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