single cell RNA-seq workshop

R installation

Running R/Rstudio with docker

In this post we will discuss how to use docker to run R and Rstudio in a standardized environment.

Working with your own single cell data

Guidelines for datasets to work with in the workshop, information on where to get public datasets, and examples of how to load different data formats into R.

Prerequisite material

Interacting with R in Rstudio

A tutorial for navigating the Rstudio IDE, RMarkdown, and setting up Rstudio projects to organize each class.

Intro_scRNAseq_QC_PCA

The goal of this lecture is to give an overview of scRNA seq, quality control, filtering, and briefly touch on PCA

Class 2: Dimensionality reduction, clustering, differential expression, cell type annotation

In this class we will discuss scRNA-seq data processing for visualization and defining transcriptome features.

Class 3: Batch correction and pseudotime

This tutorial walks through batch correction between two samples from different groups with some overlapping cell types using harmony. In the second half of the tutorial, we use timecourse data to run and evaluate pseudotime using slingshot.

Class 4: Multi-modal data

This tutorial describes a basic CITE-seq analysis workflow.

FAQ

Answers to common questions

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single cell RNA-seq workshop