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HoloNet documentation

  • Installation
  • Tutorials
    • Analyzing and visualizing cell-cell communication events
    • Decoding the holograph of functional cell-cell communication events
  • API
    • HoloNet.preprocessing.load_brca_visium_10x
    • HoloNet.preprocessing.load_lr_df
    • HoloNet.preprocessing.get_expressed_lr_df
    • HoloNet.tools.elements_expr_df_calculate
    • HoloNet.tools.compute_ce_tensor
    • HoloNet.tools.filter_ce_tensor
    • HoloNet.tools.compute_ce_network_eigenvector_centrality
    • HoloNet.tools.compute_ce_network_degree_centrality
    • HoloNet.tools.cluster_lr_based_on_ce
    • HoloNet.tools.default_w_visium
    • HoloNet.predicting.get_continuous_cell_type_tensor
    • HoloNet.predicting.get_one_hot_cell_type_tensor
    • HoloNet.predicting.get_gene_expr
    • HoloNet.predicting.get_one_case_expr
    • HoloNet.predicting.adj_normalize
    • HoloNet.predicting.mgc_repeat_training
    • HoloNet.predicting.get_mgc_result
    • HoloNet.predicting.mgc_training_for_multiple_targets
    • HoloNet.predicting.get_mgc_result_for_multiple_targets
    • HoloNet.predicting.save_model_list
    • HoloNet.predicting.load_model_list
    • HoloNet.plotting.feature_plot
    • HoloNet.plotting.cell_type_level_network
    • HoloNet.plotting.plot_cell_type_proportion
    • HoloNet.plotting.ce_hotspot_plot
    • HoloNet.plotting.ce_cell_type_network_plot
    • HoloNet.plotting.lr_umap
    • HoloNet.plotting.lr_clustering_dendrogram
    • HoloNet.plotting.lr_cluster_ce_hotspot_plot
    • HoloNet.plotting.lr_rank_in_mgc
    • HoloNet.plotting.fce_cell_type_network_plot
    • HoloNet.plotting.delta_e_proportion
    • HoloNet.plotting.plot_mgc_result
    • HoloNet.plotting.find_genes_linked_to_ce
    • HoloNet.plotting.detect_pathway_related_genes
    • HoloNet.plotting.select_w
  • References
  • .rst

References

References#

Paszke19

Paszke et al. (2019), PyTorch: An imperative style, high-performance deep learning library, Advances in Neural Information Processing Systems.

Pedregosa11

Pedregosa (2011), Scikit-learn: Machine learning in Python, J. Mach. Learn. Res..

Wolf18

Wolf et al. (2018), SCANPY: Large-scale single-cell gene expression data analysis., Genome Biol.

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By Li Haochen

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