TenxHdf5Dataset

TenxHdf5Dataset

Dataset in the 10X HDF5 feature-barcode matrix format, see here for details.

Constructor

new TenxHdf5Dataset(h5File)

Source:
Parameters:
Name Type Description
h5File SimpleFile | string | Uint8Array | File

Contents of a HDF5 file in the 10X feature-barcode format. On browsers, this may be a File object. On Node.js, this may also be a string containing a file path.

Classes

TenxHdf5Dataset

Methods

abbreviate() → {object}

Source:
Returns:

Object containing the abbreviated details of this dataset, in a form that can be cheaply stringified.

Type
object

clear()

Description:

  • Destroy caches if present, releasing the associated memory. This may be called at any time but only has an effect if cache = true in load or {@linkcodeTenxHdf5Dataset#summary summary}.

Source:

load(optionsopt) → {object}

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Parameters:
Name Type Attributes Default Description
options object <optional>
 {}

Optional parameters.

Properties
Name Type Attributes Default Description
cache boolean <optional>
 false

Whether to cache the intermediate results for re-use in subsequent calls to any methods with a cache option. If true, users should consider calling clear to release the memory once this dataset instance is no longer needed.
Returns:

Object containing the per-feature and per-cell annotations. This has the following properties:

  • features: an object where each key is a modality name and each value is a DataFrame of per-feature annotations for that modality.
  • cells: a DataFrame containing per-cell annotations.
  • matrix: a MultiMatrix containing one ScranMatrix per modality.
  • primary_ids: an object where each key is a modality name and each value is an array (usually of strings) containing the primary feature identifiers for each row in that modality.

Modality names are guaranteed to be one of "RNA", "ADT" or "CRISPR". We assume that the instance already contains an appropriate mapping from the observed feature types to each expected modality, either from the defaults or with setOptions.

If the feature annotation lacks information about the feature types, it is assumed that all features are genes, i.e., only the RNA modality is present.

Type
object

options() → {object}

Source:
Returns:

Object containing all options used for loading.

Type
object

previewPrimaryIds(optionsopt) → {object}

Source:
Parameters:
Name Type Attributes Default Description
options object <optional>
 {}

Optional parameters.

Properties
Name Type Attributes Default Description
cache boolean <optional>
 false

Whether to cache the intermediate results for re-use in subsequent calls to any methods with a cache option. If true, users should consider calling clear to release the memory once this dataset instance is no longer needed.
Returns:

An object where each key is a modality name and each value is an array (usually of strings) containing the primary feature identifiers for each row in that modality. The contents are the same as the primary_ids returned by load but the order of values may be different.

Type
object

serialize() → {object}

Source:
Returns:

Object describing this dataset, containing:

  • files: Array of objects representing the files used in this dataset. Each object corresponds to a single file and contains:
    • type: a string denoting the type.
    • file: a SimpleFile object representing the file contents.
  • options: An object containing additional options to saved.
Type
object

setOptions(options)

Source:
Parameters:
Name Type Description
options object

Optional parameters that affect load (but not summary).

Properties
Name Type Attributes Description
featureTypeRnaName string <optional>
<nullable>

Name of the feature type for gene expression. If null or the string is not present among the feature types, no RNA features are to be loaded.

If no feature type information is available in the dataset, all features are considered to be genes by default. This behavior can also be explicitly requested by setting this argument to the only non-null value among all featureType*Name parameters.
featureTypeAdtName string <optional>
<nullable>

Name of the feature type for ADTs. If null or the string is not present among the feature types, no ADT features are to be loaded.

If no feature type information is available in the dataset and this argument is set to the only non-null value among all featureType*Name parameters, all features are considered to be ADTs.
featureTypeCrisprName string <optional>
<nullable>

Name of the feature type for CRISPR guides. If null or the string is not present among the feature types, no guides are to be loaded.

If no feature type information is available in the dataset and this argument is set to the only non-null value among all featureType*Name parameters, all features are considered to be guides.
primaryRnaFeatureIdColumn string | number <optional>

Name or index of the column of the features DataFrame that contains the primary feature identifier for gene expression. If i is invalid (e.g., out of range index, unavailable name), it is ignored and the primary identifier is treated as undefined.
primaryAdtFeatureIdColumn string | number <optional>

Name or index of the column of the features DataFrame that contains the primary feature identifier for the ADTs. If i is invalid (e.g., out of range index, unavailable name), it is ignored and the primary identifier is treated as undefined.
primaryCrisprFeatureIdColumn string | number <optional>

Name or index of the column of the features DataFrame that contains the primary feature identifier for the CRISPR guides. If i is invalid (e.g., out of range index, unavailable name), it is ignored and the primary identifier is treated as undefined.

summary(optionsopt) → {object}

Source:
Parameters:
Name Type Attributes Default Description
options object <optional>
 {}

Optional parameters.

Properties
Name Type Attributes Default Description
cache boolean <optional>
 false

Whether to cache the intermediate results for re-use in subsequent calls to any methods with a cache option. If true, users should consider calling clear to release the memory once this dataset instance is no longer needed.
Returns:

Object containing the per-feature and per-cell annotations. This has the following properties:

  • modality_features: an object where each key is a modality name and each value is a DataFrame of per-feature annotations for that modality. Unlike load, modality names are arbitrary.
  • cells: a DataFrame of per-cell annotations.
Type
object

(static) defaults() → {object}

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Returns:

Default options, see setOptions for more details.

Type
object

(static) format() → {string}

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Returns:

Format of this dataset class.

Type
string

(async, static) unserialize(files, options) → {TenxHdf5Dataset}

Source:
Parameters:
Name Type Description
files Array

Array of objects like that produced by serialize.
options object

Object containing additional options to be passed to the constructor.
Returns:

A new instance of this class.

Type
TenxHdf5Dataset