【视频教程】Cellphonedb v5更新：python版Scanpy单细胞分析后续之cpdb细胞通讯

import pandas as pdimport scanpy as scimport numpy as np

#load dataadata = sc.read_h5ad('./pbmc3k.h5ad')adata = adata.raw.to_adata()adata

adata_cpdb = adata[~adata.obs['leiden'].isin(['Megakaryocytes'])]

#normalizecounts = adata_cpdb.X.toarray()adata_cpdb.X = countssc.pp.normalize_per_cell(adata_cpdb, counts_per_cell_after=1e4)adata_cpdb.layers["norm"] = adata_cpdb.X

counts = pd.DataFrame(data=adata_cpdb.layers["norm"], index=adata_cpdb.obs.index.tolist(), columns=adata_cpdb.var.index.tolist()).Tcounts.index.name = "Gene"

#save metameta = pd.DataFrame(data=adata_cpdb.obs['leiden'].tolist(), index=adata_cpdb.obs.index.tolist(), columns=["Cell type"])meta.index.name = "Cell"meta

import numpy as npimport pandas as pdimport scanpy as scimport anndataimport osimport sysfrom scipy import sparse

#Download database from sourcefrom IPython.display import HTML, displayfrom cellphonedb.utils import db_releases_utilsdisplay(HTML(db_releases_utils.get_remote_database_versions_html()['db_releases_html_table']))

cpdb_version = 'v5.0.0'cpdb_target_dir = os.path.join('./', cpdb_version)
from cellphonedb.utils import db_utilsdb_utils.download_database(cpdb_target_dir, cpdb_version)

sc.settings.verbosity = 3  # verbosity: errors (0), warnings (1), info (2), hints (3)sc.logging.print_versions()sc.settings.set_figure_params(dpi=100)  # low dpi (dots per inch) yields small inline figures

cpdb_file_path = './v5.0.0/cellphonedb.zip'meta_file_path = './meta_cpdb.txt'counts_file_path = './count_cpdb.txt'out_path = './test1'

from cellphonedb.src.core.methods import cpdb_statistical_analysis_method
cpdb_results1 = cpdb_statistical_analysis_method.call(    cpdb_file_path = cpdb_file_path,                   meta_file_path = meta_file_path,                    counts_file_path = counts_file_path,               counts_data = 'hgnc_symbol',                        #active_tfs_file_path = active_tf_path,              #microenvs_file_path = microenvs_file_path,          score_interactions = True,                          iterations = 1000,                                  threshold = 0.1,                                     threads = 5,                                        debug_seed = 42,                                    result_precision = 3,                               pvalue = 0.05,                                       separator = '|',                                     debug = False,                                      output_path = out_path                            )

cpdb_file_path = './v5.0.0/cellphonedb.zip'meta_file_path = './meta_cpdb.txt'counts_file_path = './adata_cpdb.h5ad'out_path = './test2'

from cellphonedb.src.core.methods import cpdb_statistical_analysis_method
cpdb_results2 = cpdb_statistical_analysis_method.call(    cpdb_file_path = cpdb_file_path,                   meta_file_path = meta_file_path,                    counts_file_path = counts_file_path,               counts_data = 'hgnc_symbol',                        #active_tfs_file_path = active_tf_path,              #microenvs_file_path = microenvs_file_path,          score_interactions = True,                          iterations = 1000,                                  threshold = 0.1,                                     threads = 5,                                        debug_seed = 42,                                    result_precision = 3,                               pvalue = 0.05,                                       separator = '|',                                     debug = False,                                      output_path = out_path                            )

cpdb_pbmc = summary_cpdb_results(cpdb_results1)cpdb_pbmc.head()

from cellphonedb.utils import search_utils
search_results = search_utils.search_analysis_results(    query_cell_types_1 = ['CD4 T'],  # List of cells 1, will be paired to cells 2 (list or 'All').    query_cell_types_2 = ['CD14 Monocytes', 'FCGR3A Monocytes'],     # List of cells 2, will be paired to cells 1 (list or 'All').    # query_genes = ['TGFBR1'],                                       # filter interactions based on the genes participating (list).    #query_interactions = ['CSF1_CSF1R'],                            # filter intereactions based on their name (list).    significant_means = cpdb_results1['significant_means'],          # significant_means file generated by CellphoneDB.    deconvoluted = cpdb_results1['deconvoluted'],                    # devonvoluted file generated by CellphoneDB.    interaction_scores = cpdb_results1['interaction_scores'],        # interaction score generated by CellphoneDB.    query_minimum_score = 50,                                       # minimum score that an interaction must have to be filtered.    separator = '|',                                                # separator (default: |) employed to split cells (cellA|cellB).    long_format = True                                            # converts the output into a wide table, removing non-significant interactions)
search_results.head()