%matplotlib inline

Receptor-ligand analysis

This example shows how to run the receptor-ligand analysis.

It uses an efficient re-implementation of the cellphonedb algorithm which can handle large number of interacting pairs (100k+) and cluster combinations (100+).

See also

See Neighbors enrichment analysis for finding cluster neighborhood with squidpy.gr.nhood_enrichment().

import squidpy as sq

adata = sq.datasets.seqfish()
adata

AnnData object with n_obs × n_vars = 19416 × 351
    obs: 'Area', 'celltype_mapped_refined'
    uns: 'celltype_mapped_refined_colors'
    obsm: 'X_umap', 'spatial'

To get started, we just need an anndata.AnnData object with some clustering information. Below are some useful parameters of squidpy.gr.ligrec:

• n_perms - number of permutations for the permutation test.

• interactions - list of interaction, by default we fetch all available interactions from omnipath.

• {interactions,transmitter,receiver}_params - parameters used if downloading the interactions, see omnipah.interactions.import_intercell_network for more information.

• threshold - percentage of cells required to be expressed in a given cluster.

• corr_method - false discovery rate (FDR) correction method to use.

Since we’re interested in receptors and ligands in this example, we specify these categories in receiver_params and transmitter_params, respectively. If desired, we can also restrict the resources to just a select few. For example, in order to only use cellphonedb, set interactions_params={'resources': 'CellPhoneDB'}.

res = sq.gr.ligrec(
    adata,
    n_perms=1000,
    cluster_key="celltype_mapped_refined",
    copy=True,
    use_raw=False,
    transmitter_params={"categories": "ligand"},
    receiver_params={"categories": "receptor"},
)

First, we inspect the calculated means. The resulting object is a pandas.DataFrame, with rows corresponding to interacting pairs and columns to cluster combinations.

res["means"].head()

	cluster_1	Allantois										...	Surface ectoderm
	cluster_2	Allantois	Anterior somitic tissues	Cardiomyocytes	Cranial mesoderm	Definitive endoderm	Dermomyotome	Endothelium	Erythroid	Forebrain/Midbrain/Hindbrain	Gut tube	...	Lateral plate mesoderm	Low quality	Mixed mesenchymal mesoderm	NMP	Neural crest	Presomitic mesoderm	Sclerotome	Spinal cord	Splanchnic mesoderm	Surface ectoderm
source	target
KDR	PECAM1	0.142857	0.294237	0.150901	0.233090	0.183007	0.187666	1.475811	0.214416	0.160415	0.190973	...	0.271419	0.302056	0.277021	0.254229	0.318590	0.274281	0.287045	0.242653	0.235369	0.276529
IGF1	KDR	0.162338	0.484172	0.250242	0.232278	0.164777	0.246182	2.253158	0.192922	0.141502	0.169111	...	0.327692	0.292735	0.289093	0.230291	0.306367	0.368467	0.286925	0.251546	0.270060	0.323970
PDGFA	KDR	0.240260	0.562094	0.328164	0.310200	0.242699	0.324104	2.331080	0.270844	0.219424	0.247033	...	1.748416	1.713459	1.709817	1.651015	1.727091	1.789191	1.707649	1.672270	1.690784	1.744694
NRP1	KDR	0.532468	0.854302	0.620372	0.602408	0.534906	0.616312	2.623288	0.563052	0.511632	0.539240	...	0.558029	0.523072	0.519430	0.460628	0.536704	0.598804	0.517262	0.481883	0.500397	0.554307
FGF3	FGFR3	0.324675	0.162744	0.288913	0.216180	0.467593	0.204117	0.191248	0.139589	0.705757	0.270203	...	0.255896	0.234674	0.632132	0.557881	0.299989	0.268179	0.268655	0.478236	0.385616	0.393883

5 rows × 484 columns

Next, we take a look at the p-values. If corr_method != None, this will contained the corrected p-values. The p-values marked as [NaN]{.title-ref} correspond to interactions, which did not pass the filtering threshold specified above.

res["pvalues"].head()

	cluster_1	Allantois										...	Surface ectoderm
	cluster_2	Allantois	Anterior somitic tissues	Cardiomyocytes	Cranial mesoderm	Definitive endoderm	Dermomyotome	Endothelium	Erythroid	Forebrain/Midbrain/Hindbrain	Gut tube	...	Lateral plate mesoderm	Low quality	Mixed mesenchymal mesoderm	NMP	Neural crest	Presomitic mesoderm	Sclerotome	Spinal cord	Splanchnic mesoderm	Surface ectoderm
source	target
KDR	PECAM1	1.000	0.941	1.000	0.999	1.000	1.00	0.0	1.000	1.000	1.000	...	1.000	1.0	1.0	1.000	1.0	1.000	0.999	1.0	1.000	1.000
IGF1	KDR	1.000	0.131	0.999	1.000	1.000	1.00	0.0	1.000	1.000	1.000	...	0.996	1.0	1.0	0.996	1.0	0.745	0.910	1.0	1.000	0.990
PDGFA	KDR	1.000	0.321	0.999	0.999	1.000	1.00	0.0	1.000	1.000	1.000	...	0.000	0.0	0.0	0.000	0.0	0.000	0.000	0.0	0.000	0.000
NRP1	KDR	0.909	0.074	0.823	0.885	0.967	0.83	0.0	0.939	0.993	0.975	...	1.000	1.0	1.0	1.000	1.0	0.992	0.991	1.0	1.000	1.000
FGF3	FGFR3	0.767	1.000	1.000	1.000	0.076	1.00	1.0	1.000	0.000	1.000	...	1.000	1.0	0.0	0.004	1.0	1.000	0.975	0.0	0.608	0.389

5 rows × 484 columns

Any interaction metadata downloaded from omnipath, such as the interaction type, can be accessed as:

res["metadata"].head()

		aspect_intercell_source	aspect_intercell_target	category_intercell_source	category_intercell_target	category_source_intercell_source	category_source_intercell_target	consensus_direction	consensus_inhibition	consensus_score_intercell_source	consensus_score_intercell_target	...	scope_intercell_source	scope_intercell_target	secreted_intercell_source	secreted_intercell_target	sources	transmitter_intercell_source	transmitter_intercell_target	type	uniprot_intercell_source	uniprot_intercell_target
source	target
KDR	PECAM1	functional	functional	ligand	receptor	resource_specific	resource_specific	True	False	1	10	...	generic	generic	True	False	Wang	True	False	post_translational	P35968	P16284
IGF1	KDR	functional	functional	ligand	receptor	resource_specific	resource_specific	True	False	19	23	...	generic	generic	True	True	Wang	True	False	post_translational	P05019	P35968
PDGFA	KDR	functional	functional	ligand	receptor	resource_specific	resource_specific	True	False	18	23	...	generic	generic	True	True	Wang	True	False	post_translational	P04085	P35968
NRP1	KDR	functional	functional	ligand	receptor	resource_specific	resource_specific	True	False	2	23	...	generic	generic	True	True	BioGRID;CellChatDB-cofactors;HPRD;Wang	True	False	post_translational	O14786	P35968
FGF3	FGFR3	functional	functional	ligand	receptor	resource_specific	resource_specific	False	False	17	19	...	generic	generic	True	True	Baccin2019;CellCall;CellPhoneDB;CellTalkDB;Cel...	True	False	post_translational	P11487	P22607

5 rows × 41 columns

In order to plot the results, we can run squidpy.pl.ligrec. Some useful parameters are:

• {source,target}_groups - only plot specific source/target clusters.

• dendrogram - whether to hierarchically cluster the rows, columns or both.

• mean_range - plot only interactions whose means are in this range.

• pval_threshold - plot only interactions whose p-values are below this threshold.

In the plot below, to highlight significance, we’ve marked all p-values <= 0.005 with tori.

sq.pl.ligrec(res, source_groups="Erythroid", alpha=0.005)