tiledbsoma.Experiment¶

class tiledbsoma.Experiment(handle: tiledbsoma._tdb_handles.GroupWrapper, **kwargs: Any)¶

A collection subtype that combines observations and measurements from an individual experiment.

In single cell biology, this can represent multiple modes of measurement across a single collection of cells (i.e., a “multimodal dataset”). Within an experiment, a set of measurements on a single set of variables (i.e., features) is represented as a Measurement.

obs¶

Primary annotations on the observation axis. The contents of the soma_joinid column define the observation index domain, AKA obs_id. All observations for the Experiment must be defined in this dataframe.

Type: DataFrame

ms¶

A collection of named measurements.

Type: Collection

Example

>>> import tiledbsoma
>>> with tiledbsoma.open("/path/to/experiment") as exp:
...     # While users can interact directly with an Experiment's fields:
...     obs_df = exp.obs
...
...     # the primary use case is to run queries on the experiment data.
...     q = exp.query(
...         "mtdna",
...         obs_query=tiledbsoma.AxisQuery(value_filter="tissue == 'lung'"),
...         var_query=tiledbsoma.AxisQuery(coords=(slice(50, 100),)),
...     )
...     query_obs = q.obs().concat().to_pandas()
...     query_var = q.var().concat().to_pandas()

Lifecycle

Experimental.

__init__(handle: tiledbsoma._tdb_handles.GroupWrapper, **kwargs: Any)¶

Internal-only common initializer steps.

This function is internal; users should open TileDB SOMA objects using the create() and open() factory class methods.

Methods

`__init__`(handle, **kwargs)	Internal-only common initializer steps.
`add_new_collection`(key[, kind, uri, ...])	Adds a new sub-collection to this collection.
`add_new_dataframe`(key, *[, uri, ...])	Adds a new DataFrame to this collection.
`add_new_dense_ndarray`(key, *, cls[, uri, ...])	Adds a new DenseNDArray to this Collection.
`add_new_sparse_ndarray`(key, *, cls[, uri, ...])	Adds a new SparseNDArray to this Collection.
`axis_query`(measurement_name, *[, obs_query, ...])	Creates an axis query over this experiment.
`clear`()
`close`()	Release any resources held while the object is open.
`create`(uri, *[, platform_config, context, ...])	Creates and opens a new SOMA collection in storage.
`exists`(uri[, context, tiledb_timestamp])	Finds whether an object of this type exists at the given URI.
`get`(k[,d])
`items`()
`keys`()
`open`(uri[, mode, tiledb_timestamp, context, ...])	Opens this specific type of SOMA object.
`pop`(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
`popitem`()	as a 2-tuple; but raise KeyError if D is empty.
`set`(key, value, *[, use_relative_uri])	Adds an element to the collection.
`setdefault`(k[,d])
`update`([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
`values`()

Attributes

`closed`	True if the object has been closed.
`context`	A value storing implementation-specific configuration information.
`metadata`	The metadata of this SOMA object.
`mode`	The mode this object was opened in, either `r` or `w`.
`ms`	A collection of named measurements.
`obs`	Primary observations on the observation axis.
`soma_type`	A string describing the SOMA type of this object.
`tiledb_timestamp`	The time that this object was opened in UTC.
`tiledb_timestamp_ms`	The time this object was opened, as millis since the Unix epoch.
`uri`	Accessor for the object's storage URI.