scCODA 0.1.8-foss-2021b

scCODA allows for identification of compositional changes in high-throughput sequencing count data, especially cell compositions from scRNA-seq. It also provides a framework for integration of cell-type annotated data directly from scanpy and other sources. Aside from the scCODA model (B├╝ttner, Ostner et al (2021)), the package also allows the easy application of other differential testing methods.

Accessing scCODA 0.1.8-foss-2021b

To load the module for scCODA 0.1.8-foss-2021b please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load scCODA/0.1.8-foss-2021b

BEAR Apps Version



  • scCODA 0.1.8

More Information

For more information visit the scCODA website.


This version of scCODA has a direct dependency on: ArviZ/0.11.4-foss-2021b foss/2021b matplotlib/3.4.3-foss-2021b Python/3.9.6-GCCcore-11.2.0 rpy2/3.4.5-foss-2021b scanpy/1.8.2-foss-2021b scikit-bio/0.5.7-foss-2021b scikit-learn/1.0.1-foss-2021b Seaborn/0.11.2-foss-2021b statsmodels/0.13.1-foss-2021b TensorFlow/2.8.4-foss-2021b tensorflow-probability/0.16.0-foss-2021b

Last modified on 2nd December 2022