Cas13_Lasso.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc70f2d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import logomaker\n",
    "\n",
    "import matplotlib.font_manager\n",
    "plt.rcParams.update({'font.size': 18, 'font.weight':'normal'})\n",
    "plt.rcParams.update({'font.family' : 'Arial','font.sans-serif':['Arial']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab3fba45",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sgrna</th>\n",
       "      <th>sgrna_seq</th>\n",
       "      <th>target_seq</th>\n",
       "      <th>extended_target_seq</th>\n",
       "      <th>LFC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1_196959066_196959088_-_2361</td>\n",
       "      <td>CATTGATTCTATTTTTCTTCAC</td>\n",
       "      <td>GTGAAGAAAAATAGAATCAATG</td>\n",
       "      <td>TATCCCAGTTGTGAAGAAAAATAGAATCAATGGCATTACTAT</td>\n",
       "      <td>-1.95270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1_196958980_196959002_-_2362</td>\n",
       "      <td>ATATCCAGATTTACAAACAAAT</td>\n",
       "      <td>ATTTGTTTGTAAATCTGGATAT</td>\n",
       "      <td>ACATAGTTGAATTTGTTTGTAAATCTGGATATCATCCAACAA</td>\n",
       "      <td>-1.88440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1_196949614_196949636_-_2363</td>\n",
       "      <td>TGTTGGTGACCATCCTTCTTCT</td>\n",
       "      <td>AGAAGAAGGATGGTCACCAACA</td>\n",
       "      <td>TAACGTGCGCAGAAGAAGGATGGTCACCAACACCAAAGTGTC</td>\n",
       "      <td>1.27680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1_196959107_196959129_-_2364</td>\n",
       "      <td>GTAAATTCAGAAAAAGGTGTGC</td>\n",
       "      <td>GCACACCTTTTTCTGAATTTAC</td>\n",
       "      <td>TTAGTAAAATGCACACCTTTTTCTGAATTTACTATTATATTT</td>\n",
       "      <td>1.27040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1_196957937_196957959_-_2365</td>\n",
       "      <td>CTGACAACAGGAATGAAGTAAT</td>\n",
       "      <td>ATTACTTCATTCCTGTTGTCAG</td>\n",
       "      <td>CAATGGAGACATTACTTCATTCCTGTTGTCAGTATATGCTCC</td>\n",
       "      <td>0.57566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>chr14_37592731_37592753_+_10474</td>\n",
       "      <td>GAAGAAGCCCCGGAGGGCGGGG</td>\n",
       "      <td>CCCCGCCCTCCGGGGCTTCTTC</td>\n",
       "      <td>CCACCACTAACCCCGCCCTCCGGGGCTTCTTCACTCGCTGTC</td>\n",
       "      <td>1.69290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>chr14_37594094_37594116_+_10475</td>\n",
       "      <td>TCCCTCTTCCCAAGATTATCCA</td>\n",
       "      <td>TGGATAATCTTGGGAAGAGGGA</td>\n",
       "      <td>GGAACTGCCTTGGATAATCTTGGGAAGAGGGAAAGAAAATAA</td>\n",
       "      <td>3.75330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>chr14_37593671_37593693_+_10476</td>\n",
       "      <td>CTTAATTGAATGGTGGGGTCGA</td>\n",
       "      <td>TCGACCCCACCATTCAATTAAG</td>\n",
       "      <td>CGCAGTCATCTCGACCCCACCATTCAATTAAGACCCCTAGCG</td>\n",
       "      <td>-1.18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>chr14_37593235_37593257_+_10477</td>\n",
       "      <td>TTGTAGCAACTGTAATGTTAAA</td>\n",
       "      <td>TTTAACATTACAGTTGCTACAA</td>\n",
       "      <td>TTGCTTCCCTTTTAACATTACAGTTGCTACAAGCAGGTAGAA</td>\n",
       "      <td>-0.13608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>chr14_37593981_37594003_+_10478</td>\n",
       "      <td>AACTTGTGAATACATAAATCTA</td>\n",
       "      <td>TAGATTTATGTATTCACAAGTT</td>\n",
       "      <td>CTGACTTATTTAGATTTATGTATTCACAAGTTTACCTCTGAG</td>\n",
       "      <td>-1.86340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               sgrna               sgrna_seq  \\\n",
       "0    chr1_196959066_196959088_-_2361  CATTGATTCTATTTTTCTTCAC   \n",
       "1    chr1_196958980_196959002_-_2362  ATATCCAGATTTACAAACAAAT   \n",
       "2    chr1_196949614_196949636_-_2363  TGTTGGTGACCATCCTTCTTCT   \n",
       "3    chr1_196959107_196959129_-_2364  GTAAATTCAGAAAAAGGTGTGC   \n",
       "4    chr1_196957937_196957959_-_2365  CTGACAACAGGAATGAAGTAAT   \n",
       "..                               ...                     ...   \n",
       "395  chr14_37592731_37592753_+_10474  GAAGAAGCCCCGGAGGGCGGGG   \n",
       "396  chr14_37594094_37594116_+_10475  TCCCTCTTCCCAAGATTATCCA   \n",
       "397  chr14_37593671_37593693_+_10476  CTTAATTGAATGGTGGGGTCGA   \n",
       "398  chr14_37593235_37593257_+_10477  TTGTAGCAACTGTAATGTTAAA   \n",
       "399  chr14_37593981_37594003_+_10478  AACTTGTGAATACATAAATCTA   \n",
       "\n",
       "                 target_seq                         extended_target_seq  \\\n",
       "0    GTGAAGAAAAATAGAATCAATG  TATCCCAGTTGTGAAGAAAAATAGAATCAATGGCATTACTAT   \n",
       "1    ATTTGTTTGTAAATCTGGATAT  ACATAGTTGAATTTGTTTGTAAATCTGGATATCATCCAACAA   \n",
       "2    AGAAGAAGGATGGTCACCAACA  TAACGTGCGCAGAAGAAGGATGGTCACCAACACCAAAGTGTC   \n",
       "3    GCACACCTTTTTCTGAATTTAC  TTAGTAAAATGCACACCTTTTTCTGAATTTACTATTATATTT   \n",
       "4    ATTACTTCATTCCTGTTGTCAG  CAATGGAGACATTACTTCATTCCTGTTGTCAGTATATGCTCC   \n",
       "..                      ...                                         ...   \n",
       "395  CCCCGCCCTCCGGGGCTTCTTC  CCACCACTAACCCCGCCCTCCGGGGCTTCTTCACTCGCTGTC   \n",
       "396  TGGATAATCTTGGGAAGAGGGA  GGAACTGCCTTGGATAATCTTGGGAAGAGGGAAAGAAAATAA   \n",
       "397  TCGACCCCACCATTCAATTAAG  CGCAGTCATCTCGACCCCACCATTCAATTAAGACCCCTAGCG   \n",
       "398  TTTAACATTACAGTTGCTACAA  TTGCTTCCCTTTTAACATTACAGTTGCTACAAGCAGGTAGAA   \n",
       "399  TAGATTTATGTATTCACAAGTT  CTGACTTATTTAGATTTATGTATTCACAAGTTTACCTCTGAG   \n",
       "\n",
       "         LFC  \n",
       "0   -1.95270  \n",
       "1   -1.88440  \n",
       "2    1.27680  \n",
       "3    1.27040  \n",
       "4    0.57566  \n",
       "..       ...  \n",
       "395  1.69290  \n",
       "396  3.75330  \n",
       "397 -1.18000  \n",
       "398 -0.13608  \n",
       "399 -1.86340  \n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_input = pd.read_csv('data/sgrna_seq.csv')\n",
    "df_input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033742f2",
   "metadata": {},
   "source": [
    "## Generate Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acb85dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from scipy.special import expit\n",
    "from Bio.SeqUtils import MeltingTemp as mt\n",
    "\n",
    "base_encoder1 = {\n",
    "  'A': [1, 0, 0, 0],\n",
    "  'C': [0, 1, 0, 0],\n",
    "  'G': [0, 0, 1, 0],\n",
    "  'T': [0, 0, 0, 1]}\n",
    "\n",
    "base_encoder2 = {\n",
    "  'AA': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'AC': [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'AG': [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'AT': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'CA': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'CC': [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'CG': [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'CT': [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'GA': [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
    "  'GC': [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
    "  'GG': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "  'GT': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
    "  'TA': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
    "  'TC': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
    "  'TG': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
    "  'TT': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d55bcdd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oht_0_A</th>\n",
       "      <th>oht_0_C</th>\n",
       "      <th>oht_0_G</th>\n",
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       "      <th>oht_2_A</th>\n",
       "      <th>oht_2_C</th>\n",
       "      <th>...</th>\n",
       "      <th>cnt_CG_target</th>\n",
       "      <th>cnt_CT_target</th>\n",
       "      <th>cnt_GA_target</th>\n",
       "      <th>cnt_GC_target</th>\n",
       "      <th>cnt_GG_target</th>\n",
       "      <th>cnt_GT_target</th>\n",
       "      <th>cnt_TA_target</th>\n",
       "      <th>cnt_TC_target</th>\n",
       "      <th>cnt_TG_target</th>\n",
       "      <th>cnt_TT_target</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 195 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     oht_0_A  oht_0_C  oht_0_G  oht_0_T  oht_1_A  oht_1_C  oht_1_G  oht_1_T  \\\n",
       "0        0.0      0.0      0.0      1.0      1.0      0.0      0.0      0.0   \n",
       "1        1.0      0.0      0.0      0.0      0.0      1.0      0.0      0.0   \n",
       "2        0.0      0.0      0.0      1.0      1.0      0.0      0.0      0.0   \n",
       "3        0.0      0.0      0.0      1.0      0.0      0.0      0.0      1.0   \n",
       "4        0.0      1.0      0.0      0.0      1.0      0.0      0.0      0.0   \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "395      0.0      1.0      0.0      0.0      0.0      1.0      0.0      0.0   \n",
       "396      0.0      0.0      1.0      0.0      0.0      0.0      1.0      0.0   \n",
       "397      0.0      1.0      0.0      0.0      0.0      0.0      1.0      0.0   \n",
       "398      0.0      0.0      0.0      1.0      0.0      0.0      0.0      1.0   \n",
       "399      0.0      1.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "\n",
       "     oht_2_A  oht_2_C  ...  cnt_CG_target  cnt_CT_target  cnt_GA_target  \\\n",
       "0        0.0      0.0  ...            0.0            1.0            3.0   \n",
       "1        1.0      0.0  ...            0.0            1.0            2.0   \n",
       "2        1.0      0.0  ...            2.0            0.0            3.0   \n",
       "3        1.0      0.0  ...            0.0            3.0            1.0   \n",
       "4        1.0      0.0  ...            0.0            3.0            2.0   \n",
       "..       ...      ...  ...            ...            ...            ...   \n",
       "395      1.0      0.0  ...            3.0            6.0            0.0   \n",
       "396      1.0      0.0  ...            0.0            3.0            6.0   \n",
       "397      0.0      1.0  ...            3.0            2.0            2.0   \n",
       "398      0.0      0.0  ...            0.0            3.0            1.0   \n",
       "399      0.0      0.0  ...            0.0            4.0            3.0   \n",
       "\n",
       "     cnt_GC_target  cnt_GG_target  cnt_GT_target  cnt_TA_target  \\\n",
       "0              1.0            1.0            2.0            4.0   \n",
       "1              0.0            1.0            3.0            3.0   \n",
       "2              2.0            2.0            4.0            1.0   \n",
       "3              1.0            0.0            1.0            6.0   \n",
       "4              1.0            1.0            3.0            3.0   \n",
       "..             ...            ...            ...            ...   \n",
       "395            3.0            2.0            1.0            1.0   \n",
       "396            1.0            4.0            0.0            2.0   \n",
       "397            2.0            0.0            1.0            2.0   \n",
       "398            3.0            1.0            2.0            4.0   \n",
       "399            0.0            0.0            2.0            5.0   \n",
       "\n",
       "     cnt_TC_target  cnt_TG_target  cnt_TT_target  \n",
       "0              2.0            3.0            2.0  \n",
       "1              3.0            4.0            3.0  \n",
       "2              2.0            3.0            0.0  \n",
       "3              1.0            2.0            6.0  \n",
       "4              4.0            4.0            4.0  \n",
       "..             ...            ...            ...  \n",
       "395            5.0            1.0            2.0  \n",
       "396            1.0            3.0            2.0  \n",
       "397            4.0            0.0            2.0  \n",
       "398            1.0            2.0            6.0  \n",
       "399            2.0            3.0            5.0  \n",
       "\n",
       "[400 rows x 195 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target_feature = pd.DataFrame()\n",
    "for idx in df_input.index:\n",
    "    target = df_input.loc[idx, 'extended_target_seq']\n",
    "    target = target + 'N'*(33-len(target))\n",
    "    ## 1. one-hot coding\n",
    "    for k1, v1 in enumerate(target):\n",
    "        lst_coding = base_encoder1[v1]\n",
    "        df_target_feature.loc[idx, 'oht_'+str(k1)+'_A'] = lst_coding[0]\n",
    "        df_target_feature.loc[idx, 'oht_'+str(k1)+'_C'] = lst_coding[1]\n",
    "        df_target_feature.loc[idx, 'oht_'+str(k1)+'_G'] = lst_coding[2]\n",
    "        df_target_feature.loc[idx, 'oht_'+str(k1)+'_T'] = lst_coding[3]\n",
    "    ## 2. tm\n",
    "    df_target_feature.loc[idx, 'Tm_target'] = mt.Tm_NN(target)\n",
    "    df_target_feature.loc[idx, 'Tm_target_0_5'] = mt.Tm_NN(target[0:5])\n",
    "    df_target_feature.loc[idx, 'Tm_target_5_10'] = mt.Tm_NN(target[5:10])\n",
    "    df_target_feature.loc[idx, 'Tm_target_10_15'] = mt.Tm_NN(target[10:15])\n",
    "    df_target_feature.loc[idx, 'Tm_target_15_22'] = mt.Tm_NN(target[15:])\n",
    "    df_target_feature.loc[idx, 'Tm_target_0_10'] = mt.Tm_NN(target[0:10])\n",
    "    df_target_feature.loc[idx, 'Tm_target_10_22'] = mt.Tm_NN(target[10:])\n",
    "    ## 3. count\n",
    "    df_target_feature.loc[idx, 'cnt_A_target'] = target.count('A')\n",
    "    df_target_feature.loc[idx, 'cnt_C_target'] = target.count('C')\n",
    "    df_target_feature.loc[idx, 'cnt_G_target'] = target.count('G')\n",
    "    df_target_feature.loc[idx, 'cnt_T_target'] = target.count('T')\n",
    "    df_target_feature.loc[idx, 'cnt_AA_target'] = target.count('AA')\n",
    "    df_target_feature.loc[idx, 'cnt_AC_target'] = target.count('AC')\n",
    "    df_target_feature.loc[idx, 'cnt_AG_target'] = target.count('AG')\n",
    "    df_target_feature.loc[idx, 'cnt_AT_target'] = target.count('AT')\n",
    "    df_target_feature.loc[idx, 'cnt_CA_target'] = target.count('CA')\n",
    "    df_target_feature.loc[idx, 'cnt_CC_target'] = target.count('CC')\n",
    "    df_target_feature.loc[idx, 'cnt_CG_target'] = target.count('CG')\n",
    "    df_target_feature.loc[idx, 'cnt_CT_target'] = target.count('CT')\n",
    "    df_target_feature.loc[idx, 'cnt_GA_target'] = target.count('GA')\n",
    "    df_target_feature.loc[idx, 'cnt_GC_target'] = target.count('GC')\n",
    "    df_target_feature.loc[idx, 'cnt_GG_target'] = target.count('GG')\n",
    "    df_target_feature.loc[idx, 'cnt_GT_target'] = target.count('GT')\n",
    "    df_target_feature.loc[idx, 'cnt_TA_target'] = target.count('TA')\n",
    "    df_target_feature.loc[idx, 'cnt_TC_target'] = target.count('TC')\n",
    "    df_target_feature.loc[idx, 'cnt_TG_target'] = target.count('TG')\n",
    "    df_target_feature.loc[idx, 'cnt_TT_target'] = target.count('TT')\n",
    "    \n",
    "df_target_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6192185f",
   "metadata": {},
   "source": [
    "## Lasso Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f76af2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# functions to train lasso model\n",
    "def train_lasso_model(x_pred,y_real,alpha=0.005,plot=False):\n",
    "    clf_lin_r = linear_model.Lasso(alpha=alpha)\n",
    "    clf_lin_r.fit(x_pred, y_real)\n",
    "    \n",
    "    y_pred=clf_lin_r.predict(x_pred)\n",
    "\n",
    "    # test just on training data\n",
    "#     fpr,tpr,_=roc_curve(y_real,y_pred)\n",
    "#     roc_auc=auc(fpr,tpr)\n",
    "#     print('train auc:'+str(roc_auc))\n",
    "    if plot:\n",
    "        plot_auc(fpr,tpr)\n",
    "    \n",
    "    return (clf_lin_r,y_pred)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# convert sequence features to pandas matrix\n",
    "def get_seq_features(clf_lin,nt_for_feature):\n",
    "    # \n",
    "    weight_mat=clf_lin.coef_[:nt_for_feature*4].reshape(nt_for_feature,4)\n",
    "    \n",
    "    weight_mat_p=pd.DataFrame(weight_mat)\n",
    "    weight_mat_p=weight_mat_p.rename(columns={0:'A',1:'C',2:'G',3:'T'})\n",
    "    return weight_mat_p\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "def plot_weight_mat(weight_mat_p, roc_auc):\n",
    "    plt.figure(figsize = (8,16))\n",
    "    sns.heatmap(weight_mat_p, annot=True,cmap='bwr',center=0)\n",
    "    plt.title(fname+' auc: %0.2f' % roc_auc)\n",
    "\n",
    "# function definition\n",
    "def plot_auc(false_positive,true_positive):\n",
    "    from sklearn.metrics import roc_curve, auc\n",
    "    roc_auc=auc(false_positive,true_positive)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    lw = 2\n",
    "    plt.plot(false_positive, true_positive, color='darkorange',\n",
    "             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "905d2e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xcheng/opt/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.03815236218637352, tolerance: 0.0078871875\n",
      "  positive)\n",
      "/Users/xcheng/opt/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.035683878169152194, tolerance: 0.007955000000000002\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test auc:0.8232323232323233\n",
      " \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xcheng/opt/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11387628821726281, tolerance: 0.009831000000000003\n",
      "  positive)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "nt_for_feature = 42\n",
    "\n",
    "lst_color = ['tab:orange', 'tab:red', 'tab:blue', 'tab:pink', 'tab:brown']\n",
    "\n",
    "fea_m_pd = df_target_feature\n",
    "fea_m_pd = fea_m_pd.fillna(0)\n",
    "lst_indx = list(fea_m_pd.index)\n",
    "lst_y = [1 if i < -0.5 else 0 for i in df_input.loc[lst_indx, 'LFC'].to_list()]\n",
    "\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "df = pd.DataFrame(columns = ['sgrna', 'y', 'score'])\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "lw = 3\n",
    "num = 0\n",
    "for train, test in kf.split(fea_m_pd):\n",
    "    ##\n",
    "    X_train = fea_m_pd.iloc[list(train),:]\n",
    "    X_test = fea_m_pd.iloc[list(test),:]\n",
    "    y_train = [lst_y[i] for i in train]\n",
    "    y_test = [lst_y[i] for i in test]\n",
    "    ##\n",
    "    (clf_lin_r,y_pred)=train_lasso_model(X_train,y_train,plot=False)\n",
    "    # specific guide predictor\n",
    "    clf_lin_r_S=clf_lin_r\n",
    "    y_pred_test=clf_lin_r.predict(X_test)\n",
    "    for k in range(len(list(test))):\n",
    "        df.loc[lst_indx[k], 'sgrna'] = lst_indx[k]\n",
    "        df.loc[lst_indx[k], 'y'] = y_test[k]\n",
    "        df.loc[lst_indx[k], 'score'] = y_pred_test[k]\n",
    "    fpr,tpr,_=roc_curve(y_test,y_pred_test)\n",
    "    roc_auc=auc(fpr,tpr)\n",
    "    plt.plot(fpr,tpr, color=lst_color[num],\n",
    "             lw=lw, label='Fold %d (AUC = %0.2f)' % (num, roc_auc))\n",
    "    num = num +1\n",
    "    \n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "\n",
    "\n",
    "##\n",
    "fpr,tpr,_=roc_curve(df.y.to_list(),df.score.to_list())\n",
    "roc_auc=auc(fpr,tpr)\n",
    "print('test auc:'+str(roc_auc))\n",
    "print(' ')\n",
    "\n",
    "(clf_lin_r,y_pred)=train_lasso_model(fea_m_pd,lst_y,plot=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "789a8ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight_mat_pd=get_seq_features(clf_lin_r, nt_for_feature)\n",
    "\n",
    "df_lasso_weight_target_ext = weight_mat_pd.copy()\n",
    "\n",
    "weight_mat_pd_tmp = weight_mat_pd.copy()\n",
    "\n",
    "plt.figure(figsize=(40, 6))\n",
    "# create Logo object\n",
    "g = logomaker.Logo(weight_mat_pd_tmp,\n",
    "                          shade_below=.5,\n",
    "                          fade_below=.5)\n",
    "# style using Logo methods\n",
    "g.style_spines(visible=False)\n",
    "g.style_spines(spines=['left', 'bottom'], visible=True)\n",
    "g.style_xticks(rotation=90, fmt='%d', anchor=0, fontsize=16)\n",
    "# style using Axes methods\n",
    "g.ax.set_ylabel(\"weight\", labelpad=-1)\n",
    "g.ax.xaxis.set_ticks_position('none')\n",
    "g.ax.xaxis.set_tick_params(pad=-1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01f0a4aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}