ArticleBioinformatics (Oxford, England)2021
CrossTalkeR: analysis and visualization of ligand-receptorne tworks.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 61 citations in OpenAlex.
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- Cancer-associated fibroblasts are associated with neo-adjuvant treatment response in oesophageal adenocarcinoma.British journal of cancer · 2025Article
- Inhibiting the alarmin-driven hematopoiesis-stromal cell crosstalk in primary myelofibrosis ameliorates bone marrow fibrosis.HemaSphere · 2025Article
- Exploring cellular changes in ruptured human quadriceps tendons at single-cell resolution.The Journal of physiology · 2025Article
- Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data.NAR genomics and bioinformatics · 2025Article
- scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.Bioinformatics (Oxford, England) · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.Briefings in bioinformatics · 2025Article
- Mathematical Modeling Predicts Optimal Immune Checkpoint Inhibitor and Radiotherapy Combinations and Timing of Administration.Cancer immunology research · 2025Article
- ABTrans: A Transformer-based Model for Predicting Interaction between Anti-Aβ Antibodies and Peptides.Interdisciplinary sciences, computational life sciences · 2025Article
- SpatialKNifeY (SKNY): Extending from spatial domain to surrounding area to identify microenvironment features with single-cell spatial omics data.PLoS computational biology · 2025Article
- Mapping the spatial atlas of the human bone tissue integrating spatial and single-cell transcriptomics.Nucleic acids research · 2025Article
- Encompassing view of spatial and single-cell RNA sequencing renews the role of the microvasculature in human atherosclerosis.Nature cardiovascular research · 2025Article
- Instructive interaction between myelodysplastic hematopoiesis and the bone marrow microenvironment at the single-cell level.Blood neoplasia · 2024Article
- LIANA+ provides an all-in-one framework for cell-cell communication inference.Nature cell biology · 2024Article
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationLigand-receptor (LR) network analysis allows the characterization of cellular crosstalk based on single cell RNA-seq data. However, current methods typically provide a list of inferred LR interactions and do not allow the researcher to focus on specific cell types, ligands or receptors. In addition, most of these methods cannot quantify changes in crosstalk between two biological phenotypes.
resultsCrossTalkeR is a framework for network analysis and visualization of LR interactions. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological phenotypes, i.e. disease versus homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterization of changes in cellular crosstalk in disease. AVAILABILITY AND IMPLEMENTATION: CrosstalkeR is an R package available at: Github: https://github.com/CostaLab/CrossTalkeR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.