Adjust tutorials

mimic_2_causal_inference.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# MIMIC-II Causal Inference"
+    "# MIMIC-II IAC Causal Inference"
    ]
   },
   {

mimic_2_fate.ipynb

@@ -5,7 +5,7 @@
    "id": "70954479-ed2d-4bdb-90ca-5d607019e5b9",
    "metadata": {},
    "source": [
-    "# MIMIC-II IAC Patient Fate"
+    "# MIMIC-II IAC Patient Trajectory and Fate"
    ]
   },
   {
@@ -15,8 +15,8 @@
    "source": [
     "In the previous [introduction tutorial](https://ehrapy.readthedocs.io/en/latest/tutorials/notebooks/mimic_2_introduction.html), we explored the MIMIC-II IAC dataset, comprising electronic health records (EHR) of 1776 patients in 46 features, and identified patient group-specific clusters using ehrapy. Please go through the MIMIC-II IAC introduction before performing this tutorial to get familiar with the dataset. \n",
     "\n",
-    "As a next step, we want to determine patient fate. The goal is to detect terminal states and the corresponding origins based on pseudotime. Real time very rarely reflects the actual progression of a disease. When measurements are done on a certain day, some patients will show no sign of disease (e.g. healthy or recovered), some are at the onset of a specific disease and some are in a more severe stage or even at the height.\n",
-    "For an appropriate analysis, we are interested in a continuous transition of states, such as from healthy to diseased to death, for which the real time is therefore not informative. Identification of transition states can be achieved by identifying source states (e.g. healthy) and then calculating pseudotime from this state.\n",
+    "As a next step, we want to determine patient trajectories and patient fate. The goal is to detect terminal states and the corresponding origins based on pseudotime. Real time very rarely reflects the actual progression of a disease. When measurements are done in a single snapshot or cross-sectional setting, some patients will show no sign of disease (e.g. healthy or recovered), some are at the onset of a specific disease and some are in a more severe stage or even at the height.\n",
+    "For an appropriate analysis, we are interested in a continuous transition of states, such as from healthy to diseased to death, for which the real time is therefore not available or informative. Identification of transition states can be achieved by identifying source states (e.g. healthy) and then calculating pseudotime from this state.\n",
     "Based on Markov chain modelling, we uncover patient dynamics using [CellRank](https://cellrank.readthedocs.io/en/latest/index.html).\n",
     "For more details, please read [CellRank paper 1](https://www.nature.com/articles/s41592-021-01346-6) and [CellRank paper 2](https://www.biorxiv.org/content/10.1101/2023.07.19.549685v1)."
    ]
@@ -149,7 +149,7 @@
    "id": "53aedbab",
    "metadata": {},
    "source": [
-    "This tutorial is based on the MIMIC-II IAC dataset which was previously introduced in the [MIMIC-II IAC introduction tutorial](https://ehrapy.readthedocs.io/en/latest/tutorials/notebooks/mimic_2_introduction.html). We will load the encoded version of the dataset as an AnnData object, ehrapy's default encoding is a simple one-hot encoding in this case. "
+    "This tutorial is based on the MIMIC-II IAC dataset which was previously introduced in the [MIMIC-II IAC introduction tutorial](https://ehrapy.readthedocs.io/en/latest/tutorials/notebooks/mimic_2_introduction.html)."
    ]
   },
   {
@@ -390,7 +390,7 @@
    "id": "668158ef",
    "metadata": {},
    "source": [
-    "This UMAP embedding is exactly the same as previously computed in the MIMIC-II IA introduction tutorial. Now we continue with the patient fate analysis."
+    "This UMAP embedding is exactly the same as previously computed in the MIMIC-II IAC introduction tutorial. Now we continue with the patient fate analysis."
    ]
   },
   {
@@ -477,7 +477,7 @@
    "source": [
     "The [PseudotimeKernel](https://cellrank.readthedocs.io/en/latest/notebooks/tutorials/kernels/300_pseudotime.html) computes direct transition probabilities based on a KNN graph and pseudotime.\n",
     "\n",
-    "The KNN graph contains information about the (undirected) conductivities among observations (here patients), reflecting their similarity. Pseudotime can be used to either remove edges that point against the direction of increasing pseudotime, or to downweight them."
+    "The KNN graph contains information about the (undirected) conductivities among patients, reflecting their similarity. Pseudotime can be used to either remove edges that point against the direction of increasing pseudotime, or to downweight them."
    ]
   },
   {
@@ -550,7 +550,7 @@
    "id": "6c4565ca",
    "metadata": {},
    "source": [
-    "We observe two main trajectories originating from cluster 0 going to clusters 2 and 5. Let's check the metadata again. "
+    "We observe two main trajectories originating from cluster 0 going to clusters 2, 3, and 5. Let's check the metadata again. "
    ]
   },
   {
@@ -606,7 +606,7 @@
    "id": "822d68d1-4406-4615-b695-95d1f7e24deb",
    "metadata": {},
    "source": [
-    "Cluster 2 consists of patients that deceased and had severe comorbidities while cluster 5 includes patients with a high day post ICU admission. "
+    "Cluster 4 and 1 consist of patients that deceased, while cluster 5 includes patients with a high day post ICU admission. "
    ]
   },
   {

mimic_2_survival_analysis.ipynb

@@ -5,7 +5,7 @@
    "id": "c9f3ba8d-da52-4038-8f63-2b513fd51fe4",
    "metadata": {},
    "source": [
-    "# MIMIC-II Survival Analysis"
+    "# MIMIC-II IAC Survival Analysis"
    ]
   },
   {

patient_trajectory.ipynb

@@ -0,0 +1,24 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Patient Trajectory in COVID-19\n",
+    "\n",
+    "We want to understand the patient trajectory of patients with infectious diseases such as COVID-19.\n",
+    "Single-cell genomics can generate highly detailed molecular biological information on individuals suffering from such a disease.\n",
+    "[patpy](https://github.com/lueckenlab/patpy) is a tool which can generate a patient representation from such an individual patient's single-cell genomic profile.\n",
+    "\n",
+    "Check out **[this tutorial](https://github.com/lueckenlab/patpy/blob/main/docs/notebooks/Patient_trajectories_example.ipynb)** on the patpy repository to learn more about how ehrapy can be used for trajectory inference on representations generated from patpy."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}