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Novel features in v5

  1. New python package that can be easily executed in Jupyter Notebook and Collabs.
  2. A scoring methodology to rank interaction based on the expression specificity of the interacting partners.
  3. A CellSign module to leverage interactions based on the activity of the transcription factor downstream the receptor. This module is accompanied by a collection of 211 well described receptor-transcription factor direct relationships.
  4. A new method of querying of CellphoneDB results search_utils.search_analysis_results.
  5. Tutorials to run CellphoneDB (available here)
  6. Improved computational efficiency of method 2 cpdb_statistical_analysis_method.
  7. A new database (cellphonedb-data v5.0) with more manually curated interactions, making up to a total of ~3,000 interactions. This release of CellphoneDB database has three main changes:
    • Integrates new manually reviewed interactions with evidenced roles in cell-cell communication.
    • Includes non-protein molecules acting as ligands.
    • For interactions with a demonstrated signalling directionality, partners have been ordered according (ligand is partner A, receptor partner B).
    • Interactions have been classified within signaling pathways.
    • CellphoneDB no longer imports interactions from external resources. This is to avoid the inclusion of low-confidence interactions.

See updates from previous releases here.

New in CellPhoneDB-data v4

This release involves a major CellphoneDB database update. We have invested quite some time curating more cell-cell communication interactions validated experimentally. Specifically, we have:

  1. Manually curated more protein-protein interactions involved in cell-cell communication, with special focus on protein acting as heteromeric complexes. The new database includes almost 2,000 high-confidence interactions, including heteromeric complexes! We believe modelling complexes is key to minimise false positives in the predictions.
  2. Annotated non-peptidic molecules (i.e., not encoded by a gene) acting as ligands. Examples of these include steroid hormones (e.g., estrogen). To do so, we have reconstructed the biosynthetic pathways and used the last representative enzyme as a proxy of ligand abundance. We retrieve this information by manually reviewing and curating relevant literature and peer-reviewed pathway resources such as REACTOME. We include more than 200 interactions involving non-peptidic ligands!

Check Garcia-Alonso & Lorenzi et al for an example applying CellphoneDB v4.

New in CellPhoneDB v3

  1. Incorporate spatial information CellPhoneDB now allows the incorporation of spatial information of the cells via the microenvironments file. This is a two columns file indicating which cell type is in which spatial microenvironment (see example ). CellphoneDB will use this information to define possible pairs of interacting cells (i.e. pairs of clusters sharing/coexisting in a microenvironment). You can define microenvironments with prior knowledge, imaging or Visium analysis with cell2location.
  2. New analysis method added, using Differentially Expressed Genes (cellphonedb method degs_analysis) as an alternative to the permutation-based approach (cellphonedb method statistical_analysis). This degs_analysis approach will select interactions where all the genes are expressed by a fraction of cells above a --threshold and at least one gene is a DEG. The user identifies the DEGs using their preferred tool and provides the information to CellphoneDB via text file. The first column should be the cell type/cluster and the second column the associated gene id. The remaining columns are ignored (see example ). We provide notebooks for both Seurat and Scanpy users.
  3. Database update WNT pathway has been further curated.

Check Garcia-Alonso et al for an example applying CellphoneDB v3.