pydeseq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce0f0460-a335-4e48-b23d-4119efc1c29f",
   "metadata": {},
   "source": [
    "# Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7f78a50d-fdbc-48a9-b2ff-7bc71cb80c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pydeseq2.dds import DeseqDataSet\n",
    "from pydeseq2.ds import DeseqStats\n",
    "\n",
    "sns.set_context('paper', font_scale=2) # set some nice global plotting settings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd4b0ec-6f4e-4926-8e22-8ec7d556ddaa",
   "metadata": {},
   "source": [
    "# Read in data\n",
    "Make sure to use the raw **counts matrix** csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a366bfa5-cdbf-4c87-a471-9a0f31d16e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/fshd_counts_matrix.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c6972223-9037-445e-891d-56dc45891597",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C_3_1</th>\n",
       "      <th>C_3_2</th>\n",
       "      <th>C_5_1</th>\n",
       "      <th>C_5_2</th>\n",
       "      <th>F_3_1</th>\n",
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       "      <th>DDX11L1</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>WASH7P</th>\n",
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       "      <td>17.0</td>\n",
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      "text/plain": [
       "             C_3_1  C_3_2  C_5_1  C_5_2  F_3_1  F_3_2  F_5_1  F_5_2\n",
       "DDX11L1        3.0    0.0    4.0    1.0   25.0    0.0    0.0   19.0\n",
       "WASH7P        22.0   17.0   25.0   17.0   16.0    3.0   18.0    7.0\n",
       "MIR6859-1      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "MIR1302-2HG    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "MIR1302-2      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069c55af-946e-4310-a208-ec35a68b57de",
   "metadata": {},
   "source": [
    "# Read in metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3f67968e-7ac4-4cd3-a14d-84a372d88a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('data/fshd_metadata.csv')\n",
    "metadata = metadata[metadata['Sample'].isin(df.columns)]\n",
    "\n",
    "metadata.index = metadata['Sample'] # metadata index must be column names of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eeb70e2d-a064-43c6-a283-a02474471f9c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SampleID</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Replicate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C_3_1</th>\n",
       "      <td>Control-1 Day 3 Rep 1</td>\n",
       "      <td>C_3_1</td>\n",
       "      <td>Day_3</td>\n",
       "      <td>Control</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_3_2</th>\n",
       "      <td>Control-1 Day 3 Rep 2</td>\n",
       "      <td>C_3_2</td>\n",
       "      <td>Day_3</td>\n",
       "      <td>Control</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_5_1</th>\n",
       "      <td>Control-1 Day 5 Rep 1</td>\n",
       "      <td>C_5_1</td>\n",
       "      <td>Day_5</td>\n",
       "      <td>Control</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_5_2</th>\n",
       "      <td>Control-1 Day 5 Rep 2</td>\n",
       "      <td>C_5_2</td>\n",
       "      <td>Day_5</td>\n",
       "      <td>Control</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F_3_1</th>\n",
       "      <td>FSHD2-1 Day 3 Rep 1</td>\n",
       "      <td>F_3_1</td>\n",
       "      <td>Day_3</td>\n",
       "      <td>FSHD</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                     SampleID Sample Timepoint Condition  Replicate\n",
       "Sample                                                             \n",
       "C_3_1   Control-1 Day 3 Rep 1  C_3_1     Day_3   Control          1\n",
       "C_3_2   Control-1 Day 3 Rep 2  C_3_2     Day_3   Control          2\n",
       "C_5_1   Control-1 Day 5 Rep 1  C_5_1     Day_5   Control          1\n",
       "C_5_2   Control-1 Day 5 Rep 2  C_5_2     Day_5   Control          2\n",
       "F_3_1     FSHD2-1 Day 3 Rep 1  F_3_1     Day_3      FSHD          1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8745f984-e762-424c-912a-2c75661cd243",
   "metadata": {},
   "source": [
    "# Define filters and group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1c2266f3-1a2b-42ec-b7cf-59569762a1be",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtering = {'Condition': ['Control'],\n",
    "             'Timepoint': ['Day_3','Day_5']}\n",
    "\n",
    "group = 'Timepoint' # change to the group you're interested in making the comparison in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4b5bd441-b78b-4514-90a5-4619593f3804",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_selected = metadata.copy()\n",
    "for col in filtering.keys():\n",
    "    metadata_selected = metadata_selected[metadata_selected[col].isin(filtering[col])]\n",
    "\n",
    "df_selected = df.loc[:, metadata_selected['Sample']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bc68ca-1bf4-408c-a5b0-dba61720e502",
   "metadata": {},
   "source": [
    "# Run PyDESeq2\n",
    "See package documedf_selectedntation [here](https://pydeseq2.readthedocs.io/en/latest/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5f6cb9bf-7675-4d81-80d8-21def6898aaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting size factors...\n",
      "... done in 0.00 seconds.\n",
      "\n",
      "Fitting dispersions...\n",
      "... done in 15.43 seconds.\n",
      "\n",
      "Fitting dispersion trend curve...\n",
      "... done in 17.49 seconds.\n",
      "\n",
      "Fitting MAP dispersions...\n",
      "... done in 11.88 seconds.\n",
      "\n",
      "Fitting LFCs...\n",
      "... done in 3.78 seconds.\n",
      "\n",
      "Refitting 0 outliers.\n",
      "\n",
      "Running Wald tests...\n",
      "... done in 8.78 seconds.\n",
      "\n",
      "Log2 fold change & Wald test p-value: Timepoint Day_3 vs Day_5\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
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       "      <th>DDX11L1</th>\n",
       "      <td>2.081340</td>\n",
       "      <td>-0.909322</td>\n",
       "      <td>1.910474</td>\n",
       "      <td>-0.475967</td>\n",
       "      <td>0.634098</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>WASH7P</th>\n",
       "      <td>20.371162</td>\n",
       "      <td>-0.325099</td>\n",
       "      <td>0.586397</td>\n",
       "      <td>-0.554401</td>\n",
       "      <td>0.579305</td>\n",
       "      <td>0.747450</td>\n",
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       "    <tr>\n",
       "      <th>MIR6859-1</th>\n",
       "      <td>0.000000</td>\n",
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       "      <th>MIR1302-2HG</th>\n",
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       "      <th>MIR1302-2</th>\n",
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       "    <tr>\n",
       "      <th>AC240274.1</th>\n",
       "      <td>122.205833</td>\n",
       "      <td>0.593113</td>\n",
       "      <td>0.152971</td>\n",
       "      <td>3.877294</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000585</td>\n",
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       "    <tr>\n",
       "      <th>AC213203.2</th>\n",
       "      <td>1.192229</td>\n",
       "      <td>1.773730</td>\n",
       "      <td>2.516811</td>\n",
       "      <td>0.704753</td>\n",
       "      <td>0.480964</td>\n",
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       "    <tr>\n",
       "      <th>AC213203.1</th>\n",
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       "<p>58884 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "DDX11L1        2.081340       -0.909322  1.910474 -0.475967  0.634098   \n",
       "WASH7P        20.371162       -0.325099  0.586397 -0.554401  0.579305   \n",
       "MIR6859-1      0.000000             NaN       NaN       NaN       NaN   \n",
       "MIR1302-2HG    0.000000             NaN       NaN       NaN       NaN   \n",
       "MIR1302-2      0.000000             NaN       NaN       NaN       NaN   \n",
       "...                 ...             ...       ...       ...       ...   \n",
       "AC240274.1   122.205833        0.593113  0.152971  3.877294  0.000106   \n",
       "RF00003_14     0.000000             NaN       NaN       NaN       NaN   \n",
       "RF00003_15     0.000000             NaN       NaN       NaN       NaN   \n",
       "AC213203.2     1.192229        1.773730  2.516811  0.704753  0.480964   \n",
       "AC213203.1     0.000000             NaN       NaN       NaN       NaN   \n",
       "\n",
       "                 padj  \n",
       "DDX11L1           NaN  \n",
       "WASH7P       0.747450  \n",
       "MIR6859-1         NaN  \n",
       "MIR1302-2HG       NaN  \n",
       "MIR1302-2         NaN  \n",
       "...               ...  \n",
       "AC240274.1   0.000585  \n",
       "RF00003_14        NaN  \n",
       "RF00003_15        NaN  \n",
       "AC213203.2        NaN  \n",
       "AC213203.1        NaN  \n",
       "\n",
       "[58884 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds = DeseqDataSet(\n",
    "    counts=df_selected.T,\n",
    "    clinical=metadata_selected,\n",
    "    design_factors=group,\n",
    "    refit_cooks=True\n",
    ")\n",
    "dds.deseq2()\n",
    "stat_res = DeseqStats(dds, \n",
    "                      contrast=[group] + filtering[group])\n",
    "stat_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e480279-91c4-4781-ae8f-cdb2089f93d5",
   "metadata": {},
   "source": [
    "# Format results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4c9d9d0a-ccfa-4d9f-8c30-24127b0e3416",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/apps/python/3.8.0/lib/python3.8/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "results = stat_res.results_df # get results df\n",
    "\n",
    "results['nlog10padj'] = -np.log10(results['padj']) # make -log10(adj. p-value) column\n",
    "\n",
    "results.replace([np.inf, -np.inf], np.nan, inplace=True) # remove NAN and infinity values...\n",
    "\n",
    "results = results.dropna() # drop NAs\n",
    "\n",
    "results = results.reset_index().rename(columns={'index': 'gene_name'}) # set gene name to be its own column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e9fcea-85a2-4a61-9c43-621beac273c1",
   "metadata": {},
   "source": [
    "# Volcano plot \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b8b99f70-0de5-4f3a-aff5-612530db23ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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",
      "<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": [
    "degs_up = results[(results['padj'] < 0.05) & (results['log2FoldChange'] > 1)]\n",
    "degs_down = results[(results['padj'] < 0.05) & (results['log2FoldChange'] < -1)]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7, 6))\n",
    "sns.scatterplot(data=results, x='log2FoldChange', y='nlog10padj', alpha=0.5, \n",
    "                color='black', label = \"Not significant\")\n",
    "\n",
    "# basically layer the subset dataframes on top of the original plot\n",
    "sns.scatterplot(data=degs_up, x='log2FoldChange', y='nlog10padj', alpha=0.5, \n",
    "                color='red', label = f\"Upregulated ({len(degs_up)} genes)\")\n",
    "sns.scatterplot(data=degs_down, x='log2FoldChange', y='nlog10padj', alpha=0.5, \n",
    "                color='blue', label = f\"Downregulated ({len(degs_down)} genes)\")\n",
    "\n",
    "plt.xlabel('log2(Fold Change)')\n",
    "plt.ylabel('-log10(adj. p-value)')\n",
    "plt.title('Control Day_3 vs Day_5')\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc='upper left') # move legend\n",
    "\n",
    "# draw horizontal and vertical dashed lines\n",
    "plt.axhline(1.3, color='black', linestyle='--')\n",
    "plt.axvline(1, color='black', linestyle='--')\n",
    "plt.axvline(-1, color='black', linestyle='--')\n",
    "\n",
    "# label specific points\n",
    "for index, row in results.iterrows():\n",
    "    if abs(row['log2FoldChange']) > 1 and row['nlog10padj'] > 150:\n",
    "        plt.text(row['log2FoldChange'], row['nlog10padj'], row['gene_name'], fontsize=10)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86707b98-f3d1-41eb-8293-1911df9d56e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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