FACSPy.tl.fop

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FACSPy.tl.fop#

FACSPy.tl.fop(adata, layer='compensated', cutoff=None, groupby='sample_ID', use_only_fluo=False, aggregate=False, copy=False)#

Calculates the frequency of parents. For the calculation, cutoffs per channel are necessary that can be supplied as a singular value, a list of values or via the cofactors. FOPs are calculated for all gates.

Parameters:

  • adata (AnnData) – The anndata object of shape n_obs x n_vars where rows correspond to cells and columns to the channels.

  • layer (Union[list[str], str]) – The layer corresponding to the data matrix. Similar to the gate parameter, it has a default stored in fp.settings which can be overwritten by user input. Multiple layers can be passed as a list.

  • cutoff (Optional[Union`[:py:class:`list[int], list[float], int, float]]) – Intensity cutoff above which cells are counted as positive. Can be a singular value, a value per channel. If None, values will be pulled from .var[‘cofactors’] or from .uns[‘cofactors’].

  • groupby (Union[Literal['sample_ID'], str]) – Argument to specify the grouping. Defaults to sample_ID, which will calculate the FOP per sample. Can be any column from the .obs slot of adata.

  • use_only_fluo (bool) – Parameter to specify if the FOP should only be calculated for the fluorescence channels. Defaults to False.

  • aggregate (bool) – If False, calculates the FOP as if groupby==[‘sample_ID’, groupby]. If set to True, the FOP is calculated per entry in .obs[groupby]. Defaults to False.

  • copy (bool) – Whether to copy the dataset.

Returns:

Returns adata if copy = True, otherwise adds fields to the anndata object:

.uns[f’fop_{groupby}_{layer}’]

calculated FOP values per channel

.uns[‘settings’][f’_fop_{groupby}_{layer}’]

Settings that were used for calculation

Return type:

AnnData or None

Examples

>>> import FACSPy as fp
>>> dataset
AnnData object with n_obs × n_vars = 615936 × 22
obs: 'sample_ID', 'file_name', 'condition', 'sex'
var: 'pns', 'png', 'pne', 'pnr', 'type', 'pnn', 'cofactors'
uns: 'metadata', 'panel', 'workspace', 'gating_cols', 'dataset_status_hash'
obsm: 'gating'
layers: 'compensated', 'transformed'
>>> fp.tl.fop(dataset)

>>> import FACSPy as fp
>>> dataset
AnnData object with n_obs × n_vars = 615936 × 22
obs: 'sample_ID', 'file_name', 'condition', 'sex'
var: 'pns', 'png', 'pne', 'pnr', 'type', 'pnn', 'cofactors'
uns: 'metadata', 'panel', 'workspace', 'gating_cols', 'dataset_status_hash'
obsm: 'gating'
layers: 'compensated', 'transformed'
>>> fp.settings.default_gate = "T_cells"
>>> fp.settings.default_layer = "transformed"
>>> fp.tl.pca(dataset)
>>> fp.tl.neighbors(dataset)
>>> fp.tl.leiden(dataset)
>>> fp.tl.fop(dataset,
...           groupby = "T_cells_transformed_leiden",
...           aggregate = True) # will calculate FOP per leiden cluster
>>> fp.tl.fop(dataset,
...           groupby = "T_cells_transformed_leiden",
...           aggregate = False) # will calculate FOP per leiden cluster and sample_ID
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