dropFeatures

dropFeatures ¶

dropFeatures(
    data,
    dropMarkers=None,
    dropCells=None,
    dropMetaColumns=None,
    dropGroups=None,
    groupsColumn=None,
    subsetRaw=True,
    verbose=True,
    outputDir=None,
    sdataTable=None,
)

Drop markers, cells, metadata columns, or phenotype groups.

Everything you pass is dropped in one pass, in this order: groups, then cells, then markers, then obs columns. Dropping markers also prunes .raw when subsetRaw=True, which keeps .X and .raw.X describing the same variables — forget that and later calls with layer='raw' will disagree with .X.

SpatialData tables need their annotation columns

For SpatialData input, keep the table's region_key and instance_key obs columns. Dropping them makes the table fail TableModel validation on write-back.

Parameters:

Name	Type	Description	Default
`data`	`(AnnData \| SpatialData \| str, required)`	The cell table. An `AnnData`, a path to an `.h5ad` file, a `SpatialData` object, or a path to a `.zarr` SpatialData store.	required
`dropMarkers`	`str \| list`	Markers to remove from `var`.	`None`
`dropCells`	`str \| list`	Cell names (`obs_names`) to remove.	`None`
`dropMetaColumns`	`str \| list`	Columns to remove from `obs`. Columns that are not present are ignored.	`None`
`dropGroups`	`str \| list`	Values of `groupsColumn` whose cells should be removed — e.g. dropping every `'Unknown'` cell.	`None`
`groupsColumn`	`str`	Column in `obs` that `dropGroups` refers to. Required when `dropGroups` is given.	`None`
`subsetRaw`	`bool`	Apply `dropMarkers` to `.raw` as well as to `.X`.	`True`
`verbose`	`bool`	Print the resulting shape.	`True`
`outputDir`	`str`	Directory to write the updated object to. With `None` the object is returned instead.	`None`
`sdataTable`	`str`	Which `SpatialData` table to work on.	`None`

Returns:

Name	Type	Description
`adata`	`AnnData \| SpatialData \| None`	The reduced object. `None` when `outputDir` is given.

Raises:

Type	Description
`ValueError`	When `dropGroups` is given without `groupsColumn`.

Example

# Drop a bad channel and every unphenotyped cell.
adata = sp.pp.dropFeatures(
    adata,
    dropMarkers=["DNA_6"],
    dropGroups=["Unknown"],
    groupsColumn="phenotype",
)

# Tidy up working columns before saving.
adata = sp.pp.dropFeatures(adata, dropMetaColumns=["kmeans", "leiden"])