Statistical Bioinformatics Lab

We recommend to use conda to manage R/Python environments and packages either on personal computers or on servers and clusters. You can also use conda to install softwares such as samtools. This is particularly useful when we are non-root users, which often prevents us from setting up the right computing environment. You may read more about setting up conda environment here.

• ShinyApp: https://wwylab.github.io/TmS/articles/shinyapp.html

• R: simpleSingleCell http://bioconductor.org/packages/devel/workflows/html/simpleSingleCell.html
• R: Seurat https://satijalab.org/seurat/
• Python: scanpy https://scanpy.readthedocs.io/

• awesome-single-cell https://github.com/seandavi/awesome-single-cell

• Broad institute: https://broadinstitute.github.io/2019_scWorkshop/
• Sanger: https://scrnaseq-course.cog.sanger.ac.uk/website/introduction-to-single-cell-rna-seq.html
• Orchestrating Single-Cell Analysis: http://bioconductor.org/books/release/OSCA/

• Use SRA toolkit to download sequencing data for CellRanger
• Build R & Python environment on remote servers using jupyter notebook
• Troubleshooting for RStudio: run RStudio in your own R environment

Tutorial for running DeMixT and TmS

• Variable selection & model selection (by Xiaoqian Liu)
• R's C interface tutorial (by Xiaoqian Liu)
• Feature selection II (by Xiaoqian Liu)

• How to improve scientific writing (by Xiaoqian Liu)