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lstar 0.1.0

First release. lstar is a uniform data model (L*) and a Zarr interchange format for single-cell / spatial omics, with a shared C++ core (libstar) and bindings in R, Python and C++.

Data model & store

• Datasets of axes (labelled sets you index by) and fields (typed data over a tuple of axes, with a role / span / encoding / state / coverage / provenance).
• Read / write .lstar.zarr stores (lstar_read, lstar_write), multi-chunk and gzip-compressed, byte-compatible with the Python and C++ readers.
• Collections of heterogeneous samples (kind = "collection") — per-sample cells.<s>/genes.<s> axes over gene sets that may overlap, differ, or be disjoint, plus a union cells axis carrying the joint embedding / clustering / integration graph. Build one from any list of per-sample objects with collection_from().

Format converters (profiles)

• Seurat (legacy v2 through v5; a split v5 assay is read as a collection, and a collection is written back as a split assay) — read_seurat / write_seurat. Cell-cell graphs round-trip as Graphs().
• SingleCellExperiment — read_sce / write_sce.
• Conos — write_conos (Conos → L*) and read_conos (L* → a live Conos), preserving the per-sample data and the joint graph / embedding / clustering.
• The shared vocabulary keeps the common core (counts, data/X, pca + loadings, umap, labels, metadata) lossless across conversions; what a target cannot hold is recorded in dropped, never lost silently.

Performance

• Streamed, multi-threaded (OpenMP) per-gene/per-cell reductions over the compiled core (lstar_read_block, lstar_stream_col_sum_by_group, col_sum_by_group), with bounded memory and thread-invariant results.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.