deconvatac.pp#

Submodules#

Functions#

`highly_variable_peaks`(adata, cluster_key[, layer, ...])	Selects highly variable features the "var" way.
`highly_accessible_peaks`(adata[, layer, ...])	Selects the most accessible peaks from the given AnnData object.
`reads_to_fragments`(→ None)	Convert scATAC-seq read counts to appoximate fragment counts.

Package Contents#

deconvatac.pp.highly_variable_peaks(adata: anndata.AnnData, cluster_key: str, layer: str = None, scale: float = 1, n_top_features: int = 20000)#

Selects highly variable features the “var” way.

Adapted from: GreenleafLab/ArchR

Parameters#

adata: AnnData: AnnData object of the (reference) scATAC data.
cluster_key: str: Name of column in adata.obs containing the clusters.
layer: str: Layer of the raw counts. If None, uses .X
scale: float: Scale factor, i.e. log2((sums/feature_sums) *scale + 1).
n_top_features: int: How many features to select.

Returns#

Saves a boolean indicator of the HVPs in place to adata.var[‘highly_variable’].

deconvatac.pp.highly_accessible_peaks(adata: anndata.AnnData, layer: str = None, n_top_features: int = 20000, copy: bool = False)#

Selects the most accessible peaks from the given AnnData object.

Parameters#

adata: AnnData: Annotated data object containing the peaks.
layer: str: Name of the layer to use for peak accessibility.
n_top_features: int: Number of top accessible peaks to select.
copy: bool: Whether to copy the AnnData object.

Returns#

AnnData object with the highly accessible peaks saved to adata.var[‘highly_accessible’].

deconvatac.pp.reads_to_fragments(adata: anndata.AnnData, read_layer: str | None = None, fragment_layer: str = 'fragments') → None#

Convert scATAC-seq read counts to appoximate fragment counts.

Parameters#

adata: AnnData object that contains read counts.
read_layer: Key in`.layer` that the read counts are stored in.
fragment_layer: Key in`.layer` that the fragment counts will be stored in.

Returns#

Adds layer with fragment counts in .layers[fragment_layer].