stlearn.tl.cci.run_cci

stlearn.tl.cci.run_cci(adata: AnnData, use_label: str, spot_mixtures: bool = False, min_spots: int = 3, sig_spots: bool = True, cell_prop_cutoff: float = 0.2, p_cutoff: float = 0.05, n_perms: int = 100, n_cpus: int | None = None, verbose: bool = True)[source]

Calls significant celltype-celltype interactions based on cell-type data randomisation.

Parameters:

  • adata (AnnData) – Must have had st.tl.cci_rank.run() called prior.

  • use_label (str) – If !spot_mixtures, is a key in adata.obs, else key in adata.uns. Note if spot_mixtures specified, must have both the deconvolution data in adata.uns[use_label] and the dominant cell type per spot stored in adata.obs[use_label]. See tutorial for example.

  • spot_mixtures (bool) – If true, indicates using deconvolution data, hence use_label refers to adata.uns.

  • min_spots (int) – Specifies the minimum number of spots where LR score present to include in subsequent analysis.

  • sig_spots (bool) – If true, only consider edges which include a signficant spot from calling st.tl.cci.run()

  • cell_prop_cutoff (float) – Only relevant if spot_mixtures==True, indicates cutoff where cell type considered found in spot.

  • p_cutoff (float) – Value at which p is considered significant.

  • n_perms (int) – Number of randomisations of cell data to generate p-values. If set to 0, then performs no permutations, but still does perform raw counting of the cell type interactions with each LR hotspot. This can still be visualised downstream by setting paramters to plot significant interactions to false.

  • n_cpus (int | None) – cpu resources to use.

  • verbose (bool) – True if print dialogue to user during run-time.

Returns:

adata –

Relevant information stored
adata.uns[‘lr_summary’]

Additional columns; f”n_cci_sig_{use_label}”, f”n-spot_cci_{use_label}”, f”n-spot_cci_sig_{use_label}”. Former is the no. of CCIs significant for the LR, middle is the no. of individual spot-spot interactions across all CCIs for LR, and latter is the no. of significant individual spot interactions.

adata.uns
Dataframes added:
f”lr_cci_raw_{use_label}”

The raw count of spot-spot interactions across all LR pairs for each possible CCI.

f”lr_cci_raw_{use_label}”

The count of significant spot-spot interactions across all LR pairs for each possible CCI.

Dictionaries added:
f”per_lr_cci_pvals_{use_label}”

Each key refers to a LR, with the value being a dataframe listing the p-values for each potential CCI.

f”per_lr_cci_raw_{use_label}”

Each key refers to a LR, with the value being a dataframe listing the count of spot-spot interactions via the LR in significant LR neighbourhoods stratified by each celltype-celltype combination.

f”per_lr_cci_{use_label}”

The same as f”per_lr_cci_raw_{use_label}”, except subsetted to significant CCIs.

Return type:

AnnData