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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
Using Slack in this workshop
Using Zoom in this workshop
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, part 1
Mapping Comparison
Anchoring (Comparison dataset)
Shiny App Install/Overview
Shiny App Practical Usage
AWS Hosted App (Optional)
scRNA analysis prepare, part 2
Monocle
VDJ T cell and B cell analysis
Velocity analysis
ETC
Closing thoughts
Workshop Photos
Github page
Biocore website

Installing and running the app:

  1. Enter the following into your Rconsole in Rstudio after finishing the Mapping_Comparison.Rmd (it is also located at the bottom of the file itself).

    if (!any(rownames(installed.packages()) == "shiny")){
       install.packages("shiny")
     }
     library(shiny)
        
     if (!any(rownames(installed.packages()) == "markdown")){
       if (!requireNamespace("BiocManager", quietly = TRUE))
           install.packages("BiocManager")
       BiocManager::install("markdown")
     }
     library(markdown)
        
     if (!any(rownames(installed.packages()) == "tidyr")){
       if (!requireNamespace("BiocManager", quietly = TRUE))
           install.packages("BiocManager")
       BiocManager::install("tidyr")
     }
     library(tidyr)
        
     download.file("https://github.com/ucdavis-bioinformatics/scRNA_shiny/archive/master.zip", "scRNA_shiny.zip")
     #zipf <- file.choose(new=FALSE)
     zipf <- "scRNA_shiny.zip"
     outdir <- "scRNA_shiny"
     unzip(zipf, exdir=outdir)
        
     saveRDS(s_merged, file = "shiny.rds") 
    
    
  2. Enter the following into your Rconsole in Rstudio after finishing the anchoring.Rmd (it is also located at the bottom of the file itself).
     saveRDS(s.integrated_standard, file = "anchoring.rds") 
    
  3. You will now see a new directory appear in the workshop directory called scRNA_shiny:

  4. Navigate until you see the file app.R. This is the file containing the app we will use for exploring the data. Open this file:

  5. Now Click run app at the top of RStudio:

  6. The app should now pop up in a new window:

Shiny App Tutorial


Single Marker View:

Explore a single feature (gene, metadata, etc.) and its relation to variations of clustering or on a per sample basis.

Options:

Graphs:


Double Marker View:

Explore two features (gene, metadata, etc.) and its relation to variations of clustering or on a per sample basis.

Options: All of the options here are the same as the Single Marker View with the following field as an option.

Graphs:


Marker Set (Grid)

This plot helps to explore sets of genes and their relation to the identity.

Options:

Graph:


Cluster Tree Exploration

This plot helps to identify closest related clusters so when moving into the final analysis you have a better idea of what the real cell groups are in your samples.

Having trouble understanding what a tSNE vs UMAP plot represents?