This is a pipeline to analyse single-cell RNA sequencing data from neurons (1) isolated from acute midbrain slices of transgenic mice using visually guided aspiration via patch pipettes and (2) processed using Smart-seq2.
This pipeline has been generated after attending the EMBL-EBI RNA-Sequence Analysis Course and attending and following the online course on Analysis of single cell RNA-seq data by the Hemberg Lab. Many other resources have been used, including the Orchestrating Single-Cell Analysis with Bioconductor book by Robert Amezquita, Aaron Lun, Stephanie Hicks, and Raphael Gottardo, the simpleSingleCell workflow in Bioconductor maintained by Aaron Lun, the rnaseqGene workflow maintained by Michael Love, the RNAseq123 workflow maintained by Matthew Ritchie, and the EGSEA123 workflow maintained by Matthew Ritchie.
Other key resources include Bioconductor (Huber et al., Nature Methods 2015), scRNA-tools, scater (McCarthy et al., Bioinformatics 2017), scran (Lun et al., F1000Res 2016), SC3 (Kiselev et al., Nature Methods 2017), Seurat (Butler et al., Nature Biotechnology 2018), clusterExperiment (Risso et al., PLOS Computational Biology 2018), limma (Ritchie et al., Nucleic Acids Research 2015), DESeq2 (Love et al., Genome Biology 2014), edgeR (Robinson et al., Bioinformatics 2010), MAST (Finak, McDavid, Yajima et al., Genome Biology 2015), iSEE (Rue-Albrecht & Marini et al., F1000Research 2018), t-SNE (van der Maaten & Hinton, Journal of Machine Learning Research 2008), UMAP (McInnes et al., arXiv 2018), and the Mathematical Statistics and Machine Learning for Life Sciences column by Nikolay Oskolkov.
To go through this pipeline, you will need to install the following: R3.6.1 | RTools35 | RStudio 1.1.463 | Git | Bioconductor 3.10.
Completed:
- Part I: from a gene expression matrix to a SingleCellExperiment object
- Part II: pre-processing and data quality control
- Part III: normalisation
- Part IV: modelling technical noise and feature selection
- Part V: batch correction and dimensionality reduction
- Part VI: clustering
- Part VII: differential expression analysis
- Part VIII: data visualisation
Other notebooks: