pycrosstalker 2.1.5rc0

No project description provided

pyCrossTalkeR

James S. Nagai¹, Vanessa Kloeker¹, Ruthvik Koppala¹, Nils B. Leimkühler², Michael T. Schaub ³, Rebekka K. Schneider^4,5,6, Ivan G. Costa^1*

¹Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen, 52074 Germany

²Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Germany

³Department of Computer Science, RWTH Aachen University, Germany

⁴Department of Cell Biology, Institute for Biomedical Engineering, Faculty of Medicine,RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, NRW, Germany

⁵Oncode Institute, Erasmus Medical Center, Rotterdam, 3015GD, the Netherlands

⁶Department of Hematology, Erasmus Medical Center, Rotterdam, 3015GD, the Netherlands

Motivation: Ligand-receptor (LR) analysis allows the characterization of cellular crosstalk from single cell RNA-seq data. However, current LR methods provide limited approaches for prioritization of cell types, ligands or receptors or characterizing changes in crosstalk between two biological conditions.

Results: pyCrossTalkeR is a framework for network analysis and visualisation of LR networks. pyCrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological states: disease vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of pyCrossTalkeR for characterisation of changes in cellular crosstalk in disease state.

Install

You can install pyCrossTalkeR with the simple commands below:

pip install pycrosstalker

Note: Please avoid to use the following characters in celltype name: '$'

Possible system dependencies

libudunits2-dev
libgdal-dev
gdal-bin
libproj-dev
proj-data
proj-bin
libgeos-dev

Features v2.1.3

• Single and Comparative Reports
  • Cell Cell Interaction visualization
  • Sending and Receiving Cells Ranking
  • CCI and GCI PCA ranking
    • All measures and PC table
    • PC1 and PC2 based barplot
  • LR pair visualization plot can be done
  • Statistical tests for both interaction amount and also interaction strenght

References

[1] CrossTalkeR: Analysis and Visualisation of Ligand Receptor Networks link

[2] Heterogeneous bone-marrow stromal progenitors drive myelofibrosis via a druggable alarmin axis. link

[3] Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data link