beginner-workshop-gi-5-25

Beginner’s guide to scRNA-seq - GI edition

Where: Karp 6 Conference Room
Course Material:
- Introduction to R/Rstudio/Seurat [Recording]
  - How to navigate Rstudio
  - Fundamentals and data wrangling in R
  - Working with a Seurat object
- Basic sc-RNAseq workflow [Recording]
  - Main steps for scRNAseq workflow
  - Intuition behind each step
- Data Download [Recording]
  - Download the data needed for this workshop
  - Go over the basics of a Seurat object
- Normalization & HVG selection [Recording]
  - Why and how to normalize sc-RNAseq data
  - Why and how to select highly variable genes
- Quality Control [Recording]
  - 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

Where: Karp 6 Conference Room
Course Material:
- PCA, Integration & KNN graphs [Recording]
  - Brief overview on PCA and how to use it for sc-RNAseq
  - KNN-graph representation of the data
  - Correcting batch effects with Harmony
- Clustering [Recording]
  - 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 [Recording]
  - How to compute differentially expressed genes between clusters
  - How to evaluate differential expression statistics
  - Reference-based cell type annotation
  - Manual cell type annotation
- Subclustering & Level 2 Annotation [Recording]
  - What does level 2 annotation mean and why we need it
  - How to iteratively annotate a dataset

TBD