In this workflow the abundances of transcipts are quantified using kallisto and analysed with sleuth.
The resulting data ist analysed further with the tools seaborn, scikit-learn, ComplexHeatmap and Pizzly to create summarizing plots and tables that can be found in the folder plots after execution.
- Johannes Köster (@johanneskoester), https://koesterlab.github.io
- Jana Jansen (@jana-ja)
- Ludmila Janzen (@l-janzen)
- Sophie Sattler (@sophsatt)
- Antonie Vietor (@AntonieV)
If you simply want to use this workflow, download and extract the latest release. If you intend to modify and further develop this workflow, fork this reposity. Please consider providing any generally applicable modifications via a pull request.
In any case, if you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository and, if available, its DOI (see above).
Configure the workflow according to your needs via editing the file config.yaml.
Further instructions can be found in the file.
Test your configuration by performing a dry-run via
snakemake -n
Execute the workflow locally via
snakemake --cores $Ns --use-conda
using $N cores or run it in a cluster environment via
snakemake --cluster qsub --jobs 100 --use-conda
or
snakemake --drmaa --jobs 100 --use-conda
See the Snakemake documentation for further details.