Tools for analyzing biological count data, especially from single cell RNA-seq.

Please see our manuscript for more details:

Church et al. 2022. Normalizing need not be the norm: count-based math for analyzing single-cell data. bioRxiv https://doi.org/10.1101/2022.06.01.494334

countland is implemented in both R and python. The code for each is included in this repository.

countland is available with pip: https://pypi.org/project/countland/

To prepare a conda environment and install countland (before first use):

conda create -n countland -c conda-forge
conda activate countland
pip install countland

To activate the conda environment (before each use):

conda activate countland

The develompent version from in this repository can be installed using

pip install git+https://github.com/shchurch/countland.git#subdirectory=countland-py

The easiest way to run the tutorial is as a Google Colab notebook. Just open the following link and follow the instructions:

https://colab.research.google.com/github/shchurch/countland/blob/main/tutorials_and_vignettes/python_tutorials_and_vignettes/vignette_tutorial.ipynb

Alternatively, the python tutorial can be run locally in a jupyter notebook.

countland is available from CRAN: https://CRAN.R-project.org/package=countland

From an R prompt, run the following:

install.packages("countland")

Teh development version from this repository can be installed using

library(devtools)
install_github("shchurch/countland", subdir="countland-R")

The R tutorial can be run locally as an Rmarkdown file, e.g. knit in RStudio.

See development.md for details.