Release/0.3.0#34
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Reshape the user-facing API around a single MuData input and
mdata.obsm output keys. New entry points:
- MIDAS.setup_mudata(mdata, batch_key=...) registers data setup info
on the MuData (mdata.uns['_scmidas']) and replaces the previous
class-level state mutation.
- MIDAS(mdata, configs=..., batch_size=...) constructs the model
directly from a registered MuData. Instance state instead of
class-level state — fixes a bug where two MIDAS() instances in one
process would clobber each other.
- model.get_latent_representation(kind='c'|'u'|'joint') and
model.get_imputed_values(modality=...) return arrays aligned to
mdata.obs_names, ready for direct assignment to mdata.obsm /
mdata.layers.
- model.save(dir) / MIDAS.load(dir, mdata) write/read a model.pt +
setup.json pair.
- scmidas.datasets.from_dir(dir_path, label_dir=...) loads the
directory-format datasets (mat/<m>.mtx, mask/<m>.csv,
feat/feat_dims.toml) into a MuData, including per-batch masks,
cell labels, and ATAC chromosome chunk dims.
- scmidas.pl.umap(mdata, basis='X_midas', color=[...]) and
scmidas.pl.modality_grid(model, mdata, label_key=...) wrap the
common visualizations as one-call helpers and route through a thin
AnnData wrapper to side-step the current scanpy + MuData plotting
limitations.
- ATAC binarization is now applied by default whenever 'atac' is
among the modalities; pass transform={'atac': None} to opt out.
scmidas.integrate() now uses the new path internally and writes the
joint biological latent to mdata.obsm['X_midas'] (configurable via
key_added).
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.
Other fixes:
- predict(joint_latent=False) no longer raises KeyError: 'z_c'.
15 new pytest cases pin the new API surface (setup, construct,
multi-instance independence, latent/imputation shapes, save/load
round-trip, deprecation warning).
Tutorials: - New 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. Single-file Python pipeline; ~5 min on a mid-range GPU. - Merge inputs.rst + outputs.rst into a single data_layout.rst that describes the MuData input/output contract. The directory format is moved to an "Advanced" subsection. - Rewrite demo1, demo2, demo3 to use the new API: scmidas.datasets.from_dir(...) -> setup_mudata -> MIDAS(mdata) -> get_latent_representation. The 22-line per-modality grid block in each demo collapses to scmidas.pl.modality_grid(model, mdata). Each demo gains a 6.4 "After integration" section showing Leiden + UMAP on mdata.obsm['X_midas']. - examples/quickstart.ipynb uses scmidas.pl.umap and a 5-line scanpy-friendly downstream. - Rewrite advanced/tensorboard.rst and advanced/development_instructions.rst to use the new API; the "Adding a new modality" section is reorganized into two clear steps (configs, then registry registration) with a concrete asinh5 example for transform_registry. - tutorials/basics/index.rst toctree leads with preparing_your_data and data_layout, then the three demos. API reference: - New scmidas.api.rst, scmidas.datasets.rst, scmidas.plot.rst. - modules_index.rst toctree updated. README: - Quick start uses the new API (sc.pp.neighbors(use_rep='X_midas')). - New "Bring your own data" section linking the preparing_your_data tutorial and the data_layout reference.
See docs/source/release.md for the full v0.3.0 entry.
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pytest tests/passes locally.docs/source/release.mdupdated under the next version heading.Related issues