10x_collect_info.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py:39: RuntimeWarning: Error importing cairo. Graph drawing will not work.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py:39: RuntimeWarning: Error importing cairo. Graph drawing will not work.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py:39: RuntimeWarning: Error importing cairo. Graph drawing will not work.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "/usr/lib/python3/dist-packages/graph_tool/all.py:40: RuntimeWarning: Error importing draw module, proceeding nevertheless: /usr/lib/python3/dist-packages/cairo/_cairo.cpython-37m-x86_64-linux-gnu.so: undefined symbol: cairo_tee_surface_index\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import numpy as np\n",
    "import graph_tool.all as gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_state = pickle.load( open( \"adata_10x/10x_nested_state.pkl\", \"rb\" ) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = pickle.load( open( \"adata_10x/10x_graph_g.pkl\", \"rb\" )) #ho gia il grafo caricato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 860 × 1596 \n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'leiden'\n",
       "    var: 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'leiden', 'leiden_colors', 'neighbors', 'pca'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    varm: 'PCs'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata=sc.read('adata_10x/adata_sc_10x.h5ad')\n",
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.rank_genes_groups(adata, 'leiden', method='t-test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_significativo= pd.DataFrame(adata.uns['rank_genes_groups']['names']).head(20) #### df con i 20 geni significativi per gruppo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_annotation= pd.read_table('../sc_mixology/data/csv/sc_10x_5cl.metadata.csv.gz', compression=\"gzip\", sep=\",\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase',\n",
       "       'leiden', 'cell_line'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, \"cell_line\"] = cell_annotation.loc[adata.obs_names, \"cell_line\"]\n",
    "adata.obs.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) dopo mcmc_sweeps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a) state con 5 livelli(corretto)\n",
    "##### a1) primo tentativo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35.94233505310973, 951410, 297584)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = n_state.get_bs()                   \n",
    "bs += [np.zeros(1)] * (10 - len(bs))\n",
    "\n",
    "state = n_state.copy(bs=bs, sampling=True)\n",
    "state.mcmc_sweep(niter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 64 blocks (63 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f645d99b250>, at 0x7f645c13f150>, and 10 levels of sizes [(860, 63), (63, 16), (16, 5), (5, 3), (3, 2), (2, 2), (2, 1), (1, 1), (1, 1), (1, 1)] at 0x7f645c13f0d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(state, open('adata_10x/10x_collect_info/10x_state_mcmc5.pkl', 'wb'), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "state5=pickle.load(open('adata_10x/10x_collect_info/10x_state_mcmc5.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "0             0     0      0    295       0\n",
       "1             0     0    127      0       0\n",
       "2             0   176      7      0       0\n",
       "3             1     0      4      1     188\n",
       "4            61     0      0      0       0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state5.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a2) secondo tentativo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.37174843840085, 950989, 296597)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = n_state.get_bs()                   \n",
    "bs += [np.zeros(1)] * (10 - len(bs))\n",
    "\n",
    "state = n_state.copy(bs=bs, sampling=True)\n",
    "state.mcmc_sweep(niter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 64 blocks (63 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f645d99b250>, at 0x7f6455356110>, and 10 levels of sizes [(860, 63), (63, 15), (15, 5), (5, 2), (2, 2), (2, 2), (2, 1), (1, 1), (1, 1), (1, 1)] at 0x7f6455356050>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "1             0     0    127      0       0\n",
       "2             0   176      7      0       0\n",
       "3             0     0      0    295       0\n",
       "4             1     0      4      1     188\n",
       "5            61     0      0      0       0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b) state con 4 livelli(non corretto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 57 blocks (57 nonempty), for graph <Graph object, undirected, with 860 vertices and 5976 edges at 0x7f646770c790>, at 0x7f6455517190>, and 5 levels of sizes [(860, 57), (57, 13), (13, 4), (4, 2), (2, 1)] at 0x7f64554adb50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = gt.minimize_nested_blockmodel_dl(g, deg_corr=False)\n",
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.63567502509273, 949963, 295781)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = n_state.get_bs()                   \n",
    "bs += [np.zeros(1)] * (10 - len(bs))\n",
    "\n",
    "state = n_state.copy(bs=bs, sampling=True)\n",
    "state.mcmc_sweep(niter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(state, open('adata_10x/10x_collect_info/10x_state_mcmc4.pkl', 'wb'), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "state4=pickle.load(open('adata_10x/10x_collect_info/10x_state_mcmc4.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 63 blocks (63 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f2e43edd750>, at 0x7f2e5e7d6850>, and 10 levels of sizes [(860, 63), (63, 17), (17, 4), (4, 3), (3, 2), (2, 2), (2, 1), (1, 1), (1, 1), (1, 1)] at 0x7f2e43edd250>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'cell_line' as categorical\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>176</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "0             1     0      4      1     188\n",
       "1             0     0    127      0       0\n",
       "2            61   176      7      0       0\n",
       "4             0     0      0    295       0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state4.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) collect info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a) collect info state5 (force niter 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = [np.zeros(g.num_vertices() + 1) for s in state4.get_levels()]\n",
    "pv = [None] * len(state4.get_levels())\n",
    "\n",
    "\n",
    "def collect_info(s):\n",
    "    for l,sl in enumerate(s.get_levels()):\n",
    "        B=sl.get_nonempty_B()\n",
    "        h[l][B] +=1\n",
    "        global pv\n",
    "        pv=[sl.collect_vertex_marginals(pv[l]) for l, sl in enumerate(s.get_levels())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19570.282142472726, 96497339, 30591822)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt.mcmc_equilibrate(state5, force_niter=10000, mcmc_args=dict(niter=10),\n",
    "                    callback=collect_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "0             0   176      7      0       0\n",
       "5             0     0      0    295       0\n",
       "6            61     0     71      0       0\n",
       "8             1     0     60      1     188"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state5.project_level(3).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(state5, open('adata_10x/10x_collect_info/10x_equilibrate_state_mcmc5.pkl', 'wb'), 2)\n",
    "pickle.dump(h, open('adata_10x/10x_collect_info/10x_h_state_mcmc5.pkl', 'wb'), 2)\n",
    "pickle.dump(pv, open('adata_10x/10x_collect_info/10x_pv_state_mcmc5.pkl', 'wb'), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "estate5=pickle.load(open('adata_10x/10x_collect_info/10x_equilibrate_state_mcmc5.pkl', 'rb'))\n",
    "h=pickle.load(open('adata_10x/10x_collect_info/10x_h_state_mcmc5.pkl', 'rb'))\n",
    "pv=pickle.load(open('adata_10x/10x_collect_info/10x_pv_state_mcmc5.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 63 blocks (63 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f2e5cd20790>, at 0x7f2e5cd21ad0>, and 10 levels of sizes [(860, 63), (63, 16), (16, 5), (5, 3), (3, 2), (2, 2), (2, 1), (1, 1), (1, 1), (1, 1)] at 0x7f2e5cd20750>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 67 blocks (66 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f2e5dc59ad0>, at 0x7f2e5cf86190>, and 10 levels of sizes [(860, 66), (66, 16), (16, 7), (7, 4), (4, 4), (4, 3), (3, 3), (3, 2), (2, 2), (2, 2)] at 0x7f2e5dc594d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estate5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b) collect info state 4: mcmc niter 10000, ma d=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 63 blocks (63 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f2e43edd750>, at 0x7f2e5e7d6850>, and 10 levels of sizes [(860, 63), (63, 17), (17, 4), (4, 3), (3, 2), (2, 2), (2, 1), (1, 1), (1, 1), (1, 1)] at 0x7f2e43edd250>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NestedBlockState object, with base <BlockState object with 65 blocks (63 nonempty), for graph <Graph object, undirected, with 860 vertices and 5996 edges at 0x7f2e43edd750>, at 0x7f2e5cd26a10>, and 10 levels of sizes [(860, 63), (63, 16), (16, 5), (5, 2), (2, 2), (2, 2), (2, 2), (2, 1), (1, 1), (1, 1)] at 0x7f2e5cd26990>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Received orphaned property map",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-9103d6680182>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mbs\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mstate_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mgt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmcmc_equilibrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_test\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mmcmc_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mniter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollect_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/inference/mcmc.py\u001b[0m in \u001b[0;36mmcmc_equilibrate\u001b[0;34m(state, wait, nbreaks, max_niter, force_niter, epsilon, gibbs, multiflip, mcmc_args, entropy_args, history, callback, verbose)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m             \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-17-6ead409417ea>\u001b[0m in \u001b[0;36mcollect_info\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mh\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mglobal\u001b[0m \u001b[0mpv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mpv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_vertex_marginals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-17-6ead409417ea>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mh\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mglobal\u001b[0m \u001b[0mpv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mpv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_vertex_marginals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/inference/blockmodel.py\u001b[0m in \u001b[0;36mcollect_vertex_marginals\u001b[0;34m(self, p, b, unlabel, update)\u001b[0m\n\u001b[1;32m   2165\u001b[0m         libinference.vertex_marginals(self.g._Graph__graph,\n\u001b[1;32m   2166\u001b[0m                                       \u001b[0m_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2167\u001b[0;31m                                       \u001b[0m_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2168\u001b[0m                                       update)\n\u001b[1;32m   2169\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/__init__.py\u001b[0m in \u001b[0;36m_prop\u001b[0;34m(t, g, prop)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Received orphaned property map\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         raise ValueError(\"Received property map for graph %s (base: %s), expected: %s (base: %s)\" %\n",
      "\u001b[0;31mValueError\u001b[0m: Received orphaned property map"
     ]
    }
   ],
   "source": [
    "bs = state4.get_bs()\n",
    "bs += [np.zeros(1)] * (10 - len(bs))\n",
    "state_test = state4.copy(bs=bs)\n",
    "gt.mcmc_equilibrate(state_test,  mcmc_args=dict(niter=10000, d=0.1), callback=collect_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state_test.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(state_test, open('adata_10x/10x_collect_info/10x_equilibrate_state_mcmc4.pkl', 'wb'), 2)\n",
    "pickle.dump(h, open('adata_10x/10x_collect_info/10x_h_state_mcmc4.pkl', 'wb'), 2)\n",
    "pickle.dump(pv, open('adata_10x/10x_collect_info/10x_pv_state_mcmc4.pkl', 'wb'), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estate4=pickle.load(open('adata_10x/10x_collect_info/10x_equilibrate_state_mcmc4.pkl', 'rb'))\n",
    "h=pickle.load(open('adata_10x/10x_collect_info/10x_h_state_mcmc4.pkl', 'rb'))\n",
    "pv=pickle.load(open('adata_10x/10x_collect_info/10x_pv_state_mcmc4.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c) collect info state 4: force niter 10000, mcmc niter 1, d=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Received orphaned property map",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-881c6187cc76>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mstate_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mgt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmcmc_equilibrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_niter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmcmc_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mniter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollect_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/inference/mcmc.py\u001b[0m in \u001b[0;36mmcmc_equilibrate\u001b[0;34m(state, wait, nbreaks, max_niter, force_niter, epsilon, gibbs, multiflip, mcmc_args, entropy_args, history, callback, verbose)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m             \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-17-6ead409417ea>\u001b[0m in \u001b[0;36mcollect_info\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mh\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mglobal\u001b[0m \u001b[0mpv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mpv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_vertex_marginals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-17-6ead409417ea>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mh\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mglobal\u001b[0m \u001b[0mpv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mpv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_vertex_marginals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/inference/blockmodel.py\u001b[0m in \u001b[0;36mcollect_vertex_marginals\u001b[0;34m(self, p, b, unlabel, update)\u001b[0m\n\u001b[1;32m   2165\u001b[0m         libinference.vertex_marginals(self.g._Graph__graph,\n\u001b[1;32m   2166\u001b[0m                                       \u001b[0m_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2167\u001b[0;31m                                       \u001b[0m_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2168\u001b[0m                                       update)\n\u001b[1;32m   2169\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/__init__.py\u001b[0m in \u001b[0;36m_prop\u001b[0;34m(t, g, prop)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Received orphaned property map\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         raise ValueError(\"Received property map for graph %s (base: %s), expected: %s (base: %s)\" %\n",
      "\u001b[0;31mValueError\u001b[0m: Received orphaned property map"
     ]
    }
   ],
   "source": [
    "bs = state4.get_bs()\n",
    "\n",
    "state_test = state4.copy(bs=bs)\n",
    "gt.mcmc_equilibrate(state_test, force_niter=10000, mcmc_args=dict(niter=1, d=0.1), callback=collect_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) marginals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "marginals=[]\n",
    "ml = []\n",
    "for i in range(860):\n",
    "    lista=[]\n",
    "    ml.append(len(pv[3][i]))\n",
    "    for j in pv[3][i]:\n",
    "        if j!=0:\n",
    "            lista.append(j)\n",
    "    marginals.append(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "860"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " [12750, 11330, 12310, 11630, 10110, 11940, 11700, 8880, 9340],\n",
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       " [15090, 10270, 11480, 10150, 9980, 11610, 11740, 7990, 11340],\n",
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       " [],\n",
       " []]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marginals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1.collect_vertex_marginals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pv[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'vertices'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-0453e5a488d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-91-0453e5a488d0>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/graph_tool/__init__.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    868\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    871\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'vertices'"
     ]
    }
   ],
   "source": [
    "[x for x in p]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.470983280579212, 7460162, 103000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = n_state.get_bs()                   \n",
    "bs += [np.zeros(1)] * (10 - len(bs))\n",
    "\n",
    "state = n_state.copy(bs=bs, sampling=True)\n",
    "state.multiflip_mcmc_sweep(niter=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'cell_line' as categorical\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>295</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "0            61     0     71      0       0\n",
       "3             1     0      4      1     188\n",
       "5             0     0     56      0       0\n",
       "6             0     0      0    295       0\n",
       "8             0   176      7      0       0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(state, open('adata_10x/10x_collect_info/10x_state_mcmc4.pkl', 'wb'), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "state=pickle.load(open('adata_10x/10x_collect_info/10x_state_mcmc4.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19584.684766180744, 36019864, 643081)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt.mcmc_equilibrate(state, epsilon=0.001,multiflip=True, mcmc_args=dict(niter=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = [np.zeros(g.num_vertices() + 1) for s in state.get_levels()]\n",
    "\n",
    "def collect_info(s):\n",
    "    for l,sl in enumerate(s.get_levels()):\n",
    "        B=sl.get_nonempty_B()\n",
    "        h[l][B] +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19541.89243874788, 97382190, 28534977)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt.mcmc_equilibrate(state,force_niter=10000,epsilon=0.001, mcmc_args=dict(niter=10),callback=collect_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in range (0,len(bs)):\n",
    "    idxs= np.where(h[l] > 0)[0]\n",
    "    v = h[l][idxs]\n",
    "    figure(l)\n",
    "    bar(idxs, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
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       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>127</td>\n",
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       "      <td>5</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "1             0   176      7      0       0\n",
       "2             0     0      3      0     153\n",
       "3             0     0    127      0       0\n",
       "5             0     0      0    205       0\n",
       "9            61     0      0      0       0\n",
       "10            1     0      1      1      35\n",
       "12            0     0      0     90       0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(state, open('adata_10x/10x_collect_info/10x_equilibrate_state_mcmc4.pkl', 'wb'), 2)\n",
    "pickle.dump(h, open('adata_10x/10x_collect_info/10x_h_state_mcmc4.pkl', 'wb'), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "estate=pickle.load(open('adata_10x/10x_collect_info/10x_equilibrate_state_mcmc4.pkl', 'rb'))\n",
    "h=pickle.load(open('adata_10x/10x_collect_info/10x_h_state_mcmc4.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "state=pickle.load(open('adata_10x/10x_collect_info/10x_state_mcmc4.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = [np.zeros(g.num_vertices() + 1) for s in state.get_levels()]\n",
    "\n",
    "def collect_info(s):\n",
    "    for l,sl in enumerate(s.get_levels()):\n",
    "        B=sl.get_nonempty_B()\n",
    "        h[l][B] +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19586.591997283074, 45158612, 792395)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt.mcmc_equilibrate(state, epsilon=0.001,multiflip=True, mcmc_args=dict(niter=10),callback=collect_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in range (0,len(bs)):\n",
    "    idxs= np.where(h[l] > 0)[0]\n",
    "    v = h[l][idxs]\n",
    "    figure(l)\n",
    "    bar(idxs, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>cell_line</th>\n",
       "      <th>A549</th>\n",
       "      <th>H838</th>\n",
       "      <th>H1975</th>\n",
       "      <th>H2228</th>\n",
       "      <th>HCC827</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBM_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_line  A549  H838  H1975  H2228  HCC827\n",
       "NSBM_test                                  \n",
       "0             0     0    127      0       0\n",
       "2             0     0      0    156       0\n",
       "3             1     0      4      1     188\n",
       "4             0   176      7      0       0\n",
       "6             0     0      0    139       0\n",
       "10           61     0      0      0       0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.loc[:, 'NSBM_test'] = pd.Categorical(list(state.project_level(2).get_blocks()))\n",
    "sc.pl.umap(adata, color=[\"NSBM_test\", 'cell_line'], legend_loc='on data')\n",
    "pd.crosstab(adata.obs.NSBM_test, adata.obs.cell_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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