How does stlearn calculates the number of L-R pairs of celltype

[caiquanyou]

How does stlearn calculates the number of L-R pairs of celltype? which part I should usor any code for tutorial.
For example, I run st.tl.cci.run_cci, and how could I get total interactive LR pair of cell A and cell B

[duypham2108]

You can check the description of the function here. I am not sure I got your question right, but you can set the sig_spots=0 and lower other parameters like min_spots

stLearn/stlearn/tools/microenv/cci/analysis.py

Lines 510 to 591 in bf91758

	################################################################################
	# Functions for calling Celltype-Celltype interactions #
	################################################################################
	def 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 = 1,
	verbose: bool = True,
	):
	"""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
	cpu resources to use.
	verbose: bool
	True if print dialogue to user during run-time.
	Returns
	-------
	adata: AnnData
	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.
	"""