v0.3.0

v0.3.0 (2026-05-09)

Major refresh of the user-facing API around a single :class:MuData. The new entry points (setup_mudata, MIDAS(mdata), get_latent_representation, get_imputed_values, save / load) compose directly with mdata.obsm, sc.pp.neighbors(use_rep=...), and the rest of the standard single-cell stack. A new plotting namespace scmidas.pl and a data-prep tutorial round out the package for users coming straight from raw 10x output.

• 🚀 New — MIDAS entry points centred on MuData
  • MIDAS.setup_mudata(mdata, batch_key=...) — register a MuData (writes config to mdata.uns['_scmidas']).
  • MIDAS(mdata, ...) — construct directly from a registered MuData; instance state instead of class-level state (fixes a multi-instance interference bug).
  • model.get_latent_representation(kind='c'|'u'|'joint') — returns the joint latent aligned to mdata.obs_names. Drop straight into mdata.obsm['X_midas'].
  • model.get_imputed_values(modality='rna') — returns imputed counts aligned to mdata.obs_names.
  • model.save(dir) / MIDAS.load(dir, mdata) — symmetric save/load (writes model.pt + setup.json).
  • MIDAS(mdata) now defaults to transform={'atac': 'binarize'} whenever 'atac' is among the modalities (override by passing your own transform dict).
• 🚀 New — scmidas.pl plotting namespace
  • scmidas.pl.umap(mdata, basis='X_midas', color=[...]) — one-line UMAP that works around the current scanpy + MuData plotting limitations via a thin AnnData wrapper.
  • scmidas.pl.modality_grid(model, mdata, label_key=...) — collapses the per-modality vs per-batch grid (~22 lines in the previous demos) into one call. Modality columns are ordered ATAC, RNA, ADT, Joint when present.
• 🚀 New — scmidas.datasets.from_dir
  • Loads the directory-format datasets (mat/<m>.mtx, mask/<m>.csv, feat/feat_dims.toml) into a MuData, including masks, labels, and ATAC chunk dims.
• 📚 New tutorial — Preparing your data
  • docs/source/tutorials/basics/preparing_your_data.ipynb walks from a public 10x Genomics 5k PBMC CITE-seq sample through QC, HVG selection, MuData wrap, MIDAS integration, Leiden clustering, and a synthetic mosaic example.
• 📚 Docs cleanup
  • inputs.rst + outputs.rst merged into data_layout.rst — a single page describing the MuData input/output contract. The directory format is moved to an "advanced" section.
  • All three demos (demo1, demo2, demo3) rewritten to use the new API: from_dir → setup_mudata → MIDAS(mdata) → get_latent_representation. The 22-line per-modality grid block became scmidas.pl.modality_grid(model, mdata). Each demo gained a 6.4 "After integration" section (Leiden + UMAP).
  • README adds a "Bring your own data" section linking the new tutorial and the data-layout reference.
• 🛠 Backwards compatibility
  • MIDAS.configure_data_from_mdata and MIDAS.configure_data_from_dir still work — they emit a DeprecationWarning and will be removed in 0.4.0.
  • save_checkpoint / load_checkpoint still work; new code should use save / load.
• 🐛 Fixes
  • predict(joint_latent=False) no longer raises KeyError: 'z_c'.
  • Multiple MIDAS() instances in one process now have independent state (was previously class-level — a second instance would clobber the first).