Notebook

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%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+).

::: seealso See sphx_glr_auto_examples_graph_compute_nhood_enrichment.py for finding cluster neighborhood with squidpy.gr.nhood_enrichment. :::

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import squidpy as sq

adata = sq.datasets.seqfish()
adata

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'}.

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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.

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res["means"].head()

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.

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res["pvalues"].head()

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

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res["metadata"].head()

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.

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