Small corrections notebooks

saezlab · Nov 25, 2022 · 0391f4f · 0391f4f
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diff --git a/docs/source/notebooks/msigdb.ipynb b/docs/source/notebooks/msigdb.ipynb
@@ -2,15 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6ab59a1c",
+   "id": "b66fda50",
    "metadata": {},
    "source": [
     "# Functional enrichment of biological terms"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "51c6a2a7",
+   "id": "aca06cc5",
    "metadata": {},
    "source": [
     "scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by assigning them to biological terms from prior knowledge.\n",
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e65d5aa",
+   "id": "7bb71063",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4a74e503",
+   "id": "8d8d1e9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7911e03",
+   "id": "ba3c7327",
    "metadata": {},
    "source": [
     "## Loading the data\n",
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "caeb9307",
+   "id": "b55c010e",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "063875b8",
+   "id": "5733c3d9",
    "metadata": {},
    "source": [
     "We can visualize the different cell types in it:"
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "5830fc61",
+   "id": "d54e1470",
    "metadata": {},
    "outputs": [
     {
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3434d155",
+   "id": "456e6fa7",
    "metadata": {},
    "source": [
     "## MSigDB gene sets\n",
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e23e276f",
+   "id": "ed35afbf",
    "metadata": {},
    "outputs": [
     {
@@ -277,10 +277,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba58ae14",
+   "id": "d4419a70",
    "metadata": {},
    "source": [
-    "As an example, we will use the hallmark gene sets, but we could have used any other. \n",
+    "As an example, we will use the hallmark gene sets, but we could have used any other such as KEGG or REACTOME. \n",
     "\n",
     "<div class=\"alert alert-info\">\n",
     "\n",
@@ -296,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8aae33ad",
+   "id": "63ab797b",
    "metadata": {},
    "outputs": [
     {
@@ -430,15 +430,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "263733e6",
+   "id": "abc036e7",
    "metadata": {},
    "source": [
     "For this example we will use the resource MSigDB, but we could have used any other such as GO. To see the list of available resources inside `Omnipath`, run `dc.show_resources()`"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "730beda7",
+   "id": "230069c3",
    "metadata": {},
    "source": [
     "## Enrichment with Over Representation Analysis\n",
@@ -457,7 +457,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "f18b018f",
+   "id": "28f80701",
    "metadata": {},
    "outputs": [
     {
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6e928d2",
+   "id": "a76f5bb2",
    "metadata": {},
    "source": [
     "The obtained scores (-log10(p-value))(`ora_estimate`) and p-values (`ora_pvals`) are stored in the `.obsm` key:"
@@ -491,7 +491,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a0b76012",
+   "id": "9c9d230e",
    "metadata": {},
    "outputs": [
     {
@@ -992,7 +992,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d5e9a20",
+   "id": "342fefce",
    "metadata": {},
    "source": [
     "## Visualization\n",
@@ -1004,7 +1004,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "dcb61861",
+   "id": "631022a6",
    "metadata": {},
    "outputs": [
     {
@@ -1028,7 +1028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35f062a9",
+   "id": "0d8de784",
    "metadata": {},
    "source": [
     "`dc.get_acts` returns a new `AnnData` object which holds the obtained activities in its `.X` attribute, allowing us to re-use many `scanpy` functions, for example:"
@@ -1037,7 +1037,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "6b1a8a78",
+   "id": "baad01ca",
    "metadata": {},
    "outputs": [
     {
@@ -1059,15 +1059,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3bcc01f",
+   "id": "71f38076",
    "metadata": {},
    "source": [
     "The cells highlighted seem to be enriched by coagulation."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3613244f",
+   "id": "987c3ea7",
    "metadata": {},
    "source": [
     "## Exploration\n",
@@ -1078,7 +1078,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "1280b444",
+   "id": "e7314c36",
    "metadata": {},
    "outputs": [
     {
@@ -1307,7 +1307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9488a871",
+   "id": "9f8a8f43",
    "metadata": {},
    "source": [
     "We can visualize which group is more enriched using `seaborn`:"
@@ -1316,7 +1316,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e39e730c",
+   "id": "9c7ecc46",
    "metadata": {},
    "outputs": [
     {
@@ -1339,15 +1339,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "937b7931",
+   "id": "b7566e44",
    "metadata": {},
    "source": [
     "In this specific example, we can observe that most cell types are enriched by targets of MYC, a global regulator of the immune system, and megakaryocytes are enriched by coagulation genes."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e534fff1",
+   "id": "f75e1467",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",