heatmaps_template.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87047160-0548-4204-bf8d-8bd4b96b221c",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "# Refer to injected parameters for any run.ipynb (similar cell below)\n",
    "fname = 'R1158_FKS2-HS1_single_ortho_dox'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d977859c-8df7-4b13-a469-d465d2d514aa",
   "metadata": {},
   "source": [
    "# Notebook to generate heatmaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24208fc-2642-474d-b80d-aad69cd3eac4",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d8af0a1-b3c6-4fb6-bb11-fcdd9e107931",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf88b15-f543-4b78-b133-cd2ba3e76d1a",
   "metadata": {},
   "source": [
    "## Specify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4755a58f-90bc-4ded-aace-cfddf6111c23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "variantdata_path = 'dimsum_output/variant_data/'\n",
    "processed_data_outpath = 'processed_data/'\n",
    "aggdata_outpath = 'aggregated_data/'\n",
    "heatmapdata_outpath = 'heatmaps_data/'\n",
    "mgraph_outpath = 'manuscript_figures/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bafe362-c043-49cd-a443-a919c8d072cb",
   "metadata": {},
   "source": [
    "## Get conditions from filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fac2dd5c-ea21-4613-9a4f-0386c78dd1dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "f = variantdata_path + fname\n",
    "pos_offset = {'FKS1-HS1': 639,\n",
    "              'FKS1-HS2': 1353,\n",
    "              'FKS2-HS1': 659,\n",
    "              'FKS2-HS2': 1372\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88180490-dd8e-460e-aa6a-655ec63c28ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strain: R1158\n",
      "Locus: FKS2-HS1\n",
      "Pool type: single\n",
      "Compound: dox\n"
     ]
    }
   ],
   "source": [
    "cset = f.split('/')[-1].split('_') # Extract conditions from parameter\n",
    "cset_name = '_'.join(cset) # Basename based on conditions\n",
    "strain, locus, pool_type, compound = [cset[i] for i in (0,1,2,-1)]\n",
    "print(f'Strain: {strain}')\n",
    "print(f'Locus: {locus}')\n",
    "print(f'Pool type: {pool_type}')\n",
    "print(f'Compound: {compound}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad71d689-1210-492a-b651-edad39fcf88b",
   "metadata": {},
   "source": [
    "## Import dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9c556cf-6046-45cc-99e1-b0cb4a4b755d",
   "metadata": {
    "tags": []
   },
   "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>seq_type</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>combination</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>...</th>\n",
       "      <th>b3_log10</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>mutation_type</th>\n",
       "      <th>Log2FC_1</th>\n",
       "      <th>Log2FC_2</th>\n",
       "      <th>Log2FC_3</th>\n",
       "      <th>selcoeff_1</th>\n",
       "      <th>selcoeff_2</th>\n",
       "      <th>selcoeff_3</th>\n",
       "      <th>median_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>tttatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.133219</td>\n",
       "      <td>F</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-0.002757</td>\n",
       "      <td>-0.012074</td>\n",
       "      <td>-0.026116</td>\n",
       "      <td>-0.054743</td>\n",
       "      <td>-0.066107</td>\n",
       "      <td>-0.069617</td>\n",
       "      <td>-0.066107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>cttatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.992076</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.062013</td>\n",
       "      <td>0.054033</td>\n",
       "      <td>0.046275</td>\n",
       "      <td>0.010026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002773</td>\n",
       "      <td>0.002773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>single</td>\n",
       "      <td>ctgatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.910035</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.074188</td>\n",
       "      <td>0.063071</td>\n",
       "      <td>0.083820</td>\n",
       "      <td>0.022202</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>0.040318</td>\n",
       "      <td>0.022202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>attatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>att</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.712084</td>\n",
       "      <td>I</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-0.465586</td>\n",
       "      <td>-0.444260</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.517572</td>\n",
       "      <td>-0.498293</td>\n",
       "      <td>-0.382363</td>\n",
       "      <td>-0.498293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>atgatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.821455</td>\n",
       "      <td>M</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-0.014171</td>\n",
       "      <td>0.026332</td>\n",
       "      <td>-0.025110</td>\n",
       "      <td>-0.066157</td>\n",
       "      <td>-0.027701</td>\n",
       "      <td>-0.068612</td>\n",
       "      <td>-0.066157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>3.0</td>\n",
       "      <td>tca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>4.0</td>\n",
       "      <td>cta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>aga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>439 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0     single  tttatactgtcactaagagatcca     0.0        ttt            1.0   \n",
       "1     single  cttatactgtcactaagagatcca     0.0        ctt            1.0   \n",
       "2     single  ctgatactgtcactaagagatcca     0.0        ctg            1.0   \n",
       "3     single  attatactgtcactaagagatcca     0.0        att            1.0   \n",
       "4     single  atgatactgtcactaagagatcca     0.0        atg            1.0   \n",
       "..       ...                       ...     ...        ...            ...   \n",
       "434       WT  ctaatactgtcactaagagatcca     3.0        tca            NaN   \n",
       "435       WT  ctaatactgtcactaagagatcca     4.0        cta            NaN   \n",
       "436       WT  ctaatactgtcactaagagatcca     5.0        aga            NaN   \n",
       "437       WT  ctaatactgtcactaagagatcca     6.0        gat            NaN   \n",
       "438       WT  ctaatactgtcactaagagatcca     7.0        cca            NaN   \n",
       "\n",
       "     combination    aa_seq    WT   STOP STOP_readthrough  ...  b3_log10  \\\n",
       "0            NaN  FILSLRDP   NaN  False            False  ... -2.133219   \n",
       "1            NaN  LILSLRDP   NaN  False            False  ... -1.992076   \n",
       "2            NaN  LILSLRDP   NaN  False            False  ... -1.910035   \n",
       "3            NaN  IILSLRDP   NaN  False            False  ... -2.712084   \n",
       "4            NaN  MILSLRDP   NaN  False            False  ... -2.821455   \n",
       "..           ...       ...   ...    ...              ...  ...       ...   \n",
       "434          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "435          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "436          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "437          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "438          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "\n",
       "     alt_aa   mutation_type  Log2FC_1  Log2FC_2  Log2FC_3  selcoeff_1  \\\n",
       "0         F  non-synonymous -0.002757 -0.012074 -0.026116   -0.054743   \n",
       "1         L      synonymous  0.062013  0.054033  0.046275    0.010026   \n",
       "2         L      synonymous  0.074188  0.063071  0.083820    0.022202   \n",
       "3         I  non-synonymous -0.465586 -0.444260 -0.338862   -0.517572   \n",
       "4         M  non-synonymous -0.014171  0.026332 -0.025110   -0.066157   \n",
       "..      ...             ...       ...       ...       ...         ...   \n",
       "434       S      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "435       L      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "436       R      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "437       D      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "438       P      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "\n",
       "     selcoeff_2  selcoeff_3  median_s  \n",
       "0     -0.066107   -0.069617 -0.066107  \n",
       "1      0.000000    0.002773  0.002773  \n",
       "2      0.009038    0.040318  0.022202  \n",
       "3     -0.498293   -0.382363 -0.498293  \n",
       "4     -0.027701   -0.068612 -0.066157  \n",
       "..          ...         ...       ...  \n",
       "434    0.002886    0.031410  0.029241  \n",
       "435    0.002886    0.031410  0.029241  \n",
       "436    0.002886    0.031410  0.029241  \n",
       "437    0.002886    0.031410  0.029241  \n",
       "438    0.002886    0.031410  0.029241  \n",
       "\n",
       "[439 rows x 48 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "withSyndf = pd.read_csv(processed_data_outpath+cset_name+'/selcoeff_all_libraries.csv', index_col=0)\n",
    "wtaa = withSyndf.loc[withSyndf.WT == True, 'aa_seq'].values[0]\n",
    "withSyndf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d838d15-ff2b-4fc8-825a-4d147d487804",
   "metadata": {},
   "source": [
    "## Reshape dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91a37120-7a49-4492-a757-6c517172d888",
   "metadata": {
    "tags": []
   },
   "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>seq_type</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>combination</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>...</th>\n",
       "      <th>b3_log10</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>mutation_type</th>\n",
       "      <th>Log2FC_1</th>\n",
       "      <th>Log2FC_2</th>\n",
       "      <th>Log2FC_3</th>\n",
       "      <th>selcoeff_1</th>\n",
       "      <th>selcoeff_2</th>\n",
       "      <th>selcoeff_3</th>\n",
       "      <th>median_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>tttatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.133219</td>\n",
       "      <td>F</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-0.002757</td>\n",
       "      <td>-0.012074</td>\n",
       "      <td>-0.026116</td>\n",
       "      <td>-0.054743</td>\n",
       "      <td>-0.066107</td>\n",
       "      <td>-0.069617</td>\n",
       "      <td>-0.066107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>cttatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.992076</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.062013</td>\n",
       "      <td>0.054033</td>\n",
       "      <td>0.046275</td>\n",
       "      <td>0.010026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002773</td>\n",
       "      <td>0.002773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>single</td>\n",
       "      <td>ctgatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.910035</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.074188</td>\n",
       "      <td>0.063071</td>\n",
       "      <td>0.083820</td>\n",
       "      <td>0.022202</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>0.040318</td>\n",
       "      <td>0.022202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>attatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>att</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.712084</td>\n",
       "      <td>I</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-0.465586</td>\n",
       "      <td>-0.444260</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.517572</td>\n",
       "      <td>-0.498293</td>\n",
       "      <td>-0.382363</td>\n",
       "      <td>-0.498293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>atgatactgtcactaagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MILSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.821455</td>\n",
       "      <td>M</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-0.014171</td>\n",
       "      <td>0.026332</td>\n",
       "      <td>-0.025110</td>\n",
       "      <td>-0.066157</td>\n",
       "      <td>-0.027701</td>\n",
       "      <td>-0.068612</td>\n",
       "      <td>-0.066157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>3.0</td>\n",
       "      <td>tca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>4.0</td>\n",
       "      <td>cta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>aga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>WT</td>\n",
       "      <td>ctaatactgtcactaagagatcca</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LILSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.081228</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0     single  tttatactgtcactaagagatcca     0.0        ttt            1.0   \n",
       "1     single  cttatactgtcactaagagatcca     0.0        ctt            1.0   \n",
       "2     single  ctgatactgtcactaagagatcca     0.0        ctg            1.0   \n",
       "3     single  attatactgtcactaagagatcca     0.0        att            1.0   \n",
       "4     single  atgatactgtcactaagagatcca     0.0        atg            1.0   \n",
       "..       ...                       ...     ...        ...            ...   \n",
       "434       WT  ctaatactgtcactaagagatcca     3.0        tca            NaN   \n",
       "435       WT  ctaatactgtcactaagagatcca     4.0        cta            NaN   \n",
       "436       WT  ctaatactgtcactaagagatcca     5.0        aga            NaN   \n",
       "437       WT  ctaatactgtcactaagagatcca     6.0        gat            NaN   \n",
       "438       WT  ctaatactgtcactaagagatcca     7.0        cca            NaN   \n",
       "\n",
       "     combination    aa_seq    WT   STOP STOP_readthrough  ...  b3_log10  \\\n",
       "0            NaN  FILSLRDP   NaN  False            False  ... -2.133219   \n",
       "1            NaN  LILSLRDP   NaN  False            False  ... -1.992076   \n",
       "2            NaN  LILSLRDP   NaN  False            False  ... -1.910035   \n",
       "3            NaN  IILSLRDP   NaN  False            False  ... -2.712084   \n",
       "4            NaN  MILSLRDP   NaN  False            False  ... -2.821455   \n",
       "..           ...       ...   ...    ...              ...  ...       ...   \n",
       "434          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "435          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "436          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "437          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "438          NaN  LILSLRDP  True    NaN              NaN  ...       NaN   \n",
       "\n",
       "     alt_aa   mutation_type  Log2FC_1  Log2FC_2  Log2FC_3  selcoeff_1  \\\n",
       "0         F  non-synonymous -0.002757 -0.012074 -0.026116   -0.054743   \n",
       "1         L      synonymous  0.062013  0.054033  0.046275    0.010026   \n",
       "2         L      synonymous  0.074188  0.063071  0.083820    0.022202   \n",
       "3         I  non-synonymous -0.465586 -0.444260 -0.338862   -0.517572   \n",
       "4         M  non-synonymous -0.014171  0.026332 -0.025110   -0.066157   \n",
       "..      ...             ...       ...       ...       ...         ...   \n",
       "434       S      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "435       L      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "436       R      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "437       D      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "438       P      synonymous  0.081228  0.056919  0.074911    0.029241   \n",
       "\n",
       "     selcoeff_2  selcoeff_3  median_s  \n",
       "0     -0.066107   -0.069617 -0.066107  \n",
       "1      0.000000    0.002773  0.002773  \n",
       "2      0.009038    0.040318  0.022202  \n",
       "3     -0.498293   -0.382363 -0.498293  \n",
       "4     -0.027701   -0.068612 -0.066157  \n",
       "..          ...         ...       ...  \n",
       "434    0.002886    0.031410  0.029241  \n",
       "435    0.002886    0.031410  0.029241  \n",
       "436    0.002886    0.031410  0.029241  \n",
       "437    0.002886    0.031410  0.029241  \n",
       "438    0.002886    0.031410  0.029241  \n",
       "\n",
       "[206 rows x 48 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if pool_type == 'single':\n",
    "    withWTdf = withSyndf[withSyndf.seq_type.isin(['WT','single'])]\n",
    "else:\n",
    "    withWTdf = withSyndf[withSyndf.Nham_aa <=1]\n",
    "withWTdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7382b57-efa8-497a-8fce-ce183cfb4185",
   "metadata": {
    "papermill": {
     "duration": 0.00213,
     "end_time": "2024-02-14T15:04:01.474554",
     "exception": false,
     "start_time": "2024-02-14T15:04:01.472424",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Import validation mutants missing from the dataset (only for BY FKS1-HS1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7a3efac-244e-4f51-9344-f218419bbad6",
   "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>genotype</th>\n",
       "      <th>compound</th>\n",
       "      <th>estimated_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F639C</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>2.009071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F639C</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>1.918283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>F639C</td>\n",
       "      <td>micafungin</td>\n",
       "      <td>1.941420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F639C</td>\n",
       "      <td>none</td>\n",
       "      <td>0.122445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>L640D</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>-0.011160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>L640D</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>-0.076818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>L640D</td>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.706536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>L640D</td>\n",
       "      <td>none</td>\n",
       "      <td>0.060445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genotype       compound  estimated_s\n",
       "8     F639C  anidulafungin     2.009071\n",
       "9     F639C    caspofungin     1.918283\n",
       "10    F639C     micafungin     1.941420\n",
       "11    F639C           none     0.122445\n",
       "32    L640D  anidulafungin    -0.011160\n",
       "33    L640D    caspofungin    -0.076818\n",
       "34    L640D     micafungin     0.706536\n",
       "35    L640D           none     0.060445"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescued_df = pd.read_csv(f'{aggdata_outpath}/validation_DMS_missing_estimates.csv', index_col=0)[['genotype','compound','estimated_s']]\n",
    "rescued_df.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146786f2-63c0-459a-8297-843ec043ebab",
   "metadata": {
    "papermill": {
     "duration": 0.00213,
     "end_time": "2024-02-14T15:04:01.474554",
     "exception": false,
     "start_time": "2024-02-14T15:04:01.472424",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Pivot dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52aa6034-b0e6-40e6-b176-87746be3abe9",
   "metadata": {
    "papermill": {
     "duration": 0.011431,
     "end_time": "2024-02-14T15:03:57.724758",
     "exception": false,
     "start_time": "2024-02-14T15:03:57.713327",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "withWTdf_gby = withWTdf.groupby(['alt_aa','aa_pos','nt_seq'])[['median_s']].first().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae56477a-3834-4759-9b7d-e06e695a4f88",
   "metadata": {
    "papermill": {
     "duration": 0.019841,
     "end_time": "2024-02-14T15:04:01.532077",
     "exception": false,
     "start_time": "2024-02-14T15:04:01.512236",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "aa_single = withWTdf_gby.groupby(['alt_aa','aa_pos'])[['median_s']].median().reset_index()\n",
    "aa_single['aa_pos'] = aa_single['aa_pos'].astype(int) + pos_offset[locus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b42d363-ced3-4605-a57d-e3d58418da48",
   "metadata": {},
   "outputs": [],
   "source": [
    "if (strain == 'BY4741') & (locus == 'FKS1-HS1'):\n",
    "    rescued_filtered = rescued_df[(rescued_df.compound == compound)\n",
    "                                  & (rescued_df.genotype.isin(['F639C', 'L640D', 'L642G', 'P647N', 'P647Q']))\n",
    "                                 ]\n",
    "    rescued_filtered['wt_aa'], rescued_filtered['aa_pos'], rescued_filtered['alt_aa'] = zip(*rescued_filtered['genotype'].apply(lambda x: [x[0], int(x[1:-1]), x[-1]]))\n",
    "    rescued_filtered.rename(columns={'estimated_s':'median_s'}, inplace=True)\n",
    "else:\n",
    "    rescued_filtered = pd.DataFrame([], columns=['alt_aa','aa_pos','median_s'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09796d7f-8823-49f8-b31c-9df068b5ef74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2778/1998358398.py:1: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  aa_single_complete = pd.concat([aa_single, rescued_filtered[['alt_aa','aa_pos','median_s']]], ignore_index=True)\n"
     ]
    },
    {
     "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>alt_aa</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>median_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*</td>\n",
       "      <td>659</td>\n",
       "      <td>-1.071981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>*</td>\n",
       "      <td>660</td>\n",
       "      <td>-1.147315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>*</td>\n",
       "      <td>661</td>\n",
       "      <td>-1.178368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>*</td>\n",
       "      <td>662</td>\n",
       "      <td>-1.028843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>*</td>\n",
       "      <td>663</td>\n",
       "      <td>-1.146611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Y</td>\n",
       "      <td>662</td>\n",
       "      <td>-0.008727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Y</td>\n",
       "      <td>663</td>\n",
       "      <td>-0.082083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Y</td>\n",
       "      <td>664</td>\n",
       "      <td>-0.134748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Y</td>\n",
       "      <td>665</td>\n",
       "      <td>-0.030622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Y</td>\n",
       "      <td>666</td>\n",
       "      <td>-0.017623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    alt_aa aa_pos  median_s\n",
       "0        *    659 -1.071981\n",
       "1        *    660 -1.147315\n",
       "2        *    661 -1.178368\n",
       "3        *    662 -1.028843\n",
       "4        *    663 -1.146611\n",
       "..     ...    ...       ...\n",
       "139      Y    662 -0.008727\n",
       "140      Y    663 -0.082083\n",
       "141      Y    664 -0.134748\n",
       "142      Y    665 -0.030622\n",
       "143      Y    666 -0.017623\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa_single_complete = pd.concat([aa_single, rescued_filtered[['alt_aa','aa_pos','median_s']]], ignore_index=True)\n",
    "aa_single_complete.to_csv(f'{heatmapdata_outpath}/{cset_name}.csv')\n",
    "aa_single_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b7b04ca-f7aa-4b68-9fc7-be193cefdff3",
   "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>aa_pos</th>\n",
       "      <th>659</th>\n",
       "      <th>660</th>\n",
       "      <th>661</th>\n",
       "      <th>662</th>\n",
       "      <th>663</th>\n",
       "      <th>664</th>\n",
       "      <th>665</th>\n",
       "      <th>666</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alt_aa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <th>*</th>\n",
       "      <td>-1.071981</td>\n",
       "      <td>-1.147315</td>\n",
       "      <td>-1.178368</td>\n",
       "      <td>-1.028843</td>\n",
       "      <td>-1.146611</td>\n",
       "      <td>-1.198051</td>\n",
       "      <td>-0.166271</td>\n",
       "      <td>-1.040157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>-0.072346</td>\n",
       "      <td>-0.115527</td>\n",
       "      <td>0.034027</td>\n",
       "      <td>0.034545</td>\n",
       "      <td>-0.082667</td>\n",
       "      <td>-0.069276</td>\n",
       "      <td>0.012027</td>\n",
       "      <td>0.005089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-0.050954</td>\n",
       "      <td>-0.149469</td>\n",
       "      <td>-0.064492</td>\n",
       "      <td>-0.042676</td>\n",
       "      <td>-0.063969</td>\n",
       "      <td>-0.234549</td>\n",
       "      <td>-0.004419</td>\n",
       "      <td>-0.342350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.406350</td>\n",
       "      <td>0.001041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.144339</td>\n",
       "      <td>-0.033713</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>-0.044385</td>\n",
       "      <td>0.101199</td>\n",
       "      <td>0.011353</td>\n",
       "      <td>0.045691</td>\n",
       "      <td>0.017587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.021350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>-0.035875</td>\n",
       "      <td>-0.034094</td>\n",
       "      <td>-0.011804</td>\n",
       "      <td>-0.043441</td>\n",
       "      <td>-0.043961</td>\n",
       "      <td>-0.015121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>-0.010982</td>\n",
       "      <td>-0.005898</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>-0.001763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024846</td>\n",
       "      <td>0.006037</td>\n",
       "      <td>-0.027649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>-1.148666</td>\n",
       "      <td>-0.029615</td>\n",
       "      <td>-0.002976</td>\n",
       "      <td>-0.050053</td>\n",
       "      <td>0.005825</td>\n",
       "      <td>-0.118596</td>\n",
       "      <td>0.011768</td>\n",
       "      <td>-0.021599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.002453</td>\n",
       "      <td>-0.025835</td>\n",
       "      <td>-0.000742</td>\n",
       "      <td>0.038892</td>\n",
       "      <td>-0.406350</td>\n",
       "      <td>-0.037727</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>-0.204818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.008615</td>\n",
       "      <td>-0.029689</td>\n",
       "      <td>-1.178671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.134878</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>-1.106150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>-0.303233</td>\n",
       "      <td>0.092779</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>0.096775</td>\n",
       "      <td>-0.055747</td>\n",
       "      <td>-0.823357</td>\n",
       "      <td>-0.016948</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.284694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.505991</td>\n",
       "      <td>-0.088283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>-0.141111</td>\n",
       "      <td>0.069255</td>\n",
       "      <td>0.015166</td>\n",
       "      <td>-0.103451</td>\n",
       "      <td>-0.018206</td>\n",
       "      <td>0.017667</td>\n",
       "      <td>-0.080404</td>\n",
       "      <td>0.043834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>-0.519087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.050415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.054436</td>\n",
       "      <td>-0.216860</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>-0.120176</td>\n",
       "      <td>-0.013067</td>\n",
       "      <td>-0.013820</td>\n",
       "      <td>-0.008727</td>\n",
       "      <td>-0.082083</td>\n",
       "      <td>-0.134748</td>\n",
       "      <td>-0.030622</td>\n",
       "      <td>-0.017623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.066107</td>\n",
       "      <td>-0.034504</td>\n",
       "      <td>-0.076375</td>\n",
       "      <td>-0.013031</td>\n",
       "      <td>-0.147545</td>\n",
       "      <td>-0.129976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.186627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.066157</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>-0.018975</td>\n",
       "      <td>-0.002893</td>\n",
       "      <td>-0.040282</td>\n",
       "      <td>-0.009039</td>\n",
       "      <td>-0.008196</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.022202</td>\n",
       "      <td>0.013746</td>\n",
       "      <td>0.025216</td>\n",
       "      <td>0.003949</td>\n",
       "      <td>-0.008280</td>\n",
       "      <td>-0.020286</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>-0.027699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>-0.498293</td>\n",
       "      <td>0.021206</td>\n",
       "      <td>-0.067941</td>\n",
       "      <td>-0.045526</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>-0.023177</td>\n",
       "      <td>-0.009979</td>\n",
       "      <td>-0.306671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>-0.176058</td>\n",
       "      <td>0.001097</td>\n",
       "      <td>-0.029020</td>\n",
       "      <td>-0.025435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.149012</td>\n",
       "      <td>0.011692</td>\n",
       "      <td>-0.307867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.006535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028077</td>\n",
       "      <td>-0.199825</td>\n",
       "      <td>-0.062773</td>\n",
       "      <td>-0.136520</td>\n",
       "      <td>-0.016623</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "aa_pos       659       660       661       662       663       664       665  \\\n",
       "alt_aa                                                                         \n",
       "*      -1.071981 -1.147315 -1.178368 -1.028843 -1.146611 -1.198051 -0.166271   \n",
       "P      -0.072346 -0.115527  0.034027  0.034545 -0.082667 -0.069276  0.012027   \n",
       "G      -0.050954 -0.149469 -0.064492 -0.042676 -0.063969 -0.234549 -0.004419   \n",
       "C      -0.406350  0.001041       NaN       NaN       NaN -0.144339 -0.033713   \n",
       "Q      -0.044385  0.101199  0.011353  0.045691  0.017587       NaN  0.021350   \n",
       "N      -0.035875 -0.034094 -0.011804 -0.043441 -0.043961 -0.015121       NaN   \n",
       "T      -0.010982 -0.005898 -0.016408 -0.001763       NaN -0.024846  0.006037   \n",
       "S      -1.148666 -0.029615 -0.002976 -0.050053  0.005825 -0.118596  0.011768   \n",
       "E       0.002453 -0.025835 -0.000742  0.038892 -0.406350 -0.037727  0.011701   \n",
       "D      -0.008615 -0.029689 -1.178671       NaN       NaN -0.134878  0.029241   \n",
       "K      -0.303233  0.092779  0.007105  0.096775 -0.055747 -0.823357 -0.016948   \n",
       "H      -0.284694       NaN       NaN -0.505991 -0.088283       NaN       NaN   \n",
       "R      -0.141111  0.069255  0.015166 -0.103451 -0.018206  0.017667 -0.080404   \n",
       "W      -0.519087       NaN -0.050415       NaN -0.054436 -0.216860  0.033652   \n",
       "Y      -0.120176 -0.013067 -0.013820 -0.008727 -0.082083 -0.134748 -0.030622   \n",
       "F      -0.066107 -0.034504 -0.076375 -0.013031 -0.147545 -0.129976       NaN   \n",
       "M      -0.066157  0.002034 -0.018975 -0.002893 -0.040282 -0.009039 -0.008196   \n",
       "L       0.022202  0.013746  0.025216  0.003949 -0.008280 -0.020286  0.013884   \n",
       "I      -0.498293  0.021206 -0.067941 -0.045526 -0.045000 -0.023177 -0.009979   \n",
       "V      -0.176058  0.001097 -0.029020 -0.025435       NaN -0.149012  0.011692   \n",
       "A       0.006535       NaN  0.028077 -0.199825 -0.062773 -0.136520 -0.016623   \n",
       "\n",
       "aa_pos       666  \n",
       "alt_aa            \n",
       "*      -1.040157  \n",
       "P       0.005089  \n",
       "G      -0.342350  \n",
       "C            NaN  \n",
       "Q            NaN  \n",
       "N            NaN  \n",
       "T      -0.027649  \n",
       "S      -0.021599  \n",
       "E      -0.204818  \n",
       "D      -1.106150  \n",
       "K            NaN  \n",
       "H       0.010280  \n",
       "R       0.043834  \n",
       "W            NaN  \n",
       "Y      -0.017623  \n",
       "F      -0.186627  \n",
       "M            NaN  \n",
       "L      -0.027699  \n",
       "I      -0.306671  \n",
       "V      -0.307867  \n",
       "A            NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_aa = aa_single_complete.pivot(index='alt_aa', columns='aa_pos', values='median_s')\n",
    "\n",
    "aa_sort_order = '*PGCQNTSEDKHRWYFMLIVA'\n",
    "aa_sort_dic = dict(zip(list(aa_sort_order), list(range(0,len(aa_sort_order)))))\n",
    "wide_aa.sort_index(key=lambda x: x.map(aa_sort_dic), inplace=True)\n",
    "wide_aa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a498fb14-89f9-436c-b220-946ca811cf93",
   "metadata": {},
   "source": [
    "## Draw heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04ebd588-08c6-4c17-8e76-c0e52766ea18",
   "metadata": {
    "papermill": {
     "duration": 0.153744,
     "end_time": "2024-02-14T15:03:57.909510",
     "exception": false,
     "start_time": "2024-02-14T15:03:57.755766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 180x260 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "# Convert conditions to title labels\n",
    "title_dict = {'none':'Control',\n",
    "              'dox':'Doxycycline control\\n(Paralog repressed)',\n",
    "              'anidulafungin':'Anidulafungin',\n",
    "              'caspofungin':'Caspofungin',\n",
    "              'micafungin':'Micafungin'\n",
    "             }\n",
    "\n",
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(1.8, 2.6),\n",
    "              'font.size':8,\n",
    "              'legend.title_fontsize':8, 'legend.fontsize':8,\n",
    "              'axes.labelsize':8,'axes.titlesize':8,\n",
    "              'xtick.labelsize':8, 'ytick.labelsize':8,\n",
    "             },\n",
    "       style='whitegrid')\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "# Custom color palette\n",
    "ccmap = sns.color_palette(\"blend:#009B9E,#42B7B9,#A7D3D4,#F1F1F1,#E4C1D9,#D691C1,#C75DAB\", # CARTOColors Tropic\n",
    "                          as_cmap=True)\n",
    "ccmap.set_bad('.5') # Color for missing values\n",
    "\n",
    "# Mask for missing values\n",
    "mask = pd.isnull(wide_aa)\n",
    "\n",
    "# Draw heatmap\n",
    "ax = sns.heatmap(wide_aa, mask=mask, \n",
    "                 cmap=ccmap,\n",
    "                 vmin=-2, center=0, vmax=2,\n",
    "                 #xticklabels=len(wtaa)-1,\n",
    "                 xticklabels=2,\n",
    "                 yticklabels=False,\n",
    "                 cbar_kws={'label':'Selection coefficient',\n",
    "                           'shrink':.7\n",
    "                          }\n",
    "                )\n",
    "\n",
    "ax.set_title(title_dict[compound])\n",
    "ax.set(xlabel=None, ylabel=None)\n",
    "#ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='center')\n",
    "ax.tick_params(axis='x', length=3)\n",
    "\n",
    "cax = ax.figure.axes[-1]\n",
    "cax.tick_params(length=3, pad=2)\n",
    "\n",
    "# Coordinates of WT protein sequence on the heatmap are extracted from the dataframe\n",
    "# Coordinates should be OK even if the dataframe contains missing values\n",
    "wtcoord_aa = [(i+0.5, list(aa_sort_order).index(v)+0.5) for i,v in enumerate(wtaa)]\n",
    "\n",
    "for o in wtcoord_aa:\n",
    "    ax.plot(o[0],o[1], marker='o', color='.5', markersize=4) # displays WT sequence\n",
    "\n",
    "# Variants for which we inferred the DMS score\n",
    "#estimate_coord = [(a+0.75-pos_offset[locus], list(aa_sort_order).index(b)+0.25) for a, b in zip(rescued_filtered.aa_pos, rescued_filtered.alt_aa)]\n",
    "\n",
    "#for m in estimate_coord:\n",
    "#    ax.plot(m[0],m[1], marker='*', color='.5', markersize=2)\n",
    "\n",
    "# Graphs are output in the specified folder\n",
    "plt.savefig(mgraph_outpath+cset_name+'_aa_heatmap_median_s.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "268a0b30-5d2f-4cea-8788-030fdb867074",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dms-env",
   "language": "python",
   "name": "dms-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}