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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.
Health stats visible at Monitor.