Deprecated: Use of this version of scCODA is deprecated. More information on our Applications Support and Retention Policy.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 (Büttner, 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
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