The tiledbsoma.io module¶

Functions¶

Data conversion to/from TileDB-SOMA

`tiledbsoma.io.from_h5ad`	Reads an `.h5ad` file and writes it to an `Experiment`.
`tiledbsoma.io.from_anndata`	Writes an AnnData object to an `Experiment`.
`tiledbsoma.io.to_h5ad`	Converts the experiment group to AnnData format and writes it to the specified `.h5ad` file.
`tiledbsoma.io.to_anndata`	Converts the experiment group to AnnData format.

Updating values within a TileDB-SOMA Experiment

`tiledbsoma.io.add_X_layer`	This is useful for adding X data, for example from Scanpy's `scanpy.pp.normalize_total`, `scanpy.pp.log1p`, etc.
`tiledbsoma.io.add_matrix_to_collection`	This is useful for adding X/obsp/varm/etc data, for example from Scanpy's `scanpy.pp.normalize_total`, `scanpy.pp.log1p`, etc.
`tiledbsoma.io.create_from_matrix`	Create and populate the `soma_matrix` from the contents of `matrix`.
`tiledbsoma.io.update_obs`	Given a new Pandas dataframe with desired contents, updates the SOMA experiment's entire `obs` to incorporate the changes (this is distinct from `append_obs` which adds new rows, while allowing no schema/column changes).
`tiledbsoma.io.update_var`	Given a new Pandas dataframe with desired contents, updates the SOMA experiment's specified measurement's entire `var` to incorporate the changes (this is distinct from `append_var` which adds new rows, while allowing no schema/column changes).
`tiledbsoma.io.update_matrix`	Given a `SparseNDArray` or `DenseNDArray` already opened for write, writes the new data.

Growing a TileDB-SOMA Experiment

`tiledbsoma.io.register_anndatas`	Extends registration data from the baseline, already-written SOMA experiment to include multiple H5AD input files.
`tiledbsoma.io.register_h5ads`	Extends registration data from the baseline, already-written SOMA experiment to include multiple H5AD input files.
`tiledbsoma.io.append_X`	Appends new data to an existing `X` matrix.
`tiledbsoma.io.append_obs`	Writes new rows to an existing `obs` dataframe (this is distinct from `update_obs` which mutates the entirety of the `obs` dataframe, e.g. to add/remove columns).
`tiledbsoma.io.append_var`	Writes new rows to an existing `var` dataframe (this is distinct from `update_var` which mutates the entirety of the `var` dataframe, e.g. to add/remove columns).
`tiledbsoma.io.get_experiment_shapes`	Returns the current shapes of the elements in the `Experiment`.
`tiledbsoma.io.show_experiment_shapes`	Outputs the current shapes of the elements in the `Experiment`.
`tiledbsoma.io.upgrade_experiment_shapes`	Upgrade the elements inside a SOMA `Experiment` to use the `shape` feature introduced in TileDB-SOMA 1.15.
`tiledbsoma.io.resize_experiment`	Resize the elements in the SOMA `Experiment` to fit the requested number of observations and variables.

Classes¶

class tiledbsoma.io.ExperimentAmbientLabelMapping(*, obs_axis: AxisAmbientLabelMapping, var_axes: dict[str, AxisAmbientLabelMapping], prepared: bool = False)¶

For all the to-be-appended AnnData/H5AD inputs in SOMA multi-file append-mode ingestion, this class contains information required to perform ingestion via from_h5ad or from_anndata.

This class tracks the mapping from input-data obs or var ID-column name (barcode ID, gene symbol) to SOMA join IDs for SOMA experiment obs or var, as well as any dictionary/enumeration values.

Methods

`prepare_experiment`(experiment_uri[, context])	Prepare experiment for ingestion.
`subset_for_anndata`(adata)	Return a copy of this object containing only the information necessary to ingest the specified AnnData.
`subset_for_h5ad`(h5ad_path)	Subset this plan to only contain ID maps useful for this H5AD.