gene_level_task_figure_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8dcefb48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import scipy.stats as stats\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import umap\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import pickle\n",
    "import sklearn\n",
    "import random\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "# import sentence_transformers\n",
    "plt.style.use('ggplot')\n",
    "#plt.style.use('seaborn-v0_8-dark-palette')\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"Helvetica\"\n",
    "})\n",
    "import matplotlib_inline\n",
    "matplotlib_inline.backend_inline.set_matplotlib_formats('retina')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eff63ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_info_table = pd.read_csv('/Users/yiquntchen/Downloads/gene_info_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d88de0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f\"{input_dir}/data_embedding/GPT_3_5_gene_embeddings.pickle\", \"rb\") as fp:\n",
    "    GPT_3_5_gene_embeddings = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d0adef16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create random embeddings as negative controls\n",
    "np.random.seed(2023)\n",
    "N_DIM = 1536\n",
    "fake_pretrain_embed = dict()\n",
    "for key in GPT_3_5_gene_embeddings.keys():\n",
    "    fake_pretrain_embed[key] = np.random.normal(size=(N_DIM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "142a0beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column \"gpt_embed\" with default value as False\n",
    "gene_info_table['gpt_embed'] = False\n",
    "\n",
    "# Update the new column based on the condition\n",
    "def check_embedding(row):\n",
    "    gene = row['gene_name']\n",
    "    if gene in GPT_3_5_gene_embeddings:\n",
    "        embed_value = GPT_3_5_gene_embeddings[gene]\n",
    "        if np.any(embed_value != 0):\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "gene_info_table['gpt_embed'] = gene_info_table.apply(check_embedding, axis=1)\n",
    "# Create a subset where gpt_embed is True\n",
    "subset = gene_info_table[gene_info_table['gpt_embed']]\n",
    "\n",
    "# Find gene_type elements with occurrences > 50 (in this case, just for demonstration, I'll use > 2)\n",
    "gene_type_counts = subset['gene_type'].value_counts()\n",
    "common_gene_types = gene_type_counts[gene_type_counts > 75].index\n",
    "\n",
    "# Create a final subset of the original DataFrame with only these gene_type elements\n",
    "final_subset = gene_info_table[(gene_info_table['gene_type'].isin(common_gene_types))\\\n",
    "                               &(gene_info_table['gpt_embed'])]\n",
    "\n",
    "# let's do a very simple classification for final_subset\n",
    "X_list = []\n",
    "y_list = []\n",
    "# Iterate through rows of final_subset\n",
    "for index, row in final_subset.iterrows():\n",
    "    gene_name = row['gene_name']\n",
    "    gene_type = row['gene_type']\n",
    "    # Add the embedding from the dictionary to the x list\n",
    "    if gene_name in GPT_3_5_gene_embeddings:\n",
    "        X_list.append(GPT_3_5_gene_embeddings[gene_name])\n",
    "    \n",
    "    # Add the gene_type to the y list\n",
    "        y_list.append(gene_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a7029531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size (X_train): 22686\n",
      "Test set size (X_test): 9723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yiquntchen/opt/miniconda3/envs/research/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(2023)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and test sets (80/20)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_list, y_list, test_size=0.30, random_state=2023)\n",
    "\n",
    "# Verify the split\n",
    "print(f\"Training set size (X_train): {len(X_train)}\")\n",
    "print(f\"Test set size (X_test): {len(X_test)}\")\n",
    "# 1. Logistic Regression\n",
    "lr = LogisticRegression(max_iter=100)\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred_lr = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f3e9cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9563920600637663"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(y_pred_lr,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2aa28d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get expression \n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def plot_normalized_confusion_matrix(y_true, y_pred, classes):\n",
    "    \"\"\"\n",
    "    Plot a normalized confusion matrix using seaborn.\n",
    "    \"\"\"\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    \n",
    "    # Normalize by row\n",
    "    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    cm_normalized = np.nan_to_num(cm_normalized)  # replace nan with 0\n",
    "    cm_normalized = cm_normalized * 100  # convert to percentage\n",
    "    \n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(cm_normalized, annot=True, fmt='.0f', annot_kws={'fontsize': 13.5},\n",
    "                cmap='Blues', xticklabels=classes, yticklabels=classes,cbar=False)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e902259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['protein_coding', 'snRNA', 'protein_coding', ..., 'miRNA',\n",
       "       'protein_coding', 'protein_coding'], dtype='<U20')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f097b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 834,
       "width": 993
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.set(font_scale=1.3)\n",
    "plot_normalized_confusion_matrix(np.array(y_test),y_pred_lr,np.unique(y_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11aa04ef",
   "metadata": {},
   "source": [
    "## Gene-gene interaction prediction\n",
    "#### We showcase the gene-gene interaction prediction use case below. The input data can be downloaded from https://github.com/jingcheng-du/Gene2vec repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "945d97df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gene gene interaction \n",
    "train_text_GGI = pd.read_csv('~/Downloads/Gene2vec/predictionData/train_text.txt',sep=' ',\n",
    "                            header=None)\n",
    "train_label_GGI = pd.read_csv('~/Downloads/Gene2vec/predictionData/train_label.txt',\n",
    "                              header=None)\n",
    "train_text_GGI.columns = ['gene_1','gene_2']\n",
    "train_label_GGI.columns = ['label']\n",
    "train_text_GGI_df = pd.concat([train_text_GGI,train_label_GGI], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "612054ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene gene interaction\n",
    "test_text_GGI = pd.read_csv('~/Downloads/Gene2vec/predictionData/test_text.txt',sep=' ',\n",
    "                            header=None)\n",
    "test_label_GGI = pd.read_csv('~/Downloads/Gene2vec/predictionData/test_label.txt',\n",
    "                            header=None)\n",
    "test_text_GGI.columns = ['gene_1','gene_2']\n",
    "test_label_GGI.columns = ['label']\n",
    "test_text_GGI_df = pd.concat([test_text_GGI,test_label_GGI], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439d6bfd",
   "metadata": {},
   "source": [
    "### We choose a simple featurization scheme by *adding* the emebdding vectors of the two genes together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9421f4b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_array_train = []\n",
    "y_array_train = []\n",
    "for i, row in train_text_GGI_df.iterrows():\n",
    "    if row['gene_1'] in  GPT_3_5_gene_embeddings \\\n",
    "        and row['gene_2'] in GPT_3_5_gene_embeddings:\n",
    "        X_array_train.append(GPT_3_5_gene_embeddings[row['gene_1']]+\\\n",
    "                             GPT_3_5_gene_embeddings[row['gene_2']])\n",
    "        y_array_train.append(row['label'])\n",
    "X_array_train = np.array(X_array_train)\n",
    "y_array_train = np.array(y_array_train)\n",
    "X_array_train = X_array_train.reshape(X_array_train.shape[0],-1)\n",
    "\n",
    "X_array_test = []\n",
    "y_array_test = []\n",
    "for i, row in test_text_GGI_df.iterrows():\n",
    "    if row['gene_1'] in  GPT_3_5_gene_embeddings \\\n",
    "        and row['gene_2'] in GPT_3_5_gene_embeddings:\n",
    "        X_array_test.append(GPT_3_5_gene_embeddings[row['gene_1']]+\\\n",
    "                             GPT_3_5_gene_embeddings[row['gene_2']])\n",
    "        y_array_test.append(row['label'])\n",
    "X_array_test = np.array(X_array_test)\n",
    "y_array_test = np.array(y_array_test)\n",
    "X_array_test = X_array_test.reshape(X_array_test.shape[0],-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d8e95f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# try the drug one...\n",
    "# how do we featurize \n",
    "# simplest way is to just take the union of the two input features\n",
    "X_array_train_random = []\n",
    "y_array_train_random = []\n",
    "for i, row in train_text_GGI_df.iterrows():\n",
    "    if row['gene_1'] in  fake_pretrain_embed \\\n",
    "        and row['gene_2'] in fake_pretrain_embed:\n",
    "#         X_array_train_random.append([fake_pretrain_embed[row['gene_1']],\\\n",
    "#                              fake_pretrain_embed[row['gene_2']]])\n",
    "        X_array_train_random.append(fake_pretrain_embed[row['gene_1']]+\n",
    "                             fake_pretrain_embed[row['gene_2']])\n",
    "        y_array_train_random.append(row['label'])\n",
    "X_array_train_random = np.array(X_array_train_random)\n",
    "y_array_train_random = np.array(y_array_train_random)\n",
    "X_array_train_random = X_array_train_random.reshape(X_array_train_random.shape[0],-1)\n",
    "\n",
    "# how do we featurize \n",
    "# simplest way is to just take the union of the two input features\n",
    "X_array_test_random = []\n",
    "y_array_test_random = []\n",
    "for i, row in test_text_GGI_df.iterrows():\n",
    "    if row['gene_1'] in  fake_pretrain_embed \\\n",
    "        and row['gene_2'] in fake_pretrain_embed:\n",
    "        X_array_test_random.append(fake_pretrain_embed[row['gene_1']]+\n",
    "                             fake_pretrain_embed[row['gene_2']])\n",
    "        y_array_test_random.append(row['label'])\n",
    "X_array_test_random = np.array(X_array_test_random)\n",
    "y_array_test_random = np.array(y_array_test_random)\n",
    "X_array_test_random = X_array_test_random.reshape(X_array_test_random.shape[0],-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "74acdf68",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene2vec_dim_200_iter_9 = pd.read_csv('~/Downloads/Gene2vec/pre_trained_emb/gene2vec_dim_200_iter_9.txt',\\\n",
    "                                      header=None,\n",
    "                                     sep='\\t')\n",
    "pretrain_embed = {}  # Dictionary to hold gene_name: 200-dimensional vector\n",
    "\n",
    "for index, row in gene2vec_dim_200_iter_9.iterrows():\n",
    "    gene_name = row[0]\n",
    "    vector_str = row[1]\n",
    "    vector = [float(x) for x in vector_str.split()]  \n",
    "    # Split the string into list of strings, then convert to floats\n",
    "    if len(vector) == 200:  # Ensure the row has exactly 200 numbers\n",
    "        pretrain_embed[gene_name] = np.array(vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "75f4d3be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# try the drug one...\n",
    "# how do we featurize \n",
    "# simplest way is to just take the union of the two input features\n",
    "X_array_train_gene2vec = []\n",
    "y_array_train_gene2vec = []\n",
    "for i, row in train_text_GGI_df.iterrows():\n",
    "    if row['gene_1'] in  pretrain_embed \\\n",
    "        and row['gene_2'] in pretrain_embed:\n",
    "        X_array_train_gene2vec.append(pretrain_embed[row['gene_1']]+\n",
    "                             pretrain_embed[row['gene_2']])\n",
    "        y_array_train_gene2vec.append(row['label'])\n",
    "X_array_train_gene2vec = np.array(X_array_train_gene2vec)\n",
    "y_array_train_gene2vec = np.array(y_array_train_gene2vec)\n",
    "X_array_train_gene2vec = X_array_train_gene2vec.reshape(X_array_train_gene2vec.shape[0],-1)\n",
    "\n",
    "\n",
    "X_array_test_gene2vec = []\n",
    "y_array_test_gene2vec = []\n",
    "for i, row in test_text_GGI_df.iterrows():\n",
    "    if row['gene_1'] in  pretrain_embed \\\n",
    "        and row['gene_2'] in pretrain_embed:\n",
    "        X_array_test_gene2vec.append(pretrain_embed[row['gene_1']]+\n",
    "                             pretrain_embed[row['gene_2']])\n",
    "        y_array_test_gene2vec.append(row['label'])\n",
    "X_array_test_gene2vec = np.array(X_array_test_gene2vec)\n",
    "y_array_test_gene2vec = np.array(y_array_test_gene2vec)\n",
    "X_array_test_gene2vec = X_array_test_gene2vec.reshape(X_array_test_gene2vec.shape[0],-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4bcc346",
   "metadata": {},
   "source": [
    "#### Replace the LogisticRegression() to RandomForest classifier to replace the results in the paper (slightly better AUC for GenePT and Gene2vec embeddings); we used the much faster LogisticRegression for illustration purposes here.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ce756384",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yiquntchen/opt/miniconda3/envs/research/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Using Random Forest for gene2vec data\n",
    "rf_gene2vec = LogisticRegression()  # You can add hyperparameters like n_estimators if needed\n",
    "rf_gene2vec.fit(X_array_train_gene2vec, y_array_train_gene2vec)\n",
    "y_pred_rf_gene2vec = rf_gene2vec.predict_proba(X_array_test_gene2vec)\n",
    "\n",
    "# Using Random Forest for random data\n",
    "rf_random = LogisticRegression()  # You can add hyperparameters like n_estimators if needed\n",
    "rf_random.fit(X_array_train_random, y_array_train_random)\n",
    "y_pred_rf_random = rf_random.predict_proba(X_array_test_random)\n",
    "\n",
    "# Using Random Forest for the GenePT data\n",
    "rf = LogisticRegression()  # You can add hyperparameters like n_estimators if needed\n",
    "rf.fit(X_array_train, y_array_train)\n",
    "y_pred_rf = rf.predict_proba(X_array_test)\n",
    "\n",
    "# Computing the ROC curve with _rf suffix\n",
    "fpr_rf, tpr_rf, _ = sklearn.metrics.roc_curve(y_array_test, y_pred_rf[:, 1])\n",
    "fpr_random_rf, tpr_random_rf, _ = sklearn.metrics.roc_curve(y_array_test_random, y_pred_rf_random[:, 1])\n",
    "fpr_gene2vec_rf, tpr_gene2vec_rf, _ = sklearn.metrics.roc_curve(y_array_test_gene2vec, y_pred_rf_gene2vec[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e13c3492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2e7d0cc70>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 521,
       "width": 722
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curves\n",
    "plt.figure()\n",
    "# Existing Logistic Regression curves\n",
    "# ... your existing plot code for Logistic Regression here ...\n",
    "plt.figure(figsize=(6,5))\n",
    "# Add Random Forest ROC curves\n",
    "plt.plot(fpr_rf, tpr_rf, color='blue', lw=2, \n",
    "         label='GenePT (AUC = %0.2f)' % (sklearn.metrics.roc_auc_score(y_array_test, y_pred_rf[:,1])))\n",
    "plt.plot(fpr_random_rf, tpr_random_rf, color='red', lw=2, \n",
    "         label='Random Embed (AUC = %0.2f)' % (sklearn.metrics.roc_auc_score(y_array_test_random, y_pred_rf_random[:,1])))\n",
    "plt.plot(fpr_gene2vec_rf, tpr_gene2vec_rf, color='cyan', lw=2, \n",
    "         label='Gene2vec (AUC = %0.2f)' % (sklearn.metrics.roc_auc_score(y_array_test_gene2vec, y_pred_rf_gene2vec[:,1])))\n",
    "\n",
    "# plt.plot([0, 1], [0, 1], color='navy', lw=2, 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('ROC for gene-gene interaction prediction with a random forest classifier')\n",
    "plt.legend(loc='upper left',bbox_to_anchor=(-0.3, -0.15),fontsize=11,ncol=3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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