Code describing the analyses of the single cell dataset included in Ratnayake et al (2020). There are two main code files, one describing preprocessing and QC, normalisation, dimensional reduction, clustering and cluster markers identification, using Seurat^{1, 2} (SC_RNA_seq.Rmd) and a second one, describing a trajectory analysis using PAGA³ (Trajectory_Analysis.ipynb) within the Scanpy⁴ analytical toolkit.

Both SC_RNA_seq.Rmd and Trajectory_Analysis.ipynb code files have been compiled into htmls for easy visualization and sharing.

1. Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., … Satija, R. (2019). Comprehensive Integration of Single-Cell Data. Cell. https://doi.org/10.1016/j.cell.2019.05.031
2. Butler, A., Hoffman, P., Smibert, P. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36, 411–420 (2018). https://doi.org/10.1038/nbt.4096
3. Wolf, F.A., Hamey, F.K., Plass, M. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol 20, 59 (2019). https://doi.org/10.1186/s13059-019-1663-x.
4. Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018). https://doi.org/10.1186/s13059-017-1382-0.