HMS-scRNA-workshop

A Beginner’s Guide to Analyzing scRNA-seq

Introduction to R/Rstudio
- How to navigate Rstudio
- Fundamentals and data wrangling in R
Introduction to Seurat
- Working with a Seurat object
Basic sc-RNAseq workflow
- Main steps for scRNAseq workflow
- Intuition behind each step

Quality Control
- How to carry out QC analysis
- What parameters do we need to look at and how to interpret them
- Computing doublet scores
- Tips and tricks during the QC process
PCA, Integration & KNN graphs[
- Brief overview on PCA and how to use it for sc-RNAseq
- KNN-graph representation of the data
- Correcting batch effects with Harmony
Suppl Normalization & HVG selection
- Why and how to normalize sc-RNAseq data
- Why and how to select highly variable genes

Clustering
- How to cluster sc-RNAseq data
- What algorithms can we use
- How to assess if a clustering resolution is good
Differential Gene Expression & Level 1 Annotation
- How to compute differentially expressed genes between clusters
- How to evaluate differential expression statistics
- Reference-based cell type annotation
- Manual cell type annotation
Suppl Subclustering & Level 2 Annotation
- What does level 2 annotation mean and why we need it
- How to iteratively annotate a dataset

Gene Signatures
- What tools can we use to score signatures
- How to interpret score results
- Digging into the genes driving a signature
Compositional Analysis
- Normalization methods for compositional data
- Differential abundance analysis
- PCA in compositional space