In this tutorial, we will explore the fundamental setup and perform clustering analysis for single-cell RNA sequencing using Seurat. This comprehensive tutorial aims to assist beginners in understanding the topic more effectively, employing fun analogies where possible.
We will embark on a step-by-step journey, covering crucial stages such as standard pre-processing workflow, normalization, feature selection, scaling, dimensionality reduction, clustering, and the subsequent visualization and annotation of clusters.
Our specific dataset focuses on Peripheral Blood Mononuclear Cells (PBMC), often described as the fearless immune cell army that resides within our bloodstream. These remarkable cells diligently safeguard our bodies, acting as defenders against potential invaders such as viruses and bacteria.
To make the learning experience more enjoyable and relatable, we have infused some fun analogies inspired by the LOTR universe.
So, let's get started and unravel the mysteries of Seurat-guided clustering for scRNA-seq data!
An html version of this R-notebook can be found here:
https://rpubs.com/shilpayadahalli/1063825
In this tutorial, we will use a different dataset, and carry out the clustering steps as above followed by automatic cell annotation by ScType.
https://rpubs.com/shilpayadahalli/1072751
Trajectory analysis of scRNAseq data. I have partly reproduced data from this reasearch article: Single-cell transcriptomic landscape of human blood cells