DGE-charts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1dcbf1f-23b7-474c-a811-65535fa8c9c2",
   "metadata": {},
   "source": [
    "Created by Michal Bukowski (michal.bukowski@tuta.io, m.bukowski@uj.edu.pl) under GPL-3.0 license as a part of the [dge-ontology](https://github.com/michalbukowski/dge-ontology) repository<br>\n",
    "Department of Analytical Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a605eb-c56c-40a4-a725-264b39e49cfa",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "# DGE-charts\n",
    "This Jupyter notebook aims at visualising DGE results obtained using [Salmon](https://doi.org/10.1186/s13059-014-0550-8) and [DESeq2](https://doi.org/10.1038/nmeth.4197). The exemplary input contains metadata on _Staphylococcus aureus_ RN4220 transcriptome and output from [rnaseq-pipeline-2](https://github.com/michalbukowski/rnaseq-pipeline-2) executed for two strains sampled in the logarithmic growth phase: a wild type (`wt51e`) vs. $\\Delta$_saoBC_ mutant (`mt51e`) (NCBI BioProject accession number PRJNA798259). Execution of subsequent cells in many instances relies on the output of preceding cells, thus cells should be executed in the order as presented in this notebook.\n",
    "\n",
    "<u>Notebook cell output has been saved in the original (repository) version of the notebook</u>. If rerun for the exemplary data, it should give the same results. All visualisations are saved to `output` directory as high-resolution PNG files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81f41010-effc-4f87-9944-d1db4f05811b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do the necessary imports.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib, matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from PIL import Image\n",
    "\n",
    "# Configure matplotlib grid for 3d axes and make sure\n",
    "# the way figures are displayed is the same as they are saved.\n",
    "matplotlib.rcParams['grid.linewidth'] = 0.25\n",
    "matplotlib.rcParams['grid.color'] = 'lightgray'\n",
    "%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}\n",
    "\n",
    "# Attempt to create a directory for output files,\n",
    "# in case it does not exist.\n",
    "! mkdir -p \"output\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504b2a43-9c89-4ab4-b9a4-f176e5733f55",
   "metadata": {},
   "source": [
    "## Data processing\n",
    "In this section input data is read into Pandas DataFrame objects, and then pre-processed for visualisation purposes. Details are provided in comments in the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72724554-8293-49e4-b28a-5ed084625ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def color_wheel():\n",
    "    '''Generator that yields given colours in a periodic way.\n",
    "       The colors are: royalblue crimson darkgreen purple.\n",
    "    '''\n",
    "    colors = 'royalblue crimson darkgreen purple'.split()\n",
    "    index  = 0\n",
    "    while True:\n",
    "        yield colors[index]\n",
    "        index = index + 1 if index + 1 < len(colors) else 0"
   ]
  },
  {
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   "execution_count": 3,
   "id": "b1962ec6-aa7a-40dc-b1ad-a229d9914af0",
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3335 entries, SAOUHSC_03055 to SAOUHSC_02125\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   wt51e_lg_1  3335 non-null   float64\n",
      " 1   wt51e_lg_2  3335 non-null   float64\n",
      " 2   wt51e_lg_3  3335 non-null   float64\n",
      " 3   mt51e_lg_1  3335 non-null   float64\n",
      " 4   mt51e_lg_2  3335 non-null   float64\n",
      " 5   mt51e_lg_3  3335 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 182.4+ KB\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wt51e_lg_1</th>\n",
       "      <th>wt51e_lg_2</th>\n",
       "      <th>wt51e_lg_3</th>\n",
       "      <th>mt51e_lg_1</th>\n",
       "      <th>mt51e_lg_2</th>\n",
       "      <th>mt51e_lg_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></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",
       "      <th>SAOUHSC_03055</th>\n",
       "      <td>10675.2000</td>\n",
       "      <td>9587.9400</td>\n",
       "      <td>7257.8000</td>\n",
       "      <td>7024.30000</td>\n",
       "      <td>7176.33000</td>\n",
       "      <td>4960.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAOUHSC_03054</th>\n",
       "      <td>15.7189</td>\n",
       "      <td>13.0851</td>\n",
       "      <td>15.1185</td>\n",
       "      <td>4.58333</td>\n",
       "      <td>9.24123</td>\n",
       "      <td>27.3092</td>\n",
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       "    <tr>\n",
       "      <th>SAOUHSC_03053</th>\n",
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       "      <td>23.5031</td>\n",
       "      <td>29.4745</td>\n",
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       "      <td>30.96530</td>\n",
       "      <td>57.2844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAOUHSC_03051</th>\n",
       "      <td>88.9506</td>\n",
       "      <td>87.0647</td>\n",
       "      <td>65.8333</td>\n",
       "      <td>71.75470</td>\n",
       "      <td>77.64870</td>\n",
       "      <td>131.0950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAOUHSC_03049</th>\n",
       "      <td>161.2820</td>\n",
       "      <td>149.2170</td>\n",
       "      <td>137.2740</td>\n",
       "      <td>145.41600</td>\n",
       "      <td>180.34400</td>\n",
       "      <td>136.8840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               wt51e_lg_1  wt51e_lg_2  wt51e_lg_3  mt51e_lg_1  mt51e_lg_2  \\\n",
       "Name                                                                        \n",
       "SAOUHSC_03055  10675.2000   9587.9400   7257.8000  7024.30000  7176.33000   \n",
       "SAOUHSC_03054     15.7189     13.0851     15.1185     4.58333     9.24123   \n",
       "SAOUHSC_03053     25.3568     23.5031     29.4745    15.93060    30.96530   \n",
       "SAOUHSC_03051     88.9506     87.0647     65.8333    71.75470    77.64870   \n",
       "SAOUHSC_03049    161.2820    149.2170    137.2740   145.41600   180.34400   \n",
       "\n",
       "               mt51e_lg_3  \n",
       "Name                       \n",
       "SAOUHSC_03055   4960.8200  \n",
       "SAOUHSC_03054     27.3092  \n",
       "SAOUHSC_03053     57.2844  \n",
       "SAOUHSC_03051    131.0950  \n",
       "SAOUHSC_03049    136.8840  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load TPM for all transcripts (locus_tags)\n",
    "# from two groups being compared\n",
    "# (wt51e_lg - wild type, mt51e - mutant).\n",
    "tpm_df = pd.read_csv(\n",
    "    'input/mt51e_wt51e_TPM.tsv',\n",
    "    index_col='Name',\n",
    "    sep='\\t'\n",
    ")\n",
    "display(tpm_df.info())\n",
    "tpm_df.head()"
   ]
  },
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   "execution_count": 4,
   "id": "45698e79-d338-4488-87bb-0d88b07bab11",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3335 entries, SAOUHSC_03055 to srn_1040_sRNA92\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   baseMean        2503 non-null   float64\n",
      " 1   log2FoldChange  2503 non-null   float64\n",
      " 2   lfcSE           2503 non-null   float64\n",
      " 3   pvalue          2496 non-null   float64\n",
      " 4   padj            2496 non-null   float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 156.3+ KB\n"
     ]
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       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
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       "    <tr>\n",
       "      <th>locus_tag</th>\n",
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       "      <th>SAOUHSC_03055</th>\n",
       "      <td>900.082113</td>\n",
       "      <td>-0.190838</td>\n",
       "      <td>0.253922</td>\n",
       "      <td>0.107434</td>\n",
       "      <td>0.681069</td>\n",
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       "    <tr>\n",
       "      <th>SAOUHSC_03054</th>\n",
       "      <td>12.453537</td>\n",
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       "    <tr>\n",
       "      <th>SAOUHSC_03053</th>\n",
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       "      <td>0.491597</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>SAOUHSC_03049</th>\n",
       "      <td>479.204817</td>\n",
       "      <td>-0.012935</td>\n",
       "      <td>0.165215</td>\n",
       "      <td>0.895382</td>\n",
       "      <td>0.981500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                 baseMean  log2FoldChange     lfcSE    pvalue      padj\n",
       "locus_tag                                                              \n",
       "SAOUHSC_03055  900.082113       -0.190838  0.253922  0.107434  0.681069\n",
       "SAOUHSC_03054   12.453537       -0.023077  0.201086  0.677658  0.943881\n",
       "SAOUHSC_03053  167.816277        0.057208  0.196688  0.491597  0.892770\n",
       "SAOUHSC_03051  223.827558        0.025922  0.177333  0.784860  0.962877\n",
       "SAOUHSC_03049  479.204817       -0.012935  0.165215  0.895382  0.981500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load DESeq2 DGE results for all transcripts (locus_tags)\n",
    "# from two groups being compared.\n",
    "dge_df = pd.read_csv(\n",
    "    'input/mt51e_wt51e_DGE.tsv',\n",
    "    index_col='locus_tag',\n",
    "    sep='\\t'\n",
    ")\n",
    "display(dge_df.info())\n",
    "dge_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a48513d-fc4c-4fb2-a1e7-2d3638d15de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be able to calculate log values of padj (FDR),\n",
    "# replace values equal to 0.0 with the minimal\n",
    "# observed padj value.\n",
    "adj_df = dge_df.copy()\n",
    "adj_df.loc[adj_df['padj'] == 0.0, 'padj'] = \\\n",
    "    adj_df.loc[adj_df['padj'] != 0.0, 'padj'].min()"
   ]
  },
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   "execution_count": 6,
   "id": "76e0a2d7-7789-4506-9019-8f1cb16fc640",
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    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3335 entries, SAOUHSC_03055 to SAOUHSC_02125\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   baseMean        2503 non-null   float64\n",
      " 1   log2FoldChange  2503 non-null   float64\n",
      " 2   lfcSE           2503 non-null   float64\n",
      " 3   pvalue          2496 non-null   float64\n",
      " 4   padj            2496 non-null   float64\n",
      " 5   mt51e_lg_mean   3335 non-null   float64\n",
      " 6   wt51e_lg_mean   3335 non-null   float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 337.5+ KB\n"
     ]
    },
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     "output_type": "display_data"
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       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>mt51e_lg_mean</th>\n",
       "      <th>wt51e_lg_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>SAOUHSC_03055</th>\n",
       "      <td>900.082113</td>\n",
       "      <td>-0.190838</td>\n",
       "      <td>0.253922</td>\n",
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       "      <td>93.499467</td>\n",
       "      <td>80.616200</td>\n",
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       "    <tr>\n",
       "      <th>SAOUHSC_03049</th>\n",
       "      <td>479.204817</td>\n",
       "      <td>-0.012935</td>\n",
       "      <td>0.165215</td>\n",
       "      <td>0.895382</td>\n",
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      "text/plain": [
       "                 baseMean  log2FoldChange     lfcSE    pvalue      padj  \\\n",
       "Name                                                                      \n",
       "SAOUHSC_03055  900.082113       -0.190838  0.253922  0.107434  0.681069   \n",
       "SAOUHSC_03054   12.453537       -0.023077  0.201086  0.677658  0.943881   \n",
       "SAOUHSC_03053  167.816277        0.057208  0.196688  0.491597  0.892770   \n",
       "SAOUHSC_03051  223.827558        0.025922  0.177333  0.784860  0.962877   \n",
       "SAOUHSC_03049  479.204817       -0.012935  0.165215  0.895382  0.981500   \n",
       "\n",
       "               mt51e_lg_mean  wt51e_lg_mean  \n",
       "Name                                         \n",
       "SAOUHSC_03055    6387.150000    9173.646667  \n",
       "SAOUHSC_03054      13.711253      14.640833  \n",
       "SAOUHSC_03053      34.726767      26.111467  \n",
       "SAOUHSC_03051      93.499467      80.616200  \n",
       "SAOUHSC_03049     154.214667     149.257667  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Based on column names (group_replica) extract group names\n",
    "# and calculate mean TPM for each transcript within a group.\n",
    "groups = sorted(set(\n",
    "    pd.Series(tpm_df.columns).apply(lambda col: col[:col.rfind('_')]) ))\n",
    "tpm_mean_df = pd.DataFrame()\n",
    "# Control whether replicas from two groups\n",
    "# are being compared (experiment vs. reference)\n",
    "assert len(groups) == 2\n",
    "for group in groups:\n",
    "    tpm_mean_df[f'{group}_mean'] = \\\n",
    "        tpm_df.loc[:, tpm_df.columns.str.startswith(f'{group}_')].mean(axis=1)\n",
    "\n",
    "# To be able to calculate log values of TPM values,\n",
    "# replace values equal to 0.0 with the minimal\n",
    "# observed TPM value.\n",
    "tmp_raw = tpm_mean_df.to_numpy()\n",
    "tmp_raw[tmp_raw == 0.0] = tmp_raw[tmp_raw != 0.0].min()\n",
    "tpm_mean_df = pd.DataFrame(tmp_raw, index=tpm_mean_df.index, columns=tpm_mean_df.columns)\n",
    "\n",
    "# Merge adjusted DGE results with mean TPM values on locus_tag column,\n",
    "# which was set to be the index column when reading data from files.\n",
    "fin_df = adj_df.merge(tpm_mean_df, 'right', left_index=True, right_index=True)\n",
    "display(fin_df.info())\n",
    "fin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "244174ff-039e-44e0-a72a-15e5bff47d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a boolean series for filtering DataFrames against\n",
    "# log2FoldChange (fold_th) and padj/FDR (fdr_th) thresholds.\n",
    "fold_th = 1.00\n",
    "fdr_th  = 0.05\n",
    "filtered = (fin_df['log2FoldChange'].abs() >= fold_th) & \\\n",
    "           (fin_df['padj'] <= fdr_th)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8cc059-81bd-4fef-84a7-3145eae8846e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## TPM 2-D replicas PCA\n",
    "In order to analyse general dispersion of replicas, 2-D PCA is performed for log2-transformed TPM values. Details are provided in comments in the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f519da38-0eb9-40db-a2bd-4b2ebeefb839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5399971 , 0.14321279])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get raw TPM values for all transcripts and replicas,\n",
    "# transpose it in order to place TPM values for each replica\n",
    "# row-wise to meet scikit-learn requirements\n",
    "# (vectors describing samples are rows of a 2D array)\n",
    "data = tpm_df.to_numpy().T + 1\n",
    "# Log-transform TPM values.\n",
    "data = np.log2(data)\n",
    "\n",
    "# Perform PCA for log-transformed values by\n",
    "# model fitting and then transforming original data.\n",
    "pca_2d = PCA(n_components=2)\n",
    "pca_2d.fit(data)\n",
    "\n",
    "data_trans2d = pca_2d.transform(data)\n",
    "trans2d_df = pd.DataFrame(data_trans2d.T, columns=tpm_df.columns)\n",
    "\n",
    "pca_2d.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be4d5c0e-fba3-4305-8512-3a4a0efe2b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2D plot for PCA results using matplotlib.\n",
    "fig = plt.figure(figsize=(5,4), facecolor='white', dpi=100)\n",
    "ax = fig.add_axes([0.15, 0.15, 0.80, 0.75])\n",
    "ax.grid(linestyle=':', linewidth=0.5)\n",
    "\n",
    "colors = color_wheel()\n",
    "\n",
    "for group in groups:\n",
    "    color = next(colors)\n",
    "    sub_df = trans2d_df.loc[:, trans2d_df.columns.str.startswith(group)]\n",
    "    ax.scatter(*sub_df.to_numpy(), color=color, label=group, s=80.0)\n",
    "    for rep_name, coords in sub_df.items():\n",
    "        ax.text(*coords, rep_name[rep_name.rfind('_')+1:], fontsize=6.0,\n",
    "                va='center', ha='center', color='white')\n",
    "\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_title('PCA of log2TPM values', fontsize=12.0)\n",
    "ax.set_xlabel(f'Component 1 ({pca_2d.explained_variance_ratio_[0]*100:0.0f}%)')\n",
    "ax.set_ylabel(f'Component 2 ({pca_2d.explained_variance_ratio_[1]*100:0.0f}%)')\n",
    "\n",
    "path = 'output/TPM_replicas_PCA_2d.png'\n",
    "fig.savefig(path, transparent=False, dpi=300)\n",
    "Image.open(path).convert('RGB').save(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0c07e4-c472-4edf-b1c5-396d4f03f2fc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## TPM 3-D replicas PCA\n",
    "If desirable, analogical PCA analysis might be done by reduction to 3-dimensions. Details are provided in comments in the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55840068-f467-4fe3-9e92-ee3456fced15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5399971 , 0.14321279, 0.12577146])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Go through analogical steps for 3D PCA.\n",
    "pca_3d = PCA(n_components=3)\n",
    "pca_3d.fit(data)\n",
    "\n",
    "data_trans3d = pca_3d.transform(data)\n",
    "trans3d_df = pd.DataFrame(data_trans3d.T, columns=tpm_df.columns)\n",
    "\n",
    "pca_3d.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f316093-7d1e-4fe9-85d7-ba00f798bcef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), facecolor='white', dpi=100)\n",
    "ax = fig.add_axes([0.03, 0.08, 0.81, 0.88], projection='3d')\n",
    "\n",
    "colors = color_wheel()\n",
    "min_z  = trans3d_df.iloc[-1].min()\n",
    "\n",
    "for group in groups:\n",
    "    color = next(colors)\n",
    "    sub_df = trans3d_df.loc[:, trans3d_df.columns.str.startswith(group)]\n",
    "    ax.scatter(*sub_df.to_numpy()[[0, 1], :], min_z, color=color, s=6.0, alpha=0.3)\n",
    "    markerline, stemline, baseline = ax.stem(\n",
    "        *sub_df.to_numpy(), bottom=min_z, label=group)\n",
    "    markerline.set_color(color)\n",
    "    markerline.set_markersize(8.0)\n",
    "    stemline.set_color(color)\n",
    "    stemline.set_linewidth(0.5)\n",
    "    stemline.set_linestyle(':')\n",
    "    baseline.set_linestyle('')\n",
    "    for rep_name, coords in sub_df.items():\n",
    "        ax.text(*coords, rep_name[rep_name.rfind('_')+1:], fontsize=6.0,\n",
    "                va='center', ha='center', color='white')\n",
    "\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_title('PCA of log2TPM values', fontsize=12.0)\n",
    "ax.set_xlabel(f'Component 1 ({pca_3d.explained_variance_ratio_[0]*100:0.0f}%)')\n",
    "ax.set_ylabel(f'Component 2 ({pca_3d.explained_variance_ratio_[1]*100:0.0f}%)')\n",
    "ax.set_zlabel(f'Component 3 ({pca_3d.explained_variance_ratio_[2]*100:0.0f}%)')\n",
    "\n",
    "path = 'output/TPM_replicas_PCA_3d.png'\n",
    "fig.savefig(path, transparent=False, dpi=300)\n",
    "Image.open(path).convert('RGB').save(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9b1b13-46c1-4754-815b-c92f724d5318",
   "metadata": {},
   "source": [
    "## TPM vs TPM plot\n",
    "In order to depict general differences between two transcriptomes a plot of log2-transformed mean `TPM` values might be rendered. Each point (analysed transcript) has two coordinates, `log2TPM` of a wild type and `log2TPM` of a mutant. The closer to the diagonal the lower the difference in a transcript amount. Points corresponding to transcripts that are considered differentiating are of red colour. Here these are those with _`padj`_ &le; `0.05`, and absolute values of `log2FoldChange` &ge; `0.58` (`foldChange` &ge; `1.5`). Other details are provided in comments in the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e218dd0-a046-4dbb-b955-9217bf3a390c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a log2(TPM) vs. log2(TPM) plot for the groups being compared.\n",
    "# Plot filtered results (meeting log2FoldChange and padj criteria)\n",
    "# in red, others in gray.\n",
    "fig = plt.figure(figsize=(5,4), facecolor='white', dpi=100)\n",
    "ax = fig.add_axes([0.18, 0.13, 0.80, 0.78])\n",
    "ax.grid(linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.scatter(\n",
    "    np.log2( fin_df.loc[~filtered, f'{groups[0]}_mean'] + 1 ),\n",
    "    np.log2( fin_df.loc[~filtered, f'{groups[1]}_mean'] + 1 ),\n",
    "    s=10.0, facecolor='lightgray', edgecolor='black',\n",
    "    linewidth=0.2, alpha=0.5\n",
    ")\n",
    "ax.scatter(\n",
    "    np.log2( fin_df.loc[filtered, f'{groups[0]}_mean'] + 1 ),\n",
    "    np.log2( fin_df.loc[filtered, f'{groups[1]}_mean'] + 1 ),\n",
    "    s=10.0, facecolor='crimson', edgecolor='black',\n",
    "    linewidth=0.2, alpha=0.5\n",
    ")\n",
    "\n",
    "ax.set_title('TPM vs. TPM', fontsize=12.0)\n",
    "ax.set_xlabel(f'Log$_{2}$ TPM {groups[0]}')\n",
    "ax.set_ylabel(f'Log$_{2}$ TPM {groups[1]}')\n",
    "\n",
    "path = 'output/DGE_TPM.png'\n",
    "fig.savefig(path, transparent=False, dpi=300)\n",
    "Image.open(path).convert('RGB').save(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3e6593-4b26-4688-940c-b10d0fe60cba",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Volcano plot\n",
    "Another way of depicting general differences between two transcriptomes, a plot of log10-transformed _`padj`_ vs. `log2FoldChange` values. Again, points corresponding to transcripts that are considered differentiating are of red colour. Other details are provided in comments in the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eec16361-96f3-47a9-b7b4-b904f7b9f37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a log2FoldChange vs. log10(padj) plot (vulcano plot)\n",
    "# for the groups being compared. Plot filtered results\n",
    "# (meeting log2FoldChange and padj criteria) in red, others in gray.\n",
    "fig = plt.figure(figsize=(5,4), facecolor='white', dpi=100)\n",
    "ax = fig.add_axes([0.18, 0.13, 0.80, 0.78])\n",
    "ax.grid(linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.scatter(\n",
    "    fin_df.loc[~filtered, 'log2FoldChange'],\n",
    "    -1 * np.log10( fin_df.loc[~filtered, 'padj'] ),\n",
    "    s=10.0, facecolor='lightgray', edgecolor='black',\n",
    "    linewidth=0.2, alpha=0.5\n",
    ")\n",
    "ax.scatter(\n",
    "    fin_df.loc[filtered, 'log2FoldChange'],\n",
    "    -1 * np.log10( fin_df.loc[filtered, 'padj'] ),\n",
    "    s=10.0, facecolor='crimson', edgecolor='black',\n",
    "    linewidth=0.2, alpha=0.5\n",
    ")\n",
    "\n",
    "ax.set_title('Volcano plot', fontsize=12.0)\n",
    "ax.set_xlabel('Log$_{2}$foldChange')\n",
    "ax.set_ylabel('$-1\\\\mathrm{Log}_{10}$FDR')\n",
    "\n",
    "path = 'output/fold_vs_padj.png'\n",
    "fig.savefig(path, transparent=False, dpi=300)\n",
    "Image.open(path).convert('RGB').save(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b459ec64-b66b-46c0-bfda-6ef0180a684b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}