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      Advanced Single Cell RNA-Seq Workshop

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Introduction and Lectures
Intro to the Workshop and Core
Schedule
What is Bioinformatics/Genomics?
Experimental Design and Cost Estimation
Single Cell Sample Preparation - Dr. Diana Burkart-Waco
Support
Cheat Sheets
Software and Links
Scripts
Prerequisites
CLI - Logging in and Transferring Files
CLI - Intro to Command-Line
CLI - Advanced Command-Line (extra)
CLI - Running jobs on the Cluster and using modules
R - Getting Started
R - Intro to R
R - Prepare Data in R (extra)
R - Data in R (extra)
More Materials (extra)
Data Reduction
Generating Expression Matrices
Expression project setup
Preprocessing reads with HTStream
Generating Expression Tables
VDJ T cell and B cell
Velocity analysis
Data analysis
scRNA analysis prepare
Mapping Comparison
Anchoring (Comparison dataset)
Shiny App Install/Overview
App Practical Usage
AWS Hosted App (Optional)
Monocle
VDJ T cell and B cell analysis
Velocity analysis
ETC
Closing thoughts
Workshop Photos
Github page
Biocore website

App Practical Usage

This shiny app was created with the intention of working with biologists to extract meaning from the data by exploration. Usage of the app is not for creating finalized “publish ready” images but rather a means for enabling an analysis of potential clustering based on the marker genes of interest (the biological question). The app enables easier interaction with the data in order to explore what next steps can be taken towards producing “publish quality” images and which further analyses are relevant to the biological question.

Manual intervention (such as clustering) with the Seurat objects is often needed following this exploration. The hope is for the app to enables some of these capabilities in the near future. Lets do some exercises to see how might use the app to properly infer what custom clustering may need to be done.

Exercises

1) Use the single marker view to look at percent.mito at resolution 0.5 with a reduction of either tsne or umap. Which two groups cluster the most? What part of the app can you use to see if these two clusters are the most closely related compared to the other clusters?

2) Extracting biological meaning in your data is an important part of scRNA-seq analysis. Typically this can be done by looking through literature to confirm markers for cell types from your sample type. -The data used for our analysis is from the follwoing paper: https://pubmed.ncbi.nlm.nih.gov/31733517/

Check out the paper that this data is from, can you recognize any similar patterns? 

Can you think of anything you might do to the Seurat object that might enable better analysis with the app based on the paper?

3) Run the app with the .RDS file that we created in the Anchoring Lecture. (Hint: you will need to change one line in the app.R file). What are some differences you know when doing the comparison?

A few extra things: