Scripts for "Best-practices in single-cell RNA-seq: a tutorial"
This repository contains the scripts used to generate the figures in the manuscript M. D. Luecken and F. J. Theis., "Best practices in single-cell RNA-seq analysis: a tutorial", a case study which complements the manuscript (Also in Supplementary Note 4), and a marker gene study using simulated data from the splatter package.
The main part of this repository is a case study where the best-practices established in the manuscript are applied to a mouse intestinal epithelium regions dataset from Haber et al., Nature 2018 available from the GEO under GSE92332.
The scripts in the plotting_scripts/ folder reproduce the figures that are shown in the manuscript and the supplementary materials. These scripts contain comments to explain each step. Each figure that does not have a corresponding script in the plotting_scripts/ folder were taken from the case study or the marker gene study.
In case of questions or issues, please get in touch by posting an issue in this repository.
Case study
To run the single cell tutorial from start to finish, several requirements must be met.
Requirements
General:
- Jupyter notebook
- IRKernel
- rpy2
- R >= 3.4.3
- Python >= 3.5
Python:
- scanpy
- numpy
- scipy
- pandas
- seaborn
- louvain>=0.6
- python-igraph
- ComBat python implementation from Maren Buettner's github (mbuttner/maren_codes/combat.py)
- python-gprofiler from Valentine Svensson's github (vals/python-gprofiler)
R:
- scater
- MAST
- gam
- slingshot (change DESCRIPTION file for R version 3.4.3)
- monocle 2
- limma
- splines
- ComplexHeatmap
- RColorBrewer
- clusterExperiment
- ggplot2
Application notes:
When using Slingshot in R 3.4.3, you must pull a local copy of slingshot via the github repository and change the DESCRIPTION file to say R>=3.4.3 instead of R>=3.5.0.