Example_Simulation_Playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation Playground"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an interactive environment to see how the data can be formulted and to test the phenotype simulator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\textrm{Genetic Risk Score}_{p} = \\Sigma^{N}_{i=1} \\gamma_{ij}\\beta_{i}^{p} +\\epsilon_{p} \\\\\n",
    "\\textrm{p = patient index} \\ \\  \\textrm{N = number of Causal Snps} \\ \\ \\textrm{j = index of interacting snp partner} \\\\\n",
    "\\gamma=\\textrm{Interaction Coefficient} \\ \\ \\beta=\\textrm{Effect Size} \\ \\ \\epsilon=\\textrm{Optional Patient Specfic Bias}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  \\textrm{G} = \\frac{\\textrm{G} - \\overline{G}}{\\sigma_{G}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  \\textrm{G'}_{p} = h\\textrm{G}_{p} + \\sqrt{1-h^{2}}\\mathcal{N}(0,1) \\\\ \n",
    "\\textrm{h = Heritability Constant} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your size parameters\n",
    "num_snps = 1000\n",
    "num_genes = 100\n",
    "people = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chromosome</th>\n",
       "      <th>Feature ID</th>\n",
       "      <th>Position</th>\n",
       "      <th>Allele 1</th>\n",
       "      <th>Allele 2</th>\n",
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       "      <td>G</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>G</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>10996</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>10997</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>10998</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>10999</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Chromosome  Feature ID  Position Allele 1 Allele 2\n",
       "0             0           1     10000        A        T\n",
       "1             0          33     10001        A        G\n",
       "2             0          89     10002        C        T\n",
       "3             0          84     10003        A        T\n",
       "4             0          44     10004        A        G\n",
       "..          ...         ...       ...      ...      ...\n",
       "995           0          87     10995        A        G\n",
       "996           0          57     10996        C        T\n",
       "997           0          84     10997        C        T\n",
       "998           0          60     10998        A        T\n",
       "999           0          27     10999        A        T\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the SNPs\n",
    "feature_ids = np.random.randint(num_genes, size=num_snps)\n",
    "positions = np.arange(10000,10000 + num_snps)\n",
    "allele_1 = [np.random.choice(['A', 'C']) for _ in range (num_snps)]\n",
    "allele_2 = [np.random.choice(['T', 'G']) for _ in range (num_snps)]\n",
    "\n",
    "snplist = pd.DataFrame(list(zip([0]*len(feature_ids),feature_ids, positions,allele_1,allele_2)), columns = ['Chromosome','Feature ID', 'Position', 'Allele 1', 'Allele 2'])\n",
    "snplist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the genotypes\n",
    "genotype=[]\n",
    "for i in range(people):\n",
    "    person = np.random.binomial(2, 0.3, num_snps)\n",
    "    genotype.append(person)\n",
    "df = pd.DataFrame(genotype)\n",
    "risk_allele = [1 if df.iloc[:,idx].value_counts().to_list()[0] > df.shape[0]//2 else 0 for idx in range(0, df.shape[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>10995</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0</td>\n",
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       "      <td>10996</td>\n",
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       "      <th>997</th>\n",
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       "      <td>10997</td>\n",
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       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>10998</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>10999</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Chromosome  Feature ID  Position Allele 1 Allele 2  Risk Allele\n",
       "0             0           1     10000        A        T            0\n",
       "1             0          33     10001        A        G            0\n",
       "2             0          89     10002        C        T            0\n",
       "3             0          84     10003        A        T            1\n",
       "4             0          44     10004        A        G            0\n",
       "..          ...         ...       ...      ...      ...          ...\n",
       "995           0          87     10995        A        G            1\n",
       "996           0          57     10996        C        T            0\n",
       "997           0          84     10997        C        T            0\n",
       "998           0          60     10998        A        T            0\n",
       "999           0          27     10999        A        T            0\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snplist['Risk Allele'] = risk_allele\n",
    "snplist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save snplist\n",
    "snplist.to_csv(\"sample_data/snplist_{}.csv\".format(\"chr0_test\"), sep=\" \", index_label=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "<p>5 rows × 1000 columns</p>\n",
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      "text/plain": [
       "   0    1    2    3    4    5    6    7    8    9    ...  990  991  992  993  \\\n",
       "0    0    0    0    0    1    0    0    0    1    0  ...    0    1    1    0   \n",
       "1    0    1    0    1    0    1    0    0    1    0  ...    0    1    0    1   \n",
       "2    0    1    1    1    1    0    0    1    0    0  ...    1    0    0    0   \n",
       "3    0    1    0    0    0    0    1    0    2    0  ...    0    1    0    1   \n",
       "4    1    0    0    0    1    0    0    0    1    0  ...    2    0    2    0   \n",
       "\n",
       "   994  995  996  997  998  999  \n",
       "0    1    1    1    1    0    0  \n",
       "1    0    0    1    1    0    0  \n",
       "2    1    0    0    1    0    1  \n",
       "3    2    1    1    0    0    1  \n",
       "4    1    1    0    0    1    0  \n",
       "\n",
       "[5 rows x 1000 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format the genotype\n",
    "matrix = pd.DataFrame(np.array(genotype))\n",
    "matrix.shape # Person X SNPS\n",
    "matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the genotype\n",
    "matrix.to_csv(\"sample_data/genotype_{}.csv\".format(\"chr0_test\"), sep=\" \", index_label=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 2933.24it/s]\n"
     ]
    }
   ],
   "source": [
    "import tables\n",
    "import tqdm\n",
    "f = tables.open_file(\"./sample_data/\" + \"genotype_{}.h5\".format(\"chr0_test\"), mode='w')\n",
    "num_pat = matrix.shape[0]\n",
    "num_feat = matrix.shape[1]\n",
    "array_c = f.create_earray(f.root, 'data', tables.IntCol(), (0,num_feat), expectedrows=num_pat,filters=tables.Filters(complib='zlib', complevel=1))\n",
    "f.close()\n",
    "f = tables.open_file(\"./sample_data/\" + \"genotype_{}.h5\".format(\"chr0_test\"),  mode='a')\n",
    "for pat in tqdm.tqdm(range(num_pat)):\n",
    "    a = matrix.iloc[pat,:].to_numpy()\n",
    "    a=np.reshape(a, (1,-1))\n",
    "    f.root.data.append(a)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Main:  Namespace(case_frac=0.5, causal_gene_cut=0.05, causal_snp_mode='gene', config=None, data_identifier='chr0_test', data_path='./sample_data/', dominance_frac=0.1, heritability=0.8, interactive_cut=0.2, mask_rate=0.1, max_gene_risk=5.0, max_interaction_coeff=2.0, mode='phenotype', n_causal_snps=100, phenotype_experiment_name='playground_example', recessive_frac=0.1, stratify=False)\n",
      "SNPs: 1000 People: 100\n",
      "100 Phenotype Scores Closed\n",
      "Success!\n"
     ]
    }
   ],
   "source": [
    "# Simulate phenotypes with default parameters\n",
    "!gepsi phenotype -dp ./sample_data/ --data_identifier chr0_test --phenotype_experiment_name playground_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "causal_gene_name = \"causal_genes_chr0_test_playground_example.pkl\" \n",
    "causal_snp_name = \"causal_snp_idx_chr0_test_playground_example.pkl\" \n",
    "interactive_snp_name = \"interactive_snps_chr0_test_playground_example.pkl\"\n",
    "effect_size_name = \"effect_size_chr0_test_playground_example.pkl\"\n",
    "phenotype_name = \"phenotype_chr0_test_playground_example.pkl\"\n",
    "info_path = \"./sample_data/\"\n",
    "with open((info_path + causal_snp_name), 'rb') as f:\n",
    "            causal_snps = pickle.load(f)\n",
    "with open((info_path + interactive_snp_name), 'rb') as f:\n",
    "            interactive_snps = pickle.load(f)\n",
    "with open((info_path + causal_gene_name), 'rb') as f:\n",
    "            causal_genes = pickle.load(f)\n",
    "with open((info_path + effect_size_name), 'rb') as f:\n",
    "            effect_sizes = pickle.load(f)\n",
    "with open((info_path + phenotype_name), 'rb') as f:\n",
    "            phenotype = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "execution_count": 12,
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   ],
   "source": [
    "phenotype"
   ]
  }
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