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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # # Cell interaction with CellPhoneDB | |
| # | |
| # CellPhoneDB is a publicly available repository of curated receptors, ligands and their interactions in HUMAN. CellPhoneDB can be used to search for a particular ligand/receptor, or interrogate your own single-cell transcriptomics data. | |
| # | |
| # We made three improvements in integrating the CellPhoneDB algorithm in OmicVerse: | |
| # | |
| # - We have added a tutorial on analysing `anndata` based on any `anndata`. | |
| # - We added prettier heatmaps, chord diagrams and network diagrams for visualising relationships between cells. | |
| # - We added visualisation of ligand receptor proteins in different groups | |
| # | |
| # Paper: [Single-cell reconstruction of the early maternal–fetal interface in humans](https://www.nature.com/articles/s41586-018-0698-6) | |
| # | |
| # Code: https://github.com/ventolab/CellphoneDB | |
| # | |
| # This notebook will demonstrate how to use CellPhoneDB on scRNA data and visualize it. | |
| # In[1]: | |
| import scanpy as sc | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import numpy as np | |
| import omicverse as ov | |
| import os | |
| ov.plot_set() | |
| #print(f'cellphonedb version{cellphonedb.__version__}') | |
| # ## The EVT Data | |
| # | |
| # Th EVT data have finished the celltype annotation, it can be download from the tutorial of CellPhoneDB. | |
| # | |
| # Download: https://github.com/ventolab/CellphoneDB/blob/master/notebooks/data_tutorial.zip | |
| # | |
| # In[2]: | |
| adata=sc.read('data/cpdb/normalised_log_counts.h5ad') | |
| adata=adata[adata.obs['cell_labels'].isin(['eEVT','iEVT','EVT_1','EVT_2','DC','dNK1','dNK2','dNK3', | |
| 'VCT','VCT_CCC','VCT_fusing','VCT_p','GC','SCT'])] | |
| adata | |
| # In[3]: | |
| ov.pl.embedding(adata, | |
| basis='X_umap', | |
| color='cell_labels', | |
| frameon='small', | |
| palette=ov.pl.red_color+ov.pl.blue_color+ov.pl.green_color+ov.pl.orange_color+ov.pl.purple_color) | |
| # In[4]: | |
| adata.X.max() | |
| # We can clearly see that the maximum value of the data is a floating point number less than 10. The fact that the maximum value is not an integer means that it has been normalised, and the fact that it is less than 10 means that it has been logarithmised. Note that our data cannot be `scaled`. | |
| # ## Export the anndata object | |
| # | |
| # As the input to CellPhoneDB only requires the expression matrix and cell type, we extracted only the expression matrix and cell type from adata for the next step of analysis | |
| # In[5]: | |
| sc.pp.filter_cells(adata, min_genes=200) | |
| sc.pp.filter_genes(adata, min_cells=3) | |
| adata1=sc.AnnData(adata.X,obs=pd.DataFrame(index=adata.obs.index), | |
| var=pd.DataFrame(index=adata.var.index)) | |
| adata1.write_h5ad('data/cpdb/norm_log.h5ad',compression='gzip') | |
| adata1 | |
| # ## Export the meta info of cells | |
| # | |
| # we construct a `DataFrame` object to export the meta info of cells. In EVT adata object, the celltypes were stored in the `obs['cell_labels']` | |
| # In[6]: | |
| #meta导出 | |
| df_meta = pd.DataFrame(data={'Cell':list(adata[adata1.obs.index].obs.index), | |
| 'cell_type':[ i for i in adata[adata1.obs.index].obs['cell_labels']] | |
| }) | |
| df_meta.set_index('Cell', inplace=True) | |
| df_meta.to_csv('data/cpdb/meta.tsv', sep = '\t') | |
| # ## Cell interaction analysis | |
| # | |
| # Now, we prepare the meta info of cells `meta.tsv` and matrix of scRNA-eq `norm_log.h5ad`, we can use the method of CellPhoneDB to calculate the interaction of each celltype in scRNA-seq data. | |
| # | |
| # Importantly, to avoid a series of bugs, we set the absolute path for CellPhoneDB analysis. we use `os.getcwd() ` to get the path now analysis. | |
| # In[7]: | |
| import os | |
| os.getcwd() | |
| # Another thing to note is that we need to download the `cellphonedb.zip` file from `cellphonedb-data` for further analysis. I have placed it in the `data/CPDB` directory, but you can place it in any path you are interested in | |
| # | |
| # Downloads: https://github.com/ventolab/cellphonedb-data | |
| # In[8]: | |
| cpdb_file_path = '/Users/fernandozeng/Desktop/analysis/cellphonedb-data/cellphonedb.zip' | |
| meta_file_path = os.getcwd()+'/data/cpdb/meta.tsv' | |
| counts_file_path = os.getcwd()+'/data/cpdb/norm_log.h5ad' | |
| microenvs_file_path = None | |
| active_tf_path = None | |
| out_path =os.getcwd()+'/data/cpdb/test_cellphone' | |
| # Now we run `cpdb_statistical_analysis_method` to predicted the cell interaction in scRNA-seq | |
| # In[9]: | |
| from cellphonedb.src.core.methods import cpdb_statistical_analysis_method | |
| cpdb_results = cpdb_statistical_analysis_method.call( | |
| cpdb_file_path = cpdb_file_path, # mandatory: CellphoneDB database zip file. | |
| meta_file_path = meta_file_path, # mandatory: tsv file defining barcodes to cell label. | |
| counts_file_path = counts_file_path, # mandatory: normalized count matrix - a path to the counts file, or an in-memory AnnData object | |
| counts_data = 'hgnc_symbol', # defines the gene annotation in counts matrix. | |
| active_tfs_file_path = active_tf_path, # optional: defines cell types and their active TFs. | |
| microenvs_file_path = microenvs_file_path, # optional (default: None): defines cells per microenvironment. | |
| score_interactions = True, # optional: whether to score interactions or not. | |
| iterations = 1000, # denotes the number of shufflings performed in the analysis. | |
| threshold = 0.1, # defines the min % of cells expressing a gene for this to be employed in the analysis. | |
| threads = 10, # number of threads to use in the analysis. | |
| debug_seed = 42, # debug randome seed. To disable >=0. | |
| result_precision = 3, # Sets the rounding for the mean values in significan_means. | |
| pvalue = 0.05, # P-value threshold to employ for significance. | |
| subsampling = False, # To enable subsampling the data (geometri sketching). | |
| subsampling_log = False, # (mandatory) enable subsampling log1p for non log-transformed data inputs. | |
| subsampling_num_pc = 100, # Number of componets to subsample via geometric skectching (dafault: 100). | |
| subsampling_num_cells = 1000, # Number of cells to subsample (integer) (default: 1/3 of the dataset). | |
| separator = '|', # Sets the string to employ to separate cells in the results dataframes "cellA|CellB". | |
| debug = False, # Saves all intermediate tables employed during the analysis in pkl format. | |
| output_path = out_path, # Path to save results. | |
| output_suffix = None # Replaces the timestamp in the output files by a user defined string in the (default: None). | |
| ) | |
| # In[10]: | |
| ov.utils.save(cpdb_results,'data/cpdb/gex_cpdb_test.pkl') | |
| # In[5]: | |
| cpdb_results=ov.utils.load('data/cpdb/gex_cpdb_test.pkl') | |
| # ## Network of celltype analysis | |
| # | |
| # It is worth noting that we will be using ov for all downstream analysis, starting with cell network analysis, where we provide the `ov.single.cpdb_network_cal` function to extract interactions, and the `ov.single.cpdb_plot_network` function for very elegant visualization | |
| # In[6]: | |
| interaction=ov.single.cpdb_network_cal(adata = adata, | |
| pvals = cpdb_results['pvalues'], | |
| celltype_key = "cell_labels",) | |
| # In[7]: | |
| interaction['interaction_edges'].head() | |
| # In[8]: | |
| ov.plot_set() | |
| # In[9]: | |
| fig, ax = plt.subplots(figsize=(4,4)) | |
| ov.pl.cpdb_heatmap(adata,interaction['interaction_edges'],celltype_key='cell_labels', | |
| fontsize=11, | |
| ax=ax,legend_kws={'fontsize':12,'bbox_to_anchor':(5, -0.9),'loc':'center left',}) | |
| # In[10]: | |
| fig, ax = plt.subplots(figsize=(2,4)) | |
| ov.pl.cpdb_heatmap(adata,interaction['interaction_edges'],celltype_key='cell_labels', | |
| source_cells=['EVT_1','EVT_2','dNK1','dNK2','dNK3'], | |
| ax=ax,legend_kws={'fontsize':12,'bbox_to_anchor':(5, -0.9),'loc':'center left',}) | |
| # In[11]: | |
| fig=ov.pl.cpdb_chord(adata,interaction['interaction_edges'],celltype_key='cell_labels', | |
| count_min=60,fontsize=12,padding=50,radius=100,save=None,) | |
| fig.show() | |
| # In[12]: | |
| fig, ax = plt.subplots(figsize=(4,4)) | |
| ov.pl.cpdb_network(adata,interaction['interaction_edges'],celltype_key='cell_labels', | |
| counts_min=60, | |
| nodesize_scale=5, | |
| ax=ax) | |
| # In[13]: | |
| fig, ax = plt.subplots(figsize=(4,4)) | |
| ov.pl.cpdb_network(adata,interaction['interaction_edges'],celltype_key='cell_labels', | |
| counts_min=60, | |
| nodesize_scale=5, | |
| source_cells=['EVT_1','EVT_2','dNK1','dNK2','dNK3'], | |
| ax=ax) | |
| # In[14]: | |
| fig, ax = plt.subplots(figsize=(4,4)) | |
| ov.pl.cpdb_network(adata,interaction['interaction_edges'],celltype_key='cell_labels', | |
| counts_min=60, | |
| nodesize_scale=5, | |
| target_cells=['EVT_1','EVT_2','dNK1','dNK2','dNK3'], | |
| ax=ax) | |
| # In[15]: | |
| ov.single.cpdb_plot_network(adata=adata, | |
| interaction_edges=interaction['interaction_edges'], | |
| celltype_key='cell_labels', | |
| nodecolor_dict=None,title='EVT Network', | |
| edgeswidth_scale=25,nodesize_scale=10, | |
| pos_scale=1,pos_size=10,figsize=(6,6), | |
| legend_ncol=3,legend_bbox=(0.8,0.2),legend_fontsize=10) | |
| # Sometimes, the whole network you don't want to use for analysis, the sub-network is useful for analysis. we can exacted the sub-network from it. | |
| # | |
| # We need to exacted the sub-interaction first, we assumed that the five celltypes `['EVT_1','EVT_2','dNK1','dNK2','dNK3']` which is interested | |
| # In[16]: | |
| sub_i=interaction['interaction_edges'] | |
| sub_i=sub_i.loc[sub_i['SOURCE'].isin(['EVT_1','EVT_2','dNK1','dNK2','dNK3'])] | |
| sub_i=sub_i.loc[sub_i['TARGET'].isin(['EVT_1','EVT_2','dNK1','dNK2','dNK3'])] | |
| # Then, we exacted the sub-anndata object | |
| # In[17]: | |
| sub_adata=adata[adata.obs['cell_labels'].isin(['EVT_1','EVT_2','dNK1','dNK2','dNK3'])] | |
| sub_adata | |
| # Now we plot the sub-interaction network between the cells in scRNA-seq | |
| # In[18]: | |
| ov.single.cpdb_plot_network(adata=sub_adata, | |
| interaction_edges=sub_i, | |
| celltype_key='cell_labels', | |
| nodecolor_dict=None,title='Sub-EVT Network', | |
| edgeswidth_scale=25,nodesize_scale=1, | |
| pos_scale=1,pos_size=10,figsize=(5,5), | |
| legend_ncol=3,legend_bbox=(0.8,0.2),legend_fontsize=10) | |
| # In[19]: | |
| fig=ov.pl.cpdb_chord(sub_adata,sub_i,celltype_key='cell_labels', | |
| count_min=10,fontsize=12,padding=60,radius=100,save=None,) | |
| fig.show() | |
| # In[20]: | |
| fig, ax = plt.subplots(figsize=(4,4)) | |
| ov.pl.cpdb_network(sub_adata,sub_i,celltype_key='cell_labels', | |
| counts_min=10, | |
| nodesize_scale=5, | |
| ax=ax) | |
| # In[21]: | |
| fig, ax = plt.subplots(figsize=(3,3)) | |
| ov.pl.cpdb_heatmap(sub_adata,sub_i,celltype_key='cell_labels', | |
| ax=ax,legend_kws={'fontsize':12,'bbox_to_anchor':(5, -0.9),'loc':'center left',}) | |
| # ## The ligand-receptor exacted | |
| # | |
| # We can set EVT as ligand or receptor to exacted the ligand-receptor proteins from the result of CellPhoneDB. | |
| # | |
| # | |
| # The most important step is that we need to extract the results of the analysis with eEVT as the ligand, and here we use ov's function `ov.single.cpdb_exact_target`,`ov.single.cpdb_exact_source` to do this | |
| # In[22]: | |
| sub_means=ov.single.cpdb_exact_target(cpdb_results['means'],['eEVT','iEVT']) | |
| sub_means=ov.single.cpdb_exact_source(sub_means,['dNK1','dNK2','dNK3']) | |
| sub_means.head() | |
| # In[23]: | |
| ov.pl.cpdb_interacting_heatmap(adata=adata, | |
| celltype_key='cell_labels', | |
| means=cpdb_results['means'], | |
| pvalues=cpdb_results['pvalues'], | |
| source_cells=['dNK1','dNK2','dNK3'], | |
| target_cells=['eEVT','iEVT'], | |
| plot_secret=True, | |
| min_means=3, | |
| nodecolor_dict=None, | |
| ax=None, | |
| figsize=(2,6), | |
| fontsize=10,) | |
| # Sometimes we care about the expression of ligand in SOURCE and receptor in TARGET, we provide another function for getting the expression situation | |
| # In[24]: | |
| ov.pl.cpdb_group_heatmap(adata=adata, | |
| celltype_key='cell_labels', | |
| means=cpdb_results['means'], | |
| cmap={'Target':'Blues','Source':'Reds'}, | |
| source_cells=['dNK1','dNK2','dNK3'], | |
| target_cells=['eEVT','iEVT'], | |
| plot_secret=True, | |
| min_means=3, | |
| nodecolor_dict=None, | |
| ax=None, | |
| figsize=(2,6), | |
| fontsize=10,) | |
| # We can also build Ligand, Receptor, SOURCE, and TARGET into a regulatory network, which is interesting. | |
| # In[25]: | |
| ov.pl.cpdb_interacting_network(adata=adata, | |
| celltype_key='cell_labels', | |
| means=cpdb_results['means'], | |
| source_cells=['dNK1','dNK2','dNK3'], | |
| target_cells=['eEVT','iEVT'], | |
| means_min=1, | |
| means_sum_min=1, | |
| nodecolor_dict=None, | |
| ax=None, | |
| figsize=(6,6), | |
| fontsize=10) | |
| # Sometimes we want to analyse ligand-receptor pathway enrichment or function, so we need to extract ligand-receptor pairs from the significant ligand-receptors filtered out above, and omicverse provides an easy function `ov.single.cpdb_interaction_filtered` to do this here | |
| # In[40]: | |
| sub_means=sub_means.loc[~sub_means['gene_a'].isnull()] | |
| sub_means=sub_means.loc[~sub_means['gene_b'].isnull()] | |
| enrichr_genes=sub_means['gene_a'].tolist()+sub_means['gene_b'].tolist() | |
| # A tutorial on enrichment you can find in the Bulk chapter of tutorials: | |
| # | |
| # https://omicverse.readthedocs.io/en/latest/Tutorials-bulk/t_deg/ or https://starlitnightly.github.io/omicverse/Tutorials-bulk/t_deg/ | |
| # In[ ]: | |
| pathway_dict=ov.utils.geneset_prepare('genesets/GO_Biological_Process_2023.txt',organism='Human') | |
| # In[14]: | |
| #deg_genes=dds.result.loc[dds.result['sig']!='normal'].index.tolist() | |
| enr=ov.bulk.geneset_enrichment(gene_list=enrichr_genes, | |
| pathways_dict=pathway_dict, | |
| pvalue_type='auto', | |
| organism='human') | |
| # In[20]: | |
| ov.plot_set() | |
| ov.bulk.geneset_plot(enr,figsize=(2,4),fig_title='GO-Bio(EVT)', | |
| cax_loc=[2, 0.45, 0.5, 0.02],num=8, | |
| bbox_to_anchor_used=(-0.25, -13),custom_ticks=[10,100], | |
| cmap='Greens') | |
| # In[ ]: | |