Version Compatibility

Reference examples tested with: bin2cell 0.3+, scanpy 1.10+, anndata 0.10+, spatialdata 0.1+, squidpy 1.4+

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.

High-Resolution Binning

"Turn my Visium HD 2um bins into cells" -> Aggregate sub-cellular capture features UP into single-cell profiles, using a registered nucleus image to decide which bins belong to which cell when one exists.

• Python: Bin2cell (b2c.read_visium -> b2c.stardist -> b2c.insert_labels -> b2c.bin_to_cell) for image-guided reconstruction; scanpy/squidpy for fixed-bin aggregation when no image exists

Governing Principle

Binning is the INVERSE of deconvolution. Deconvolution takes a capture unit that is LARGER than a cell (a 55um Visium spot holding 1-10 cells) and mixes it DOWN into the cell-type fractions inside it. High-resolution platforms have the opposite geometry: a Visium HD 2um bin, a Stereo-seq ~220nm DNB spot, and a Slide-seqV2 10um bead are SMALLER than or comparable to a single cell, so each unit is a fragment of one cell, not a mixture of several. The task is to aggregate fragments UP into whole cells, never to deconvolve a mixture that does not exist. Running deconvolution on 2um bins invents fractional cell-type mixtures inside features that hold only part of one cell.