Test data cleanup
The test data we use to run notebooks and test coverage alike is a bit messy. It is hard to understand which data is used where. We should do a cleaning run at some point. We may be able to remove some of the test files and use external data e.g. from scanpy.datasets
. Or another idea might be to outsource the files and load them on demand.
Anyhow, the whole testing structure should be revisited and refactored as needed.
Plotting:
-
clustering.py -
embedding.py -
general.py -
genometracks.py -
highly_variable.py -
marker_genes.py -
qc_filter.py -
velocity.py
Tools:
-
bam.py -
calc_overlap_fc.py @Jasmin.Walter -
celltype_annotation.py @Noah.Knoppik -
clustering.py -
dim_reduction.py -
embedding.py -
frip.py @philipp.goymann -
gene_correlation.py -
highly_variable.py @Moritz.Hobein -
insertsize.py @jannik.luebke -
marker_genes.py @Yousef.Alayoubi -
multiomics.py @jannik.luebke -
norm_correct.py @aviral.jain -
peak_annotation.py @Jasmin.Walter -
qc_filter.py @Noah.Knoppik -
receptor_ligand.py -
tobias.py -
tsse.py @philipp.goymann
Utils:
-
adata.py -
assemblers.py @stefan.nitz -
bioutils.py @stefan.nitz -
checker.py @Moritz.Hobein -
creators.py @Yousef.Alayoubi -
decorator.py @aviral.jain -
general.py -
io.py -
jupyter.py -
multiprocessing.py @jannik.luebke -
tables.py
See this presentation for detailed instructions: https://docs.google.com/presentation/d/1U2raXqmlCiJr3fGbZVy3lhZ2Zdt_hU47lYSkKwxmSQs/edit?usp=sharing
Edited by Jan Detleffsen