[REFACTOR] Refactor all omics files.

omics/S2M2_Troppo_exercise_2.ipynb

@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "890101d30cf1fe37",
+   "metadata": {},
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from math import log\n",
+    "import cobra\n",
+    "import re\n",
+    "\n",
+    "from troppo.omics.readers.generic import TabularReader\n",
+    "from troppo.methods_wrappers import ModelBasedWrapper, ReconstructionWrapper\n",
+    "from troppo.omics.integration import ContinuousScoreIntegrationStrategy, CustomSelectionIntegrationStrategy\n",
+    "from troppo.methods.reconstruction.gimme import GIMME, GIMMEProperties\n",
+    "from cobamp.utilities.parallel import batch_run\n",
+    "from copy import deepcopy\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Exercise 2\n",
+    "\n",
+    "We have shown several types of omics data which can be used to reconstruct a tissue specific model. For this exercise, we will use the breast cancer cell lines present in the CCLE panel.\n",
+    "\n",
+    "a) Select the 'ACH-000019', 'ACH-000028', 'ACH-000349' samples. With them, reconstruct a tissue model for each with the FastCORE algorithm. Perform FBA and pFBA for all the 3 models reconstructed. Highlight the main differences between them."
+   ],
+   "id": "f30b4781ac8ae8aa"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a75f7d6a47fda8b",
+   "metadata": {},
+   "source": [
+    "Define the parsing rules for the GPRs that will be used later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "47487d35d3894174",
+   "metadata": {},
+   "source": [
+    "patt = re.compile('__COBAMPGPRDOT__[0-9]{1}')\n",
+    "replace_alt_transcripts = lambda x: patt.sub('', x)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2fa56f4053f8b2c",
+   "metadata": {},
+   "source": [
+    "#### Read model and omics data\n",
+    "\n",
+    "You have to download the .csv file containing the gene expression for breast cancer cell lines present in the CCLE panel. The nomenclature has been normalized for what is found in the metabolic model of this exercise."
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "model = cobra.io.load_matlab_model('redHUMAN_recon2_smin.mat')\n",
+    "model"
+   ],
+   "id": "85125e12e0ab2686",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "",
+   "id": "c7a0871e44476be3"
+  },
+  {
+   "cell_type": "code",
+   "id": "57d953123b778a92",
+   "metadata": {},
+   "source": "omics_data = pd.read_csv('CCLE_breast_cancer_expression.csv', index_col=0)  ",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-02T16:44:13.703802Z",
+     "start_time": "2024-07-02T16:44:13.681248Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "import cplex",
+   "id": "62d073cbb332e271",
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'cplex'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
+      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_78348\\3947162218.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[1;32mimport\u001B[0m \u001B[0mcplex\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'cplex'"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-02T16:43:27.099056Z",
+     "start_time": "2024-07-02T16:43:27.074057Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "import docplex",
+   "id": "472bc35bf94e2634",
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'docplex'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
+      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_78348\\2079162164.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[1;32mimport\u001B[0m \u001B[0mdocplex\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'docplex'"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "",
+   "id": "1e348470b486b94",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "b) Try to reproduce the Warburg Effect (if already not present). Use the `escher` library to view the metabolic pathway.\n",
+    "\n",
+    "Hint: Change the uptake of the oxygen drain to a small value. Also, our model is the redHUMAN reconstruction based on Recon2. However, some of the reaction names overlap with Recon1 and so we will use the central carbon metabolism map for that model."
+   ],
+   "id": "32cee16cd40a433b"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "220fa18772f76c56"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "833bb51ed0cdc61c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "c63e8d83f4d5fece"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "c) Select a random sample. Reconstruct 3 models using with different thresholds. What are the main differences?",
+   "id": "1ceef275964fbf9f"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "1b0551850e1a20f1"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "a3e61af13b4478f8"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "dd58ab00609fea3b"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}