Skip to content

proposed_4day_schedule.md

Latest commit

 

History

History
185 lines (140 loc) · 10.5 KB

proposed_4day_schedule.md

File metadata and controls

185 lines (140 loc) · 10.5 KB

Schedule for the single-cell RNA-seq data analysis workshop

Pre-reading

Day 1

Time Topic Instructor
09:30 - 09:45 Workshop introduction Meeta
09:45 - 11:00 Introduction to Single Cell RNA-sequencing: a practical guide Dr. Arpita Kulkarni
11:00 - 11:05 Break
11:05 - 11:15 scRNA-seq pre-reading discussion All
11:15 - 11:55 Quality control set-up Noor
11:55 - 12:00 Overview of self-learning materials and homework submission Meeta

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  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

  2. Quality control with additional metrics

    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:
    - Compute essential QC metrics for each sample
    - Create plots to visualize metrics per sample
    - Critically evaluate each plot and learn what each QC metric means

  3. Theory of PCA

    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.

II. Submit your work:

  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while runnning code in the lesson, email us
  • Post any conceptual questions that you would like to have reviewed in class here.

Day 2

Time Topic Instructor
09:30 - 10:15 Self-learning lessons discussion All
10:15 - 11:15 Normalization and regressing out unwanted variation Meeta
11:15 - 11:25 Break
11:25 - 12:00 An brief introduction to Integration Meeta

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Integration cont'd

    Click here for a preview of this lesson

  2. Clustering

    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

II. Submit your work:

  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while runnning code in the lesson, email us
  • Post any conceptual questions that you would like to have reviewed in class here.

Day 3

Time Topic Instructor
09:30 - 10:00 Self-learning lessons discussion All
10:00 - 11:00 Clustering quality control
11:00 - 11:15 Break
11:15 - 12:00 Clustering quality control

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Marker identification

    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

Answer Keys

Downstream analyses

Differential expression between conditions

Resources

We have covered the analysis steps in quite a bit of detail for scRNA-seq exploration of cellular heterogeneity using the Seurat package. For more information on topics covered, we encourage you to take a look at the following resources:

Building on this workshop

Other helpful links