\"../data/neur.dpt.batch.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/neur.dpt.batch.Y.txt\")\n",
    "else:\n",
    "    X_ind = np.loadtxt(\"../data/neur.dpt.hires.batch.X.txt\")\n",
    "    Y_ind = np.loadtxt(\"../data/neur.dpt.hires.batch.Y.txt\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=3\n",
    "G=int(max(X_ind[:,2])+1)\n",
    "T=5\n",
    "K1=1\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 5), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "m = run_splitGPM(X_ind, Y_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind = np.linspace(0, 1, 100)[:, None]\n",
    "mu, var = m.predict_f(Xnew_ind)\n",
    "fig, ax = plt.subplots(1,5, figsize=(20,4))\n",
    "mu = mu.T.reshape((K1, K2, -1)).T\n",
    "var = var.T.reshape((K1, K2, -1)).T\n",
    "a1 = m.W1.value.argmax(1)[X_ind[:, 1].astype(int)]\n",
    "a2 = m.W2.value.argmax(1)[X_ind[:, 2].astype(int)]\n",
    "for k in range(K1):\n",
    "    for l in range(K2):\n",
    "        ax[l%5].plot(Xnew_ind, mu[:, l, k])\n",
    "        ax[l%5].set_title(\"Cluster %d\" % l)\n",
    "        ax[l%5].fill_between(\n",
    "            Xnew_ind.flatten(),\n",
    "            mu[:, l, k] - np.sqrt(var[:, l, k])*2,\n",
    "            mu[:, l, k] + np.sqrt(var[:, l, k])*2, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/neur.dpt.batch.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/neur.dpt.hires.batch.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00042146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>5.19507e-14</td>\n",
       "      <td>0.0112541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ESTROGEN_RESPONSE_EARLY</th>\n",
       "      <td>0.0004536</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_INTERFERON_GAMMA_RESPONSE</th>\n",
       "      <td>0.00321537</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
       "      <td>0.186078</td>\n",
       "      <td>9.515e-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>2.53371e-14</td>\n",
       "      <td>0.00499829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00375471</td>\n",
       "      <td>0.0791595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000502374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_OXIDATIVE_PHOSPHORYLATION</th>\n",
       "      <td>0.00107669</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     0                   2  \\\n",
       "                                   bonferonni-adjusted bonferonni-adjusted   \n",
       "HALLMARK_MITOTIC_SPINDLE                             1          0.00042146   \n",
       "HALLMARK_G2M_CHECKPOINT                              1         5.19507e-14   \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY             0.0004536                   1   \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE          0.00321537                   1   \n",
       "HALLMARK_MTORC1_SIGNALING                     0.186078           9.515e-05   \n",
       "HALLMARK_E2F_TARGETS                                 1         2.53371e-14   \n",
       "HALLMARK_MYC_TARGETS_V1                              1          0.00375471   \n",
       "HALLMARK_MYC_TARGETS_V2                              1         0.000502374   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION          0.00107669                   1   \n",
       "\n",
       "                                                     3  \n",
       "                                   bonferonni-adjusted  \n",
       "HALLMARK_MITOTIC_SPINDLE                             1  \n",
       "HALLMARK_G2M_CHECKPOINT                      0.0112541  \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY                     1  \n",
       "HALLMARK_INTERFERON_GAMMA_RESPONSE                   1  \n",
       "HALLMARK_MTORC1_SIGNALING                            1  \n",
       "HALLMARK_E2F_TARGETS                        0.00499829  \n",
       "HALLMARK_MYC_TARGETS_V1                      0.0791595  \n",
       "HALLMARK_MYC_TARGETS_V2                              1  \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                   1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_results = view_gsea(m, genedict)\n",
    "active_results.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified by batch and individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not hi_res:\n",
    "    X_ind_neur = np.loadtxt(\"../data/neur.dpt.batchind.X.txt\")\n",
    "    Y_ind_neur = np.loadtxt(\"../data/neur.dpt.batchind.Y.txt\")\n",
    "else:\n",
    "    X_ind_neur = np.loadtxt(\"../data/neur.dpt.hires.batchind.X.txt\")\n",
    "    Y_ind_neur = np.loadtxt(\"../data/neur.dpt.hires.batchind.Y.txt\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=9\n",
    "G=int(max(X_ind_neur[:,2])+1)\n",
    "T=5\n",
    "K1=3\n",
    "K2=5\n",
    "N=C*T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "random.seed(2021)\n",
    "m_neur = run_splitGPM(X_ind_neur, Y_ind_neur, C, G, T, K1, K2, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>cell line/batch</th>\n",
       "      <th>assignment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SNG-NA18511--Batch1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SNG-NA18511--Batch2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SNG-NA18511--Batch3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SNG-NA18858--Batch1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SNG-NA18858--Batch2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SNG-NA18858--Batch3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SNG-NA19160--Batch1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SNG-NA19160--Batch2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SNG-NA19160--Batch3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cell line/batch  assignment\n",
       "0  SNG-NA18511--Batch1           2\n",
       "1  SNG-NA18511--Batch2           2\n",
       "2  SNG-NA18511--Batch3           2\n",
       "3  SNG-NA18858--Batch1           1\n",
       "4  SNG-NA18858--Batch2           2\n",
       "5  SNG-NA18858--Batch3           1\n",
       "6  SNG-NA19160--Batch1           2\n",
       "7  SNG-NA19160--Batch2           2\n",
       "8  SNG-NA19160--Batch3           2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/neur.dpt.batchind.linedict.pickle\", 'rb') as f:\n",
    "        linedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/neur.dpt.hires.batchind.linedict.pickle\", 'rb') as f:\n",
    "        linedict = pickle.load(f)\n",
    "        f.close()\n",
    "cell_clusts_neur = pd.DataFrame(data=linedict.values(), columns=[\"cell line/batch\"])\n",
    "cell_clusts_neur['assignment'] = m_neur.W1.value.argmax(1)\n",
    "cell_clusts_neur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b34df0df898>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew_ind_neur = np.linspace(0, 1, 100)[:, None]\n",
    "mu_neur, var_neur = m_neur.predict_f(Xnew_ind_neur)\n",
    "fig_neur, ax_neur = plt.subplots(1,5, figsize=(20,4))\n",
    "mu_neur = mu_neur.T.reshape((K1, K2, -1)).T\n",
    "var_neur = var_neur.T.reshape((K1, K2, -1)).T\n",
    "a1_neur = m_neur.W1.value.argmax(1)[X_ind_neur[:, 1].astype(int)]\n",
    "a2_neur = m_neur.W2.value.argmax(1)[X_ind_neur[:, 2].astype(int)]\n",
    "line_leg = []\n",
    "for k in range(K1):\n",
    "    if sum(a1_neur == k) == 0:\n",
    "        continue  # no need to plot empty modules\n",
    "    else:\n",
    "        line_leg.append(\"line/replicate cluster \" + str(k))\n",
    "    for l in range(K2):\n",
    "        ax_neur[l%5].plot(Xnew_ind_neur, mu_neur[:, l, k])\n",
    "        ax_neur[l%5].set_title(\"Gene Cluster %d\" % l)\n",
    "        ax_neur[l%5].fill_between(\n",
    "            Xnew_ind_neur.flatten(),\n",
    "            mu_neur[:, l, k] - np.sqrt(var_neur[:, l, k])*2,\n",
    "            mu_neur[:, l, k] + np.sqrt(var_neur[:, l, k])*2, alpha=0.3)\n",
    "ax_neur[l%5].legend(line_leg, bbox_to_anchor=(2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_neur.savefig('../figs/neur_gene_clusts.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "      <th>bonferonni-adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
       "      <td>1</td>\n",
       "      <td>2.17277e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_G2M_CHECKPOINT</th>\n",
       "      <td>1</td>\n",
       "      <td>1.48348e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ESTROGEN_RESPONSE_EARLY</th>\n",
       "      <td>0.000267846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
       "      <td>6.99e-08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_E2F_TARGETS</th>\n",
       "      <td>1</td>\n",
       "      <td>3.49535e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
       "      <td>0.192287</td>\n",
       "      <td>9.34926e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_MYC_TARGETS_V2</th>\n",
       "      <td>5.43658e-07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_OXIDATIVE_PHOSPHORYLATION</th>\n",
       "      <td>0.0026008</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY</th>\n",
       "      <td>0.000274695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           3  \\\n",
       "                                         bonferonni-adjusted   \n",
       "HALLMARK_MITOTIC_SPINDLE                                   1   \n",
       "HALLMARK_G2M_CHECKPOINT                                    1   \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY                 0.000267846   \n",
       "HALLMARK_MTORC1_SIGNALING                           6.99e-08   \n",
       "HALLMARK_E2F_TARGETS                                       1   \n",
       "HALLMARK_MYC_TARGETS_V1                             0.192287   \n",
       "HALLMARK_MYC_TARGETS_V2                          5.43658e-07   \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                 0.0026008   \n",
       "HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY         0.000274695   \n",
       "\n",
       "                                                           4  \n",
       "                                         bonferonni-adjusted  \n",
       "HALLMARK_MITOTIC_SPINDLE                         2.17277e-06  \n",
       "HALLMARK_G2M_CHECKPOINT                          1.48348e-22  \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY                           1  \n",
       "HALLMARK_MTORC1_SIGNALING                                  1  \n",
       "HALLMARK_E2F_TARGETS                             3.49535e-25  \n",
       "HALLMARK_MYC_TARGETS_V1                          9.34926e-06  \n",
       "HALLMARK_MYC_TARGETS_V2                                    1  \n",
       "HALLMARK_OXIDATIVE_PHOSPHORYLATION                         1  \n",
       "HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY                   1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not hi_res:\n",
    "    with open(\"../data/neur.dpt.batchind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "else:\n",
    "    with open(\"../data/neur.dpt.hires.batchind.genedict.pickle\", 'rb') as f:\n",
    "        genedict = pickle.load(f)\n",
    "        f.close()\n",
    "active_results_neur = view_gsea(m_neur, genedict)\n",
    "active_results_neur.loc[:, pd.IndexSlice[:, 'bonferonni-adjusted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_results_neur.to_csv(\"../results/neur_gsea_batchind.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n",
      "Tensor(\"test/add_6:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/conditional/base_conditional/transpose:0\", shape=(?, 15), dtype=float64)\n",
      "Tensor(\"test/Reshape_2:0\", shape=(?, 1), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "random.seed(2021)\n",
    "n_runs = 10\n",
    "similarity_neur = np.zeros([C,C], dtype=int)\n",
    "for i in range(n_runs):\n",
    "    mi = run_splitGPM(X_ind_neur, Y_ind_neur, C, G, T, K1, K2, N)\n",
    "    for j in range(C):\n",
    "        for k in range(j):\n",
    "            if mi.W1.value.argmax(1)[j] == mi.W1.value.argmax(1)[k]:\n",
    "                similarity_neur[j,k] += 1\n",
    "                similarity_neur[k,j] += 1\n",
    "for j in range(C):\n",
    "    similarity_neur[j,j] = n_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = [g[6:].replace(\"Batch\", \"Replicate\") for g in list(linedict.values())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, '18511--Replicate1'),\n",
       " Text(1.5, 0, '18511--Replicate2'),\n",
       " Text(2.5, 0, '18511--Replicate3'),\n",
       " Text(3.5, 0, '18858--Replicate1'),\n",
       " Text(4.5, 0, '18858--Replicate2'),\n",
       " Text(5.5, 0, '18858--Replicate3'),\n",
       " Text(6.5, 0, '19160--Replicate1'),\n",
       " Text(7.5, 0, '19160--Replicate2'),\n",
       " Text(8.5, 0, '19160--Replicate3')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm_neur = sns.heatmap(similarity_neur, cmap=\"OrRd\",\n",
    "           xticklabels=groups,\n",
    "           yticklabels=groups)\n",
    "hm_neur.set_xticklabels(hm_neur.get_xticklabels(), \n",
    "                          rotation=45, \n",
    "                          horizontalalignment='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm_neur.figure.savefig('../figs/neur_cluster_stability.png', bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:myenv]",
   "language": "python",
   "name": "conda-env-myenv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}