Hands-On Workshop: Single Cell RNAseq Analysis in R Using Seurat, Harmony, and Single R
An everything bagel for the brain. Substantive, layered, well-seasoned.
Summary
This article announces a hands-on workshop on single cell RNAseq (scRNAseq) analysis using R packages including Seurat, Harmony, and Single R. The workshop covers the complete workflow from reading count data through quality control, filtering, normalization, clustering, UMAP visualization, and identification of cluster markers. It emphasizes understanding the rationale behind each analytical step and provides training on visualizing single-cell expression data.
Key quotes
· 4 pulledAnalysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows.
In this hands-on workshop we will show you how to perform single cell RNAseq analysis using Seurat, Harmony and Single R - R packages for QC, analysis, and exploration of single-cell RNAseq data.
We will discuss the 'why' behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers.
We will also explore various ways of visualising single cell expression data.
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