Version Compatibility

Reference examples tested with: numpy 1.26+, scipy 1.12+, pandas 2.2+, anndata 0.10+, scanpy 1.10+, Matrix R package 1.6+, HDF5Array 1.30+ (Bioconductor), DelayedArray 0.28+, zellkonverter 1.12+, zarr-python 2.18+, dask 2024.1+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Sparse Matrix Handling

"Store / compute on a single-cell matrix without blowing up memory" -> Pick the sparse format that matches the access pattern (CSC for column ops, CSR for row ops), respect the R/Python convention difference (Bioconductor stores cells in columns; AnnData stores cells in rows), and use HDF5/Zarr backed mode for matrices too large for RAM.

The Single Most Important Modern Insight -- The R/Python interop transpose is silent and catastrophic

R Bioconductor (Seurat, SingleCellExperiment) stores cells in COLUMNS and uses dgCMatrix (Column-Compressed Sparse). Python scverse (AnnData, scanpy) stores cells in ROWS and defaults to CSR (Compressed Sparse Row). Round-tripping with anndata2ri, zellkonverter, or rpy2 triggers a TRANSPOSE under the hood -- once per direction. Two flips silently cancel. A debugging session that converts back and forth multiple times can end up with mysteriously transposed data and no error.