scVelo_computation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running scvelo 0.2.3 (python 3.8.5) on 2021-06-01 12:03.\n"
     ]
    }
   ],
   "source": [
    "import scvelo as scv\n",
    "scv.logging.print_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "scv.settings.verbosity = 3  # show errors(0), warnings(1), info(2), hints(3)\n",
    "scv.settings.presenter_view = True  # set max width size for presenter view\n",
    "scv.set_figure_params('scvelo')  # for beautified visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    }
   ],
   "source": [
    "data = scv.read(\"./rgl_neuroblast_glioblast.loom\", cache=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.var_names_make_unique()\n",
    "data = data[(data.obs.Clusters < 321)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Radial glia cells : keep only 3000 of them, take from e9.0 to e13.5\n",
    "radial = data[data.obs.Class == \"Radial glia\"]\n",
    "radial9 = radial[radial.obs['Age'] == 'e9.0']\n",
    "radial9 = radial9[1:500]\n",
    "radial10 = radial[radial.obs['Age'] == 'e10.0']\n",
    "radial10 = radial10[1:500]\n",
    "radial11 = radial[radial.obs['Age'] == 'e11.0']\n",
    "radial11 = radial11[1:500]\n",
    "radial12 = radial[radial.obs['Age'] == 'e12.0']\n",
    "radial12 = radial12[1:500]\n",
    "radial13 = radial[radial.obs['Age'] == 'e13.0']\n",
    "radial13 = radial13[1:500]\n",
    "radial135 = radial[radial.obs['Age'] == 'e13.5']\n",
    "radial135 = radial135[1:500]\n",
    "\n",
    "radial = radial9.concatenate(radial10, radial11, radial12, radial13, radial135, join = 'outer')\n",
    "\n",
    "\n",
    "#Early neural tube cells : take them all\n",
    "early_neural_tube = data[data.obs.Class == \"Early Neural Tube\"]\n",
    "\n",
    "#OPCs : take them all\n",
    "opc_s = data[data.obs.Class == \"OPCs\"]\n",
    "\n",
    "#Glia cells : take approx 3000 of them, from e14.0 to e18.0\n",
    "glia = data[data.obs.Class == \"Glia\"]\n",
    "glia_14 = glia[glia.obs['Age']  == 'e14.0']\n",
    "glia_135 = glia[glia.obs['Age']  == 'e13.5']\n",
    "glia_15 = glia[glia.obs['Age']  == 'e15']\n",
    "glia_155 = glia[glia.obs['Age']  == 'e15.5']\n",
    "glia_16 = glia[glia.obs['Age']  == 'e16.0']\n",
    "glia_16 = glia_16[1:500]\n",
    "glia_165 = glia[glia.obs['Age']  == 'e16.5']\n",
    "glia_165 = glia_165[1:500]\n",
    "glia_17 = glia[glia.obs['Age']  == 'e17.0']\n",
    "glia_17 = glia_17[1:500]\n",
    "glia_18 = glia[glia.obs['Age']  == 'e18.0']\n",
    "glia_18 = glia_18[1:500]\n",
    "glia = glia_14.concatenate(glia_135, glia_15, glia_155, glia_16, glia_165, glia_17, glia_18, join='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = radial.concatenate(early_neural_tube, opc_s, glia, join='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 196,
       "width": 172
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scv.pl.proportions(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocess the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered out 2918 genes that are detected 20 counts (shared).\n",
      "Normalized count data: X, spliced, unspliced.\n",
      "Extracted 2000 highly variable genes.\n",
      "Logarithmized X.\n",
      "computing neighbors\n",
      "    finished (0:00:26) --> added \n",
      "    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)\n",
      "computing moments based on connectivities\n",
      "    finished (0:00:02) --> added \n",
      "    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)\n"
     ]
    }
   ],
   "source": [
    "scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)\n",
    "scv.pp.moments(adata, n_pcs=30, n_neighbors=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dynamical model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recovering dynamics (using 1/4 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67d5e12979d64af1b454039514a866ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1113.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    finished (0:16:19) --> added \n",
      "    'fit_pars', fitted parameters for splicing dynamics (adata.var)\n"
     ]
    }
   ],
   "source": [
    "scv.tl.recover_dynamics(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing velocities\n",
      "    finished (0:00:13) --> added \n",
      "    'velocity', velocity vectors for each individual cell (adata.layers)\n",
      "computing velocity graph\n",
      "    finished (0:00:09) --> added \n",
      "    'velocity_graph', sparse matrix with cosine correlations (adata.uns)\n"
     ]
    }
   ],
   "source": [
    "scv.tl.velocity(adata, mode='dynamical')\n",
    "scv.tl.velocity_graph(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write('../scvelo_final1.h5ad', compression='gzip')"
   ]
  }
 ],
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