Documentation for cell2cell

This documentation is for our cell2cell suite, which includes the regular cell2cell and Tensor-cell2cell tools. The former is for inferring cell-cell interactions and communication in one sample or context, while the latter is for deconvolving complex patterns of cell-cell communication across multiple samples or contexts simultaneously into interpretable factors representing patterns of communication.

Here, multiple classes and functions are implemented to facilitate the analyses, including a variety of visualizations to simplify the interpretation of results:

  • cell2cell.analysis : Includes simplified pipelines for running the analyses, and functions for downstream analyses of Tensor-cell2cell
  • cell2cell.clustering : Includes multiple scipy-based functions for performing clustering methods.
  • cell2cell.core : Includes the core functions for inferring cell-cell interactions and communication. It includes scoring methods, cell classes, and interaction spaces.
  • cell2cell.datasets : Includes toy datasets and annotations for testing functions in basic scenarios.
  • cell2cell.external : Includes built-in approaches borrowed from other tools to avoid incompatibilities (e.g. UMAP, tensorly, and PCoA).
  • cell2cell.io : Includes functions for opening and saving diverse types of files.
  • cell2cell.plotting : Includes all the visualization options that cell2cell offers.
  • cell2cell.preprocessing : Includes functions for manipulating data and variables (e.g. data preprocessing, integration, permutation, among others).
  • cell2cell.spatial : Includes filtering of cell-cell interactions results given intercellular distance, as well as defining neighborhoods by grids or moving windows.
  • cell2cell.stats : Includes statistical analyses such as enrichment analysis, multiple test correction methods, permutation approaches, and Gini coefficient.
  • cell2cell.tensor : Includes all functions pertinent to the analysis of Tensor-cell2cell
  • cell2cell.utils : Includes general utilities for analyzing networks and performing parallel computing.

Below, all the inputs, parameters (including their different options), and outputs are detailed. Source code of the functions is also included.