diff --git a/schedule/proposed_new_schedule.md b/schedule/proposed_new_schedule.md
index e6d1d4b..d6def14 100644
--- a/schedule/proposed_new_schedule.md
+++ b/schedule/proposed_new_schedule.md
@@ -11,7 +11,7 @@
| Time | Topic | Instructor |
|:-----------:|:----------:|:--------:|
| 09:30 - 09:45 | [Workshop introduction](../slides/Intro_to_workshop_all_in_person.pdf) | Meeta |
-| 09:45 - 11:00| [Introduction to Single Cell RNA-sequencing: a practical guide](../slides/101023_Chan_workshop_AK_latest.pdf) | [Dr. Arpita Kulkarni](https://singlecellcore.hms.harvard.edu/people/arpita-kulkarni-phd) |
+| 09:45 - 11:00| [Introduction to Single Cell RNA-sequencing: a practical guide]() | [Dr. Arpita Kulkarni](https://singlecellcore.hms.harvard.edu/people/arpita-kulkarni-phd) |
| 11:00 - 11:05 | Break |
| 11:05 - 11:15 | scRNA-seq pre-reading discussion | All |
| 11:15 - 11:55 | [Quality control set-up](../lessons/03_SC_quality_control-setup.md) | Noor |
@@ -21,17 +21,17 @@
### Before the next class:
I. Please **study the contents** and **work through all the code** within the following lessons:
- 1. [Overview of scRNA-seq Analysis Workflow](../lessons/postQC_workflow.md)
-
- Click here for a preview of this lesson
-
Let's do a quick overview of what's in store for us during the workshop. For each step we describe Goals, Challenges and Reccomendations to guide you on your way. This is a great reference page for you when you run your own analyses!
-
- 2. [Quality control of cellranger counts]()
+
+ 1. [Quality control of cellranger counts]()
Click here for a preview of this lesson
+
In this lesson you will:
+ - Discuss the outputs of cellranger (provide code in a pulldown)
+ - Create plots from metrics summary.txt
+ - Review web summary HTML report
- 3. [Quality control with additional metrics](../lessons/04_SC_quality_control.md)
+ 2. [Quality control with additional metrics](../lessons/04_SC_quality_control.md)
Click here for a preview of this lesson
Before you start any analysis, it’s important to know whether or not you have good quality cells. At these early stages you can flag or remove samples that could produce erroneous results downstream.
In this lesson you will:
@@ -39,9 +39,8 @@ I. Please **study the contents** and **work through all the code** within the fo
- Create plots to visualize metrics per sample
- Critically evaluate each plot and learn what each QC metric means
-
-
- 4. [Theory of PCA](../lessons/05_theory_of_PCA.md)
+
+ 3. [Theory of PCA](../lessons/05_theory_of_PCA.md)
Click here for a preview of this lesson
Before we can begin the next steps of the workflow, we need to make sure you have a good understanding of Principal Components Analysis (PCA). This method will be utilized in the scRNA-seq analysis workflow, and this foundation will help you better navigate those steps and interpretation of results.
@@ -63,8 +62,8 @@ II. **Submit your work**:
| Time | Topic | Instructor |
|:-----------:|:----------:|:--------:|
| 09:30 - 10:15 | Self-learning lessons discussion | All |
-| 10:15 - 10:25 | Break |
-| 10:25 - 11:25| [Normalization and regressing out unwanted variation](../lessons/06_SC_SCT_normalization.md) | Meeta |
+| 10:15 - 11:15| [Normalization and regressing out unwanted variation](../lessons/06_SC_SCT_normalization.md) | Meeta |
+| 11:15 - 11:25 | Break |
| 11:25 - 12:00| [An brief introduction to Integration](../lessons/06_integration.md) | Meeta |
### Before the next class:
@@ -72,12 +71,9 @@ I. Please **study the contents** and **work through all the code** within the fo
1. [Integration cont'd](../lessons/../lessons/06_integration.md)
Click here for a preview of this lesson
-
From the UMAP visualization of our data we can see that the cells are positioned into groups. Our next task is to isolate clusters of cells that are most similar to one another based on gene expression.
In this lesson you will:
- - Learn the theory behind clustering and how it is performed in Seurat
- - Cluster cells and visualize them on the UMAP
- 1. [Clustering](../lessons/07_SC_clustering_cells_SCT.md)
+ 2. [Clustering](../lessons/07_SC_clustering_cells_SCT.md)
Click here for a preview of this lesson
From the UMAP visualization of our data we can see that the cells are positioned into groups. Our next task is to isolate clusters of cells that are most similar to one another based on gene expression.
In this lesson you will:
@@ -85,24 +81,6 @@ I. Please **study the contents** and **work through all the code** within the fo
- Cluster cells and visualize them on the UMAP
- 2. [Clustering quality control](../lessons/08_SC_clustering_quality_control.md)
-
- Click here for a preview of this lesson
-
After separating cells into clusters, it is crtical to evaluate whether they are biologically meaningful or not. At this point we can also decide if we need to re-cluster and/or potentialy go back to a previous QC step.
-
In this lesson you will:
- - Check to see that clusters are not influenced by uninteresting sources of variation
- - Check to see whether the major principal components are driving the different clusters
- - Explore the cell type identities by looking at the expression for known markers across the clusters.
-
-
- 3. [Marker identification](../lessons/09_merged_SC_marker_identification.md)
-
- Click here for a preview of this lesson
-
By this point, you have defined most of your clusters as representative populations of particular cell types. However, there may still some uncertanity and/or unknowns. This step in workflow is about using the gene expression data to identify genes that exhibit a significantly higher (or lower) level of expression for a partcular cluster of cells.
In this lesson, we idenitfy these lists of genes and use them to:
- - Verify the identity of certain clusters
- - Help surmise the identity of any unknown clusters
-
-
II. **Submit your work**:
* Each lesson above contains exercises; please go through each of them.
@@ -118,11 +96,33 @@ II. **Submit your work**:
| Time | Topic | Instructor |
|:-----------:|:----------:|:--------:|
-| 09:30 - 10:30 | Self-learning lessons discussion | All |
-| 10:30 - 10:40 | Workflow summary | Noor |
-| 10:40 - 10:45 | Break |
-| 10:45 - 11:30 | Discussion, Final Q & A | All |
-| 11:30 - 12:00 | [Wrap up](../slides/Workshop_wrapup_in_person.pdf) | Meeta |
+| 09:30 - 10:00 | Self-learning lessons discussion | All |
+| 10:00 - 11:00| [Clustering quality control](../lessons/08_SC_clustering_quality_control.md) | |
+| 11:00 - 11:15 | Break | |
+| 11:15 - 12:00 | [Clustering quality control](../lessons/08_SC_clustering_quality_control.md)| |
+
+### Before the next class:
+I. Please **study the contents** and **work through all the code** within the following lessons:
+
+ 1. [Marker identification](../lessons/09_merged_SC_marker_identification.md)
+
+ Click here for a preview of this lesson
+
By this point, you have defined most of your clusters as representative populations of particular cell types. However, there may still some uncertanity and/or unknowns. This step in workflow is about using the gene expression data to identify genes that exhibit a significantly higher (or lower) level of expression for a partcular cluster of cells.
In this lesson, we idenitfy these lists of genes and use them to:
+ - Verify the identity of certain clusters
+ - Help surmise the identity of any unknown clusters
+
+
+ 2. Load the data into object, prepare for activities in-class Day
+
+## Day 4
+
+| Time | Topic | Instructor |
+|:-----------:|:----------:|:--------:|
+| 09:30 - 10:00 | Self-learning lessons discussion | All |
+| 10:00 - 10:45| [In-class practical exercises: QC]() | |
+| 10:45 - 10:55 | Break | |
+| 10:55 - 11:45 | [In-class practical exercises: Clustering ]()| |
+| 11:45 - 12:00 | [Wrap-up]()| |
***