Merge pull request #23 from soelmicheletti/main

Corrected file path in cobra

Author: marouenbg

netbooks/netZooPy/cobra.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "7e26ed88",
    "metadata": {},
    "source": [
     "# Decomposing gene co-expression networks with COBRA (Python version)\n",
@@ -12,6 +13,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8520e757",
    "metadata": {},
    "source": [
     "## 1. Introduction\n",
@@ -29,6 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "fe567bf2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,6 +40,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c27aff27",
    "metadata": {},
    "source": [
     "On the server, we need to change the working directory to the `data` folder of the current useer."
@@ -45,6 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7e5dc5ad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,6 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "402463e2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,18 +72,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "1f43ac8c",
    "metadata": {},
    "outputs": [],
    "source": [
     "gene_expression = pd.read_csv(ppath+\"gene_expression_thca.csv\", index_col = 0).to_numpy()\n",
-    "metadata = pd.read_csv(ppath+\"data/thca_metadata.csv\", index_col = 0)\n",
+    "metadata = pd.read_csv(ppath+\"thca_metadata.csv\", index_col = 0)\n",
     "batch = metadata['batch'].to_numpy()\n",
     "cancer = metadata['status'].to_numpy()\n",
     "sex = metadata['sex'].to_numpy()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f01301b9",
    "metadata": {},
    "source": [
     "Here gene_expression is a gene expression matrix for 19711 genes and 572 samples. Batch, cancer, and sex are sample-specific metadata as vectors of length 572."
@@ -86,6 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "eefe741a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,6 +106,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e23e09b2",
    "metadata": {},
    "source": [
     "## 2. Applications of COBRA\n",
@@ -114,6 +124,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "56c0ce1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,6 +133,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "19e30957",
    "metadata": {},
    "source": [
     "For batch correction, the design matrix must contain an intercept in the first column, and the batches (encoded usy dummy coding for identifiability) in the remaining columns. "
@@ -130,6 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "008a3832",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,6 +153,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "db8d69f1",
    "metadata": {},
    "source": [
     "We get a design matrix with 17 covariates (an intercept and 16 for the dummy coding) for the 572 samples in our study. "
@@ -148,6 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "45b0a40d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,6 +171,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9556f9b9",
    "metadata": {},
    "source": [
     "We are now ready to fit COBRA"
@@ -164,6 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "c0b3776b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,6 +189,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f07cae8c",
    "metadata": {},
    "source": [
     "The batch corrected network consider only the mean effect after removing the contribution of the batch variables. It is computed as follows. "
@@ -180,6 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "feb8b5a5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,6 +207,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3c577a75",
    "metadata": {},
    "source": [
     "### 3.2 Differential co-expression analysis\n",
@@ -197,6 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "fc0a5747",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,6 +226,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "21a412df",
    "metadata": {},
    "source": [
     "In this case, the design matrix contains an intercept an a second column with an indicator for cancer/ healthy. The additional columns are for the variables we want to adjust for. Similarly as before, we consider the batch variable. "
@@ -213,6 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "d26518b1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,6 +247,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0df93493",
    "metadata": {},
    "source": [
     "We are now ready to fit COBRA and extract the component corresponding to the differential co-expression. Since the indicator variable for cancer is the second column in our design matrix, the COBRA-adjusted differential co-expression network corresponds to the second component of COBRA's decomposition. "
@@ -232,6 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "3887dba5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,6 +266,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "15b8e757",
    "metadata": {},
    "source": [
     "### 3.3 Identifying the component for a covariate of interest\n",
@@ -251,6 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "c3f136eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,6 +288,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "78f33e7b",
    "metadata": {},
    "source": [
     "With this design, the last component of COBRA's decomposition describes the sex differes in cancer between male and females. "
@@ -269,6 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "a55644d4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,6 +307,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8286994c",
    "metadata": {},
    "source": [
     "## Reference\n",
@@ -288,7 +318,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -302,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,