Cell-Cell Communication#

Cell-cell communication (CCC) refers to the process by which cells interact with each other, using molecular signals. The signals can be transmitted through various mechanisms, such as the release of signaling molecules called ligands by one cell that bind to receptors on another cell, or through direct physical contact between cells. CCC plays a critical role in a wide range of biological processes, including development, immune responses, and tissue repair, among others. In this section, we will provide a concise overview of the process for conducting CCC analysis using Stereopy.

Cell clustering#

Download our example data, and complete basic analysis processing.

[1]:

import stereo as st
import warnings
warnings.filterwarnings('ignore')

Before proceeding with the cell-cell communication analysis, make sure to perform the necessary cell clustering analysis if it hasn’t been completed yet.

[2]:

data = st.io.read_h5ad('../test_data/mouse_embryo_heart_new.h5ad')
# preprocessing
data.tl.cal_qc()

# data.plt.genes_count is a good option to observe gene distribution before filtering
# data.tl.filter_cells(min_gene=1, max_gene=2000, min_n_genes_by_counts=3, max_n_genes_by_counts=800, pct_counts_mt=6, inplace=True)

data.tl.raw_checkpoint()
data.tl.normalize_total()

# clustering
data.tl.pca(n_pcs=50, res_key='pca', use_highly_genes=False)
data.tl.neighbors(pca_res_key='pca', res_key='neighbors')
data.tl.leiden(neighbors_res_key='neighbors', res_key='leiden')
# data.plt.cluster_scatter(res_key='leiden', plotting_scale_width=2)

[2023-11-15 11:35:58][Stereo][21850][MainThread][139852866279232][st_pipeline][41][INFO]: start to run cal_qc...
[2023-11-15 11:35:59][Stereo][21850][MainThread][139852866279232][st_pipeline][44][INFO]: cal_qc end, consume time 0.5781s.
[2023-11-15 11:35:59][Stereo][21850][MainThread][139852866279232][st_pipeline][41][INFO]: start to run normalize_total...
[2023-11-15 11:35:59][Stereo][21850][MainThread][139852866279232][st_pipeline][44][INFO]: normalize_total end, consume time 0.2943s.
[2023-11-15 11:35:59][Stereo][21850][MainThread][139852866279232][st_pipeline][41][INFO]: start to run pca...
[2023-11-15 11:35:59][Stereo][21850][MainThread][139852866279232][dim_reduce][78][WARNING]: svd_solver: auto can not be used with sparse input.
Use "arpack" (the default) instead.
[2023-11-15 11:36:34][Stereo][21850][MainThread][139852866279232][st_pipeline][44][INFO]: pca end, consume time 34.5284s.
[2023-11-15 11:36:34][Stereo][21850][MainThread][139852866279232][st_pipeline][41][INFO]: start to run neighbors...
[2023-11-15 11:40:43][Stereo][21850][MainThread][139852866279232][st_pipeline][44][INFO]: neighbors end, consume time 249.7218s.
[2023-11-15 11:40:43][Stereo][21850][MainThread][139852866279232][st_pipeline][41][INFO]: start to run leiden...
[2023-11-15 11:41:10][Stereo][21850][MainThread][139852866279232][st_pipeline][44][INFO]: leiden end, consume time 26.3186s.

Note

Since this example data already have cell type labels stored in obs, we can omit the clustering step and proceed directly to the next section.

[3]:

data.plt.cluster_scatter(res_key='celltype', show_plotting_scale=False)

[3]:
../_images/Tutorials_Cell_Cell_Communication_8_2.png

Spatial information incorperation#

To incorperate the spatial information and ensure the accuracy of the signaling model, we assume that intercellular ligand-receptor (L-R) communications routinely exist among closely neighboring cells.

Before conducting the communication analysis, it is advisable to filter the cells that are located in close proximity to each other. This step ensures that the cells considered for communication analysis are physically close enough to facilitate actual communication. Nevertheless, you can always skip this step and proceed directly to the actual CCC analysis part using the entire dataset.

In Stereopy, we provide two approaches for performing spatial filteration.

Microenvironment#

The first step is input microenvironment information into the communication analysis. It treats each cell type as a cohesive unit, and microenvironments are formed by combining two or more closely located cell types. Microenvironments can be calculated with data.tl.gen_ccc_micro_envs. We provide two approaches when calculating microenvironments: one is using the minimum spanning tree (MST); the other is through splitting the fully connected network into multiple strongly connected components.

This function should be ran twice because it includes two parts:

  • Calculating how clusters are divided into microenvironments under diffrent thresholds.

  • Generating microenvironments by choosing an appropriate method and threshold based on the result of first part.

More details refer to API.

[4]:

data.tl.gen_ccc_micro_envs(
            cluster_res_key='celltype',
            res_key='ccc_micro_envs'
            )

[2023-11-15 11:41:14][Stereo][21850][MainThread][139852866279232][st_pipeline][77][INFO]: register algorithm gen_ccc_micro_envs to <stereo.core.st_pipeline.AnnBasedStPipeline object at 0x7f31b4323340>

Now, you can choose a appropriate threshold based on this function's result.

[4]:

Note

The coordinates must be stored as x and y in obs, or spatial in obsm.

The final bootstrap MST is stored in mst_final and pairwise KL-divergences are stored in pairwise_kl_divergence of uns['ccc_micro_envs']. Pick a proper method and threshold to get the final microenvironments.

[5]:

data.tl.gen_ccc_micro_envs(
            method='split',
            threshold=2,
            res_key='ccc_micro_envs'
            )

Using the ‘split’ method with theshold 2, we got two microenvironments as follows:

[6]:

data.tl.result['ccc_micro_envs']['micro_envs']

[6]:
cell_type microenvironment
0 endocardial/endothelial (EC) microenv_0
1 ventricular-specific CM microenv_0
2 atrial-specific CM microenv_0
3 epicardial (EP) microenv_0
4 blood microenv_1
5 fibro-mesenchymal (FM) microenv_1

Niche#

The alternative filteration approach constructs a niche for two given cell types at individual cell level. For each cell of type 1, only cells of type 2 that are within a certain Euclidean distance threshold, denoted as niche_distance, are retained. The niche of cell type 1 and 2 is constructed by including all cells of type 1 that have type 2 neighbors, as well as all their neighboring type 2 cells.

You can run data.tl.get_niche to get a new data object based on a pair of cell clusters, which represents a niche.

Afterwards, run communication analysis based on this niche.

More in API.

[7]:

data_niche = data.tl.get_niche(
                            niche_distance=0.025,
                            cluster_1='epicardial (EP)',
                            cluster_2='ventricular-specific CM',
                            cluster_res_key='celltype'
                            )
data_niche

[2023-11-15 11:41:21][Stereo][21850][MainThread][139852866279232][st_pipeline][77][INFO]: register algorithm get_niche to <stereo.core.st_pipeline.AnnBasedStPipeline object at 0x7f31b4323340>

[7]:

AnnData object with n_obs × n_vars = 14792 × 30254
    obs: 'ctype_user', 'slice', 'seurat_clusters', 'celltype', 'DBSCAN', 'total_counts', 'n_genes_by_counts', 'pct_counts_mt', 'leiden'
    var: 'n_cells', 'n_counts', 'mean_umi'
    uns: 'sn', 'pca', 'pca_variance_ratio', 'neighbors', 'leiden', 'gene_exp_leiden', 'ccc_micro_envs'
    obsm: 'spatial', 'spatial_regis', 'X_pca'
    obsp: 'connectivities', 'distances'

Communication analysis#

We suggest using normalized non-log-transformed data to do the analysis.

analysis_type can be set to simple or statistical. simple does not rely on any statistics and only provides the mean expression values for each interaction for each possible cell type pair, while statistical also estimates the statistical significance of these mean expression values using a permutation approach.

This function currently supports the species of HUMAN and MOUSE. If input the data of other species, you have to translate the genes to homologous genes of human or mouse. Then select a database from cellphonedb, liana and celltalkdb [Efremova20], or input the path of your own database.

Note

HUMAN species can not be used with celltalkdb database for the moment.

You can incorperate the spatial information by specifying microenvironments with parameter micro_envs:

[8]:

data.tl.cell_cell_communication(
                analysis_type='statistical',
                cluster_res_key='celltype',
                species='MOUSE',
                database='liana',
                micro_envs='ccc_micro_envs',
                res_key='cell_cell_communication'
                )

[2023-11-15 11:41:38][Stereo][21850][MainThread][139852866279232][st_pipeline][77][INFO]: register algorithm cell_cell_communication to <stereo.core.st_pipeline.AnnBasedStPipeline object at 0x7f31b4323340>
[2023-11-15 11:41:38][Stereo][21850][MainThread][139852866279232][main][128][INFO]: species: MOUSE
[2023-11-15 11:41:38][Stereo][21850][MainThread][139852866279232][main][129][INFO]: database: liana
[2023-11-15 11:43:06][Stereo][21850][MainThread][139852866279232][main][188][INFO]: [statistical analysis] Threshold:0.1 Precision:3 Iterations:500 Threads:1
[2023-11-15 11:43:29][Stereo][21850][MainThread][139852866279232][main][218][INFO]: Running Real Analysis
[2023-11-15 11:43:29][Stereo][21850][MainThread][139852866279232][main][761][INFO]: Limiting cluster combinations using microenvironments
[2023-11-15 11:43:29][Stereo][21850][MainThread][139852866279232][main][232][INFO]: Running Statistical Analysis
statistical analysis: 100%|███████████████████████████████████████| 500/500 [26:45<00:00,  3.21s/it]
[2023-11-15 12:10:14][Stereo][21850][MainThread][139852866279232][main][1029][INFO]: Building Pvalues result
[2023-11-15 12:10:15][Stereo][21850][MainThread][139852866279232][main][1064][INFO]: Building results

Or directly perform the analysis on the niche StereoExpData:

[9]:

data_niche.tl.cell_cell_communication(
                        analysis_type='statistical',
                        cluster_res_key='celltype',
                        species='MOUSE',
                        database='liana',
                        threshold=0.1,
                        res_key='cell_cell_communication'
                        )

[2023-11-15 12:10:22][Stereo][21850][MainThread][139852866279232][st_pipeline][77][INFO]: register algorithm cell_cell_communication to <stereo.core.st_pipeline.AnnBasedStPipeline object at 0x7f2ff4f46160>
[2023-11-15 12:10:22][Stereo][21850][MainThread][139852866279232][main][128][INFO]: species: MOUSE
[2023-11-15 12:10:22][Stereo][21850][MainThread][139852866279232][main][129][INFO]: database: liana
[2023-11-15 12:10:36][Stereo][21850][MainThread][139852866279232][main][188][INFO]: [statistical analysis] Threshold:0.1 Precision:3 Iterations:500 Threads:1
[2023-11-15 12:10:40][Stereo][21850][MainThread][139852866279232][main][218][INFO]: Running Real Analysis
[2023-11-15 12:10:40][Stereo][21850][MainThread][139852866279232][main][232][INFO]: Running Statistical Analysis
statistical analysis: 100%|███████████████████████████████████████| 500/500 [04:35<00:00,  1.81it/s]
[2023-11-15 12:15:16][Stereo][21850][MainThread][139852866279232][main][1029][INFO]: Building Pvalues result
[2023-11-15 12:15:16][Stereo][21850][MainThread][139852866279232][main][1064][INFO]: Building results

You could also set subsampling=True to enable subsampling of the cells for faster performance.

Result observation#

The results of cell-cell communication are stored in data.tl.result.

The means result shows the mean expression values of each L-R pair for each cell cluster pair.

[10]:

# mean
data_niche.tl.result['cell_cell_communication']['means']

[10]:
id_cp_interaction interacting_pair partner_a partner_b gene_a gene_b secreted receptor_a receptor_b annotation_strategy is_integrin epicardial (EP)|epicardial (EP) epicardial (EP)|ventricular-specific CM ventricular-specific CM|epicardial (EP) ventricular-specific CM|ventricular-specific CM
1 CPI-SS0A7B487D4 KLRG2_WNT11 simple:A4D1S0 simple:O96014 KLRG2 WNT11 False False False user_curated False 0.000 0.000 0.130 0.036
2 CPI-SS0C08F5056 FZD9_WNT11 simple:O00144 simple:O96014 FZD9 WNT11 False False False user_curated False 0.136 0.042 0.143 0.050
3 CPI-SS029839DC3 MUSK_WNT11 simple:O15146 simple:O96014 MUSK WNT11 False False False user_curated False 0.000 0.000 0.129 0.036
4 CPI-SS0F653A282 FZD6_WNT11 simple:O60353 simple:O96014 FZD6 WNT11 False False False user_curated False 0.156 0.063 0.153 0.060
5 CPI-SS070E5DE9E FZD7_WNT11 simple:O75084 simple:O96014 FZD7 WNT11 False False False user_curated False 0.265 0.172 0.197 0.104
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4675 CPI-SS0AEC3600B NTNG2_LRRC4C simple:Q96CW9 simple:Q9HCJ2 NTNG2 LRRC4C False False False user_curated False 0.063 0.061 0.053 0.051
4677 CPI-SS0F2FBF1F6 ROBO3_NELL2 simple:Q96MS0 simple:Q99435 ROBO3 NELL2 False False False user_curated False 0.094 0.113 0.105 0.124
4678 CPI-SS0777989FE DCBLD2_SEMA4B simple:Q96PD2 simple:Q9NPR2 DCBLD2 SEMA4B False False False user_curated False 0.611 0.619 0.497 0.506
4681 CPI-SS0F30C777C LRRC4C_NTNG1 simple:Q9HCJ2 simple:Q9Y2I2 LRRC4C NTNG1 False False False user_curated False 0.057 0.063 0.055 0.060
4682 CPI-SS0F0C92F50 RTN4_TNFRSF19 simple:Q9NQC3 simple:Q9NS68 RTN4 TNFRSF19 False False False user_curated False 1.697 1.683 1.435 1.421

3225 rows × 15 columns

If done statistical analysis, the significant_means results only keeps statistical significant mean values in the mean result (non-significant means have a value of -1).

[11]:

# significant mean
data_niche.tl.result['cell_cell_communication']['significant_means'].iloc[:10]

[11]:
id_cp_interaction interacting_pair partner_a partner_b gene_a gene_b secreted receptor_a receptor_b annotation_strategy is_integrin rank epicardial (EP)|epicardial (EP) epicardial (EP)|ventricular-specific CM ventricular-specific CM|epicardial (EP) ventricular-specific CM|ventricular-specific CM
507 CPI-SS020F28ACA PKM_CD44 simple:P14618 simple:P16070 PKM CD44 False False False user_curated False 0.25 -1.000000 -1.000 -1.000 25.476999
505 CPI-SS010B52BDD VCAN_CD44 simple:P13611 simple:P16070 VCAN CD44 False False False user_curated False 0.25 -1.000000 -1.000 -1.000 20.889999
3051 CPI-SS048D2A753 APP_RPSA simple:P05067 simple:P08865 APP RPSA False False False user_curated False 0.25 13.528000 -1.000 -1.000 -1.000000
1344 CPI-SS0375E45FC DCN_ERBB4 simple:P07585 simple:Q15303 DCN ERBB4 False False False user_curated False 0.25 -1.000000 3.890 -1.000 -1.000000
2940 CPI-SS042A6F835 IGF2_IGF2R simple:P01344 simple:P11717 IGF2 IGF2R False False False user_curated False 0.50 12.075000 12.825 -1.000 -1.000000
3944 CPI-SS016BE33C2 NCL_PTN simple:P19338 simple:P21246 NCL PTN False False False user_curated False 0.50 9.104000 -1.000 8.968 -1.000000
497 CPI-SS0BCAB7F53 VIM_CD44 simple:P08670 simple:P16070 VIM CD44 False False False user_curated False 0.50 24.686001 25.083 -1.000 -1.000000
495 CPI-SS0EF14F3FB COL1A2_CD44 simple:P08123 simple:P16070 COL1A2 CD44 False False False user_curated False 0.50 22.490999 22.888 -1.000 -1.000000
1951 CPI-SS052990001 ITGB1_VCAN simple:P05556 simple:P13611 ITGB1 VCAN False False False user_curated False 0.50 -1.000000 3.159 -1.000 2.908000
490 CPI-SS0EAB0009F FN1_CD44 simple:P02751 simple:P16070 FN1 CD44 False False False user_curated False 0.50 24.709999 25.107 -1.000 -1.000000

The pvalues result shows the p-values for each mean value in the means result.

[12]:

# p-value
data_niche.tl.result['cell_cell_communication']['pvalues']

[12]:
id_cp_interaction interacting_pair partner_a partner_b gene_a gene_b secreted receptor_a receptor_b annotation_strategy is_integrin epicardial (EP)|epicardial (EP) epicardial (EP)|ventricular-specific CM ventricular-specific CM|epicardial (EP) ventricular-specific CM|ventricular-specific CM
1 CPI-SS0A7B487D4 KLRG2_WNT11 simple:A4D1S0 simple:O96014 KLRG2 WNT11 False False False user_curated False 1.0 1.0 1.0 1.0
2 CPI-SS0C08F5056 FZD9_WNT11 simple:O00144 simple:O96014 FZD9 WNT11 False False False user_curated False 1.0 1.0 1.0 1.0
3 CPI-SS029839DC3 MUSK_WNT11 simple:O15146 simple:O96014 MUSK WNT11 False False False user_curated False 1.0 1.0 1.0 1.0
4 CPI-SS0F653A282 FZD6_WNT11 simple:O60353 simple:O96014 FZD6 WNT11 False False False user_curated False 1.0 1.0 1.0 1.0
5 CPI-SS070E5DE9E FZD7_WNT11 simple:O75084 simple:O96014 FZD7 WNT11 False False False user_curated False 1.0 1.0 1.0 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4675 CPI-SS0AEC3600B NTNG2_LRRC4C simple:Q96CW9 simple:Q9HCJ2 NTNG2 LRRC4C False False False user_curated False 1.0 1.0 1.0 1.0
4677 CPI-SS0F2FBF1F6 ROBO3_NELL2 simple:Q96MS0 simple:Q99435 ROBO3 NELL2 False False False user_curated False 1.0 1.0 1.0 1.0
4678 CPI-SS0777989FE DCBLD2_SEMA4B simple:Q96PD2 simple:Q9NPR2 DCBLD2 SEMA4B False False False user_curated False 1.0 1.0 1.0 1.0
4681 CPI-SS0F30C777C LRRC4C_NTNG1 simple:Q9HCJ2 simple:Q9Y2I2 LRRC4C NTNG1 False False False user_curated False 1.0 1.0 1.0 1.0
4682 CPI-SS0F0C92F50 RTN4_TNFRSF19 simple:Q9NQC3 simple:Q9NS68 RTN4 TNFRSF19 False False False user_curated False 1.0 1.0 1.0 1.0

3225 rows × 15 columns

Set parameters, output, *_filename and output_format, to save results into files, more in API.

Visualization of communication#

We provide dot plot, heatmap, circos plot and sankey plot for visualizing the CCC analysis results.

Note

Currently, only statistical analysis type is supported to visualization.

Note

On this example, you need to replace data_niche to data when running the visualization functions if analysis is done on entire data.

Dotplot#

Here we recommend setting interacting_pairs and clusters1 /clusters2 before plotting, because the whole result might be too huge to be displayed.

[13]:

# a list of 'gene1_gene2'
interacting_pairs = [
                    'GPC3_CD81',
                    'COL1A1_CD44',
                    'FN1_CD44',
                    'DCN_ERBB4',
                    'VIM_CD44',
                    'ITGB1_VCAN'
                    ]

# interacting_pairs = None

data_niche.plt.ccc_dot_plot(
                    res_key='cell_cell_communication',
                    interacting_pairs=interacting_pairs,
                    clusters1='ventricular-specific CM'
                    )

[2023-11-15 12:15:18][Stereo][21850][MainThread][139852866279232][plot_collection][82][INFO]: register plot_func ccc_dot_plot to <stereo.plots.plot_collection.PlotCollection object at 0x7f2ff4f46340>
[2023-11-15 12:15:18][Stereo][21850][MainThread][139852866279232][plot_ccc][74][INFO]: Generating dot plot

[13]:
../_images/Tutorials_Cell_Cell_Communication_42_3.png

Heatmap#

The heatmap displays the number/log-number of significant L-R pairs for each pair of cell types.

[14]:

data_niche.plt.ccc_heatmap(res_key='cell_cell_communication')

[2023-11-15 12:15:18][Stereo][21850][MainThread][139852866279232][plot_collection][82][INFO]: register plot_func ccc_heatmap to <stereo.plots.plot_collection.PlotCollection object at 0x7f2ff4f46340>
[2023-11-15 12:15:18][Stereo][21850][MainThread][139852866279232][plot_ccc][160][INFO]: Generating heatmap plot

[14]:
../_images/Tutorials_Cell_Cell_Communication_44_3.png

Circos plot#

The circos plot shows the number of ligand-receptor pairs (with direction) between each cell cluster.

[15]:

data_niche.plt.ccc_circos_plot(res_key='cell_cell_communication')

[2023-11-15 12:15:19][Stereo][21850][MainThread][139852866279232][plot_collection][82][INFO]: register plot_func ccc_circos_plot to <stereo.plots.plot_collection.PlotCollection object at 0x7f2ff4f46340>
[2023-11-15 12:15:19][Stereo][21850][MainThread][139852866279232][plot_ccc][255][INFO]: Generating circos plot

[15]:
../_images/Tutorials_Cell_Cell_Communication_46_3.png

Gene Regulatory Network (GRN)#

In order to display the sankey plot, you need to run GRN beforehand.

Normally, running GRN with niche is more performance but may get fewer regulons, it may cause the sankey plot to fail to display, in this case, you can try to run with entire data to guarantee the output of sankey plot.

[17]:

# tfs_fn = '../test_data/grn/test_mm_mgi_tfs.txt'
# database_fn = '../test_data/grn/mm10_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather'
# motif_anno_fn = '../test_data/grn/motifs-v10nr_clust-nr.mgi-m0.001-o0.0.tbl'

# data_niche.tl.regulatory_network_inference(
#     database_fn,
#     motif_anno_fn,
#     tfs_fn,
#     save_regulons=True,
#     fn_prefix='2D_niche',
#     num_workers=20,
#     method='hotspot',
#     use_raw=True
# )

[16]:

tfs_fn = '../test_data/grn/test_mm_mgi_tfs.txt'
database_fn = '../test_data/grn/mm10_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather'
motif_anno_fn = '../test_data/grn/motifs-v10nr_clust-nr.mgi-m0.001-o0.0.tbl'


data.tl.regulatory_network_inference(
    database_fn,
    motif_anno_fn,
    tfs_fn,
    save_regulons=True,
    fn_prefix='2D',
    num_workers=20,
    method='hotspot',
    use_raw=True
)

[2023-11-15 12:15:19][Stereo][21850][MainThread][139852866279232][st_pipeline][77][INFO]: register algorithm regulatory_network_inference to <stereo.core.st_pipeline.AnnBasedStPipeline object at 0x7f31b4323340>
[2023-11-15 12:15:19][Stereo][21850][MainThread][139852866279232][main][94][INFO]: the raw expression matrix will be used.
[2023-11-15 12:16:10][Stereo][21850][MainThread][139852866279232][main][379][INFO]: Loading ranked database...
[2023-11-15 12:16:10][Stereo][21850][MainThread][139852866279232][main][222][INFO]: cached file not found, running hotspot now
[2023-11-15 12:17:38][Stereo][21850][MainThread][139852866279232][main][247][INFO]: compute_autocorrelations()
100%|██████████| 14405/14405 [01:08<00:00, 209.45it/s]
[2023-11-15 12:19:03][Stereo][21850][MainThread][139852866279232][main][249][INFO]: compute_autocorrelations() done
[2023-11-15 12:19:03][Stereo][21850][MainThread][139852866279232][main][252][INFO]: compute_local_correlations

Computing pair-wise local correlation on 558 features...

100%|██████████| 558/558 [00:10<00:00, 53.55it/s]
100%|██████████| 155403/155403 [02:34<00:00, 1007.14it/s]
[2023-11-15 12:22:22][Stereo][21850][MainThread][139852866279232][main][255][INFO]: Network Inference DONE
[2023-11-15 12:22:22][Stereo][21850][MainThread][139852866279232][main][256][INFO]: Hotspot: create 558 features
[2023-11-15 12:22:22][Stereo][21850][MainThread][139852866279232][main][257][INFO]: (558, 558)
[2023-11-15 12:22:22][Stereo][21850][MainThread][139852866279232][main][262][INFO]: detected 12 predefined TF in data

2023-11-15 12:22:48,449 - pyscenic.utils - INFO - Calculating Pearson correlations.
INFO:pyscenic.utils:Calculating Pearson correlations.

2023-11-15 12:22:48,510 - pyscenic.utils - WARNING - Note on correlation calculation: the default behaviour for calculating the correlations has changed after pySCENIC verion 0.9.16. Previously, the default was to calculate the correlation between a TF and target gene using only cells with non-zero expression values (mask_dropouts=True). The current default is now to use all cells to match the behavior of the R verision of SCENIC. The original settings can be retained by setting 'rho_mask_dropouts=True' in the modules_from_adjacencies function, or '--mask_dropouts' from the CLI.
        Dropout masking is currently set to [False].
WARNING:pyscenic.utils:Note on correlation calculation: the default behaviour for calculating the correlations has changed after pySCENIC verion 0.9.16. Previously, the default was to calculate the correlation between a TF and target gene using only cells with non-zero expression values (mask_dropouts=True). The current default is now to use all cells to match the behavior of the R verision of SCENIC. The original settings can be retained by setting 'rho_mask_dropouts=True' in the modules_from_adjacencies function, or '--mask_dropouts' from the CLI.
        Dropout masking is currently set to [False].

2023-11-15 12:22:51,697 - pyscenic.utils - INFO - Creating modules.
INFO:pyscenic.utils:Creating modules.
[2023-11-15 12:22:57][Stereo][21850][MainThread][139852866279232][main][432][INFO]: cached file not found, running prune modules now

[########################################] | 100% Completed | 359.10 s

[2023-11-15 12:28:59][Stereo][21850][MainThread][139852866279232][main][476][INFO]: cached file not found, calculating auc_activity_level now

Create regulons from a dataframe of enriched features.
Additional columns saved: []

Sankey plot#

The sankey plot shows the ligand-receptor communications between a pair of cell clusters and the latent regulatory relationship between receptors and downstream TFs in the receiver cells.

The parameter regulons specifies the path of file saving regulons which is output of function of Gene Regulatory Network.

You need to specify the path of file which contains the weighted network infomation by parameter weighted_network_path . By default, you can download the NicheNet-V2 weighted network files from here.

There are tow files about weighted network infomations:

  1. Use weighted_network_lr_sig_human.txt when the parameter species on data.tl.cell_cell_communication is set to 'HUMAN'.

  2. Use weighted_network_lr_sig_mouse.txt when the parameter species is set to 'MOUSE'.

[ ]:

data_niche.plt.ccc_sankey_plot(
    sender_cluster='ventricular-specific CM',
    receiver_cluster='epicardial (EP)',
    homo_transfer=True,
    weighted_network_path='../test_data/weighted_network_lr_sig_mouse.txt',
    regulons='./2D_regulon_list.csv',
    pct_expressed=0.01
)

ccc_sankey_2D.png