This repository contain resources and guides for the single-cell alternative splicing project.

The aim of this project is to identify regulated alternative splicing events between cell clusters from scRNA-Seq experiments. Here are the key objectives:

1. Understand a typical scRNA-seq workflow (Smart-Seq vs 10X Genomics)
2. Review current approaches to analyze single-cell alternative splicing and its limitations
3. Process published scRNA-seq dataset to identify cell clusters
4. Merge sequencing reads from cell clusters and compare alternative splicing landscape between clusters
5. Identify enrichment in gene groups regulated by alternative splicing

Below are several resources that are useful for this project.

• scRNA-seq datasets
  1. Early emergence of cortical interneuron diversity in the mouse embryo, 2018; Mi et al
     • Data can be found here
  2. Shared and distinct transcriptomic cell types across neocortical areas, 2018; Tasic et al
     • Data can be found here or here
• Programs/packages for bioinformatics analysis
  1. Seurat, for QC and clustering of single-cell data
  2. Whippet, for analyzing alternative splicing changes

Some of the current tasks that can be done:

• Literature review of scRNA-seq workflows (Smart-Seq vs 10X Genomics)
• Literature review of single-cell alternative splicing analysis
• Download count matrix from scRNA-seq dataset
• Familiarize with Seurat package and R programming
• Import scRNA-seq matrix into R
• Carry QC on dataset and normalise+scale data
• Perform dimensional reduction (preferably UMAP/tSNE) and create clusters
• Identify cluster biomarkers and infer its cell type (if possible)
• Label tsne/UMAP plot with cell types annotated from main paper
• Plan pipeline for whippet analysis on clustered scRNA-seq transcriptome