swan_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b01e95e5-7c83-4920-b4b5-953ddaa613c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import swan_vis as swan\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2809dd91-6543-405f-890c-70caab6db703",
   "metadata": {},
   "source": [
    "## Load SwanGraph into Python\n",
    "We have a SwanGraph object constructed from C2C12 myoblasts and 3-day differentiated myotubes. Bulk long-read RNA-seq was performed using polyA primers and PacBio sequencing. To quantify transcript expression, reads were mapped to the mouse genome using Minimap2, then the reads were cleaned and processed with TranscriptClean and [TALON](https://github.com/mortazavilab/TALON). \n",
    "\n",
    "In short, BAM files were downloaded from [this ENCODE data cart](https://www.encodeproject.org/carts/c2c12_bulk_pb/) and pre-processed with the walkthrough described [here](https://freese.gitbook.io/swan/tutorials/data_processing).\n",
    "\n",
    "We will use [Swan](https://freese.gitbook.io/swan/) to analyze and visualize the C2C12 long-read transcriptome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9d8d5f9-7829-4f9a-9705-4ce202f09640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read in graph from ../swan_data/swan.p\n"
     ]
    }
   ],
   "source": [
    "# load up a saved SwanGraph from a pickle file\n",
    "sg = swan.read('../swan_data/swan.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebee6339-8cfe-4192-9690-0c8e8a758a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"sg\" is an object with class and object attributes\n",
    "# to view the metadata (pandas dataframe, like an R dataframe) associated with the swangraph, check the adata.obs attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7b33e60-3c8e-4c4c-bf5e-67eacda6e6d5",
   "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>dataset</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>replicate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENCFF202MCY</th>\n",
       "      <td>ENCFF202MCY</td>\n",
       "      <td>72hr</td>\n",
       "      <td>R1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENCFF396UFT</th>\n",
       "      <td>ENCFF396UFT</td>\n",
       "      <td>72hr</td>\n",
       "      <td>R2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENCFF408MUF</th>\n",
       "      <td>ENCFF408MUF</td>\n",
       "      <td>0hr</td>\n",
       "      <td>R2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENCFF652XIT</th>\n",
       "      <td>ENCFF652XIT</td>\n",
       "      <td>0hr</td>\n",
       "      <td>R1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dataset timepoint replicate\n",
       "index                                       \n",
       "ENCFF202MCY  ENCFF202MCY      72hr        R1\n",
       "ENCFF396UFT  ENCFF396UFT      72hr        R2\n",
       "ENCFF408MUF  ENCFF408MUF       0hr        R2\n",
       "ENCFF652XIT  ENCFF652XIT       0hr        R1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd3cda28-6aff-477f-8cbb-2a294470bc1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sg.adata.obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fedae297-c090-486e-be89-f81eda15db8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "swan_vis.swangraph.SwanGraph"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the sg object itself is its own special type\n",
    "type(sg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ed13f3-a4f5-4d12-8f5a-fa9f5082a910",
   "metadata": {},
   "source": [
    "## Visualize transcript models for a gene of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a7ae584-56aa-410f-b53b-dc4a04a21dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x201.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a gene summary graph for the given gene\n",
    "sg.plot_graph(\"Mef2c\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a539f30e-d03f-49b5-8f9b-114c55f1252d",
   "metadata": {},
   "source": [
    "# Differential gene expression and differential transcript expression\n",
    "## Questions\n",
    "1. What is the difference between a gene and a transcript?\n",
    "2. What eukaryotic process produces different transcript isoforms?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "35735583-c3ba-4cf7-8a08-c4ff26ac6acf",
   "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>gid</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>log2fc</th>\n",
       "      <th>mean</th>\n",
       "      <th>zero_mean</th>\n",
       "      <th>grad</th>\n",
       "      <th>coef_mle</th>\n",
       "      <th>coef_sd</th>\n",
       "      <th>ll</th>\n",
       "      <th>gname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19364</th>\n",
       "      <td>ENSMUSG00000068327.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>0.546721</td>\n",
       "      <td>False</td>\n",
       "      <td>6.023196e-01</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Tlx2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32331</th>\n",
       "      <td>ENSMUSG00000092819.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>0.765519</td>\n",
       "      <td>False</td>\n",
       "      <td>8.658804e-05</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>-2.393727</td>\n",
       "      <td>Gm23639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10830</th>\n",
       "      <td>ENSMUSG00000037747.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>0.546995</td>\n",
       "      <td>False</td>\n",
       "      <td>1.841082e-10</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>-2.853563</td>\n",
       "      <td>Phyhipl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31987</th>\n",
       "      <td>ENSMUSG00000092335.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>0.218798</td>\n",
       "      <td>False</td>\n",
       "      <td>4.175245e-02</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Zfp977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>ENSMUSG00000019979.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>0.656120</td>\n",
       "      <td>False</td>\n",
       "      <td>3.873635e-06</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>-2.338283</td>\n",
       "      <td>Apaf1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         gid  pval  qval      log2fc      mean  zero_mean  \\\n",
       "19364   ENSMUSG00000068327.5   0.0   0.0 -297.776029  0.546721      False   \n",
       "32331   ENSMUSG00000092819.1   0.0   0.0 -297.776029  0.765519      False   \n",
       "10830   ENSMUSG00000037747.9   0.0   0.0 -297.776029  0.546995      False   \n",
       "31987   ENSMUSG00000092335.1   0.0   0.0 -297.776029  0.218798      False   \n",
       "2240   ENSMUSG00000019979.12   0.0   0.0 -297.776029  0.656120      False   \n",
       "\n",
       "               grad    coef_mle        coef_sd        ll    gname  \n",
       "19364  6.023196e-01 -297.776029  2.222759e-162  0.000000     Tlx2  \n",
       "32331  8.658804e-05 -297.776029  2.222759e-162 -2.393727  Gm23639  \n",
       "10830  1.841082e-10 -297.776029  2.222759e-162 -2.853563  Phyhipl  \n",
       "31987  4.175245e-02 -297.776029  2.222759e-162  0.000000   Zfp977  \n",
       "2240   3.873635e-06 -297.776029  2.222759e-162 -2.338283    Apaf1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deg - differential gene expression\n",
    "degs = sg.adata.uns[\"deg_timepoint_0hr_72hr\"] # Differential gene expression (\"DEG\") results stored here\n",
    "degs.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c51cc208-293a-42ec-95d1-6c108f38434c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>tid</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>log2fc</th>\n",
       "      <th>mean</th>\n",
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       "      <th>47581</th>\n",
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       "      <td>ENSMUSG00000038900.17</td>\n",
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       "      <td>-297.776029</td>\n",
       "      <td>0.109399</td>\n",
       "      <td>False</td>\n",
       "      <td>3.338657e-03</td>\n",
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       "      <td>-0.959681</td>\n",
       "      <td>ENSMUSG00000095407.2</td>\n",
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       "      <th>85485</th>\n",
       "      <td>ENSMUST00000178522.2</td>\n",
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       "      <td>-297.776029</td>\n",
       "      <td>0.109331</td>\n",
       "      <td>False</td>\n",
       "      <td>5.204170e-15</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSMUSG00000009035.15</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>ENSMUSG00000009073.16</td>\n",
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       "      <td>ENSMUST00000178520.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>0.109331</td>\n",
       "      <td>False</td>\n",
       "      <td>5.204170e-15</td>\n",
       "      <td>-297.776029</td>\n",
       "      <td>2.222759e-162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ENSMUSG00000095453.1</td>\n",
       "      <td>n-R5s149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         tid  pval  qval      log2fc      mean  zero_mean  \\\n",
       "47581   ENSMUST00000133842.1   0.0   0.0 -297.776029  0.218661      False   \n",
       "85506   ENSMUST00000178545.2   0.0   0.0 -297.776029  0.109399      False   \n",
       "85485   ENSMUST00000178522.2   0.0   0.0 -297.776029  0.109331      False   \n",
       "11079  ENSMUST00000053079.12   0.0   0.0 -297.776029  1.312241      False   \n",
       "85484   ENSMUST00000178520.1   0.0   0.0 -297.776029  0.109331      False   \n",
       "\n",
       "               grad    coef_mle        coef_sd        ll  \\\n",
       "47581  1.142108e-02 -297.776029  2.222759e-162 -1.446915   \n",
       "85506  3.338657e-03 -297.776029  2.222759e-162 -0.959681   \n",
       "85485  5.204170e-15 -297.776029  2.222759e-162  0.000000   \n",
       "11079  1.161889e-01 -297.776029  2.222759e-162  0.000000   \n",
       "85484  5.204170e-15 -297.776029  2.222759e-162  0.000000   \n",
       "\n",
       "                         gid     gname  \n",
       "47581  ENSMUSG00000038900.17     Rpl12  \n",
       "85506   ENSMUSG00000095407.2  Tmem200c  \n",
       "85485  ENSMUSG00000009035.15  Tmem184b  \n",
       "11079  ENSMUSG00000009073.16       Nf2  \n",
       "85484   ENSMUSG00000095453.1  n-R5s149  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# det - differential gene expression\n",
    "test = sg.adata.uns[\"det_timepoint_0hr_72hr\"] # Differential transcript expression (\"DET\") results stored here\n",
    "test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9796005a-78b8-4376-b78b-86e3996901b1",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>gid</th>\n",
       "      <th>p_val</th>\n",
       "      <th>dpi</th>\n",
       "      <th>adj_p_val</th>\n",
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       "      <th>891</th>\n",
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       "      <td>1.947155e-65</td>\n",
       "      <td>97.953918</td>\n",
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       "      <th>825</th>\n",
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       "      <td>97.497833</td>\n",
       "      <td>3.208189e-79</td>\n",
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       "      <th>2936</th>\n",
       "      <td>ENSMUSG00000036918.16</td>\n",
       "      <td>3.341302e-06</td>\n",
       "      <td>95.452316</td>\n",
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       "      <th>4946</th>\n",
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       "      <td>94.938362</td>\n",
       "      <td>5.222765e-24</td>\n",
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       "    <tr>\n",
       "      <th>3576</th>\n",
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       "      <th>3546</th>\n",
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       "      <td>1.000000e+00</td>\n",
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       "      <td>0.003403</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>3538</th>\n",
       "      <td>ENSMUSG00000048327.6</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5429 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        gid         p_val        dpi     adj_p_val\n",
       "891   ENSMUSG00000020836.15  1.947155e-65  97.953918  2.032905e-63\n",
       "825   ENSMUSG00000020439.17  2.422836e-81  97.497833  3.208189e-79\n",
       "2936  ENSMUSG00000036918.16  3.341302e-06  95.452316  2.897752e-05\n",
       "4946  ENSMUSG00000033060.15  1.606560e-25  94.938362  5.222765e-24\n",
       "3576  ENSMUSG00000036879.15  2.569213e-42  93.635071  1.549806e-40\n",
       "...                     ...           ...        ...           ...\n",
       "713   ENSMUSG00000019804.12  1.000000e+00   0.019366  1.000000e+00\n",
       "3546   ENSMUSG00000043702.9  1.000000e+00   0.007408  1.000000e+00\n",
       "763    ENSMUSG00000020116.7  1.000000e+00   0.006302  1.000000e+00\n",
       "4785  ENSMUSG00000041126.16  1.000000e+00   0.003403  1.000000e+00\n",
       "3538   ENSMUSG00000048327.6  1.000000e+00   0.001633  1.000000e+00\n",
       "\n",
       "[5429 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# die_iso - differential isoform expression\n",
    "test = sg.adata.uns[\"die_iso_timepoint_0hr_72hr\"] # Differential isoform expression (\"DIE\") results stored here\n",
    "\n",
    "test.sort_values(by=['dpi'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4e32e9a-ad9e-4880-a330-c5b7d587201a",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>39889</th>\n",
       "      <td>ENSMUSG00000030530.15</td>\n",
       "      <td>5.562945e-08</td>\n",
       "      <td>89.651779</td>\n",
       "      <td>6.101258e-07</td>\n",
       "      <td>Furin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24418</th>\n",
       "      <td>ENSMUSG00000038170.15</td>\n",
       "      <td>5.226496e-30</td>\n",
       "      <td>89.272077</td>\n",
       "      <td>1.956872e-28</td>\n",
       "      <td>Pde4dip</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         gid         p_val        dpi     adj_p_val    gname\n",
       "6911   ENSMUSG00000020836.15  1.947155e-65  97.953918  2.032905e-63    Coro6\n",
       "6453   ENSMUSG00000020439.17  2.422836e-81  97.497833  3.208189e-79     Smtn\n",
       "22614  ENSMUSG00000036918.16  3.341302e-06  95.452316  2.897752e-05     Ttc7\n",
       "39052  ENSMUSG00000033060.15  1.606560e-25  94.938362  5.222765e-24     Lmo7\n",
       "27578  ENSMUSG00000036879.15  2.569213e-42  93.635071  1.549806e-40     Phkb\n",
       "12608  ENSMUSG00000026253.14  4.357568e-87  92.196701  6.759210e-85    Chrng\n",
       "29891  ENSMUSG00000044813.15  3.027641e-33  91.546761  1.245232e-31      Shb\n",
       "36961  ENSMUSG00000035900.18  2.560713e-14  90.443451  4.793832e-13   Gramd4\n",
       "39889  ENSMUSG00000030530.15  5.562945e-08  89.651779  6.101258e-07    Furin\n",
       "24418  ENSMUSG00000038170.15  5.226496e-30  89.272077  1.956872e-28  Pde4dip"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# die_iso - differential isoform expression\n",
    "test = sg.adata.uns[\"die_iso_timepoint_0hr_72hr\"] # Differential isoform expression (\"DIE\") results stored here\n",
    "\n",
    "test = test.merge(sg.t_df[['gid', 'gname']], how='left', on='gid')\n",
    "test = test.loc[test.adj_p_val <= 0.05]\n",
    "test = test.sort_values(by='dpi', ascending=False)\n",
    "test.drop_duplicates(inplace=True)\n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d5e3472-2d80-482d-9fec-7d63e8d6cbb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting transcripts for ENSMUSG00000005583.16\n",
      "Saving transcript path graph for ENSMUST00000199450.4 as ../swan_data/figures/mef2c_c2c12_novel_ENSMUST00000199450.4_path.png\n",
      "Saving transcript path graph for TALONT000362201 as ../swan_data/figures/mef2c_c2c12_novel_TALONT000362201_path.png\n",
      "Saving transcript path graph for ENSMUST00000198199.4 as ../swan_data/figures/mef2c_c2c12_novel_ENSMUST00000198199.4_path.png\n",
      "Saving transcript path graph for ENSMUST00000005722.13 as ../swan_data/figures/mef2c_c2c12_novel_ENSMUST00000005722.13_path.png\n",
      "Saving transcript path graph for ENSMUST00000196832.1 as ../swan_data/figures/mef2c_c2c12_novel_ENSMUST00000196832.1_path.png\n",
      "Saving transcript path graph for ENSMUST00000199432.4 as ../swan_data/figures/mef2c_c2c12_novel_ENSMUST00000199432.4_path.png\n",
      "Saving transcript path graph for ENSMUST00000198217.4 as ../swan_data/figures/mef2c_c2c12_novel_ENSMUST00000198217.4_path.png\n",
      "Generating report for ENSMUSG00000005583.16\n"
     ]
    }
   ],
   "source": [
    "# generate a report for the given gene\n",
    "# save it with the given filepath prefix\n",
    "# include differential transcript expression test significance results\n",
    "# (defaults to significance threshold q >= 0.05)\n",
    "# display the novelty category associated with the transcript\n",
    "# display novel splice sites and junctions\n",
    "#     as outlined nodes and dashed edges respectively\n",
    "sg.gen_report('Mef2c',\n",
    "              prefix='../swan_data/figures/mef2c_c2c12',\n",
    "              novelty=True,\n",
    "              indicate_novel=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54da50b4-3ae3-4ac1-925d-221107b2f276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting transcripts for ENSMUSG00000020836.15\n",
      "Saving transcript path graph for TALONT000535409 as ../swan_data/figures/coro6_c2c12_novel_TALONT000535409_path.png\n",
      "Saving transcript path graph for ENSMUST00000102493.7 as ../swan_data/figures/coro6_c2c12_novel_ENSMUST00000102493.7_path.png\n",
      "Saving transcript path graph for TALONT000535406 as ../swan_data/figures/coro6_c2c12_novel_TALONT000535406_path.png\n",
      "Saving transcript path graph for TALONT000535398 as ../swan_data/figures/coro6_c2c12_novel_TALONT000535398_path.png\n",
      "Saving transcript path graph for TALONT000535401 as ../swan_data/figures/coro6_c2c12_novel_TALONT000535401_path.png\n",
      "Saving transcript path graph for TALONT000535407 as ../swan_data/figures/coro6_c2c12_novel_TALONT000535407_path.png\n",
      "Saving transcript path graph for TALONT000535403 as ../swan_data/figures/coro6_c2c12_novel_TALONT000535403_path.png\n",
      "Saving transcript path graph for ENSMUST00000108391.8 as ../swan_data/figures/coro6_c2c12_novel_ENSMUST00000108391.8_path.png\n",
      "Generating report for ENSMUSG00000020836.15\n"
     ]
    }
   ],
   "source": [
    "sg.gen_report('Coro6',\n",
    "              prefix='../swan_data/figures/coro6_c2c12',\n",
    "              novelty=True,\n",
    "              layer='pi',\n",
    "              cmap='magma',\n",
    "              indicate_novel=True,\n",
    "              transcript_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a4ac76-8f8a-4f0c-a77e-15b4a80dbc54",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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