NOTE: this is a post hoc excercise to demonstrate the process of creating release notes semi-automatically as the endpoint of development via github.
The previous minimum viable product version of the workflow employed R based tools and harmonized samples with the Harmony package. This workflow suffered from poor scaling properties: memory and resource utilization became un-manageable for greater than >500k cells.
Key updates:
[ ] depricate R in place of python tools
[ ] depricate snakemake handling
[ ] rewrite scripts to leverage scanpy scvi-tools
[ ] cellassign for infering cell types
[ ] scvi for integration
[ ] scib-metrics for tracking the tradeoff between technical variability due to batch/sample effects and conserving biological variability nescessary for further scientific inquiry
NOTE: this is a post hoc excercise to demonstrate the process of creating release notes semi-automatically as the endpoint of development via github.
The previous minimum viable product version of the workflow employed R based tools and harmonized samples with the Harmony package. This workflow suffered from poor scaling properties: memory and resource utilization became un-manageable for greater than >500k cells.
Key updates: