CrossTalkeR: analysis and visualization of ligand–receptorne tworks

Abstract

Motivation Ligand-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. Results CrossTalkeR is a framework for network analysis and visualisation 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 vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterisation of changes in cellular crosstalk in disease. Availability CrosstalkeR is an R package available at: Github: https://github.com/CostaLab/CrossTalkeR. Zenodo: https://zenodo.org/record/4740646 Supplementary information Supplementary data are available at Bioinformatics online.

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

“…To increase the reliability of LR inference CPDB input contained a database elaborated by the combination of five LR data sources (CPDB (Efremova et al, 2020), TalkLR (Wang et al, 2020), scTensor (Tsuyuzaki et al, 2019) were kept in the final LR database. Using the statistically significant interactions (p-value<0.05) from CPDB output, ranking and the visualization were generated by CrossTalkeR (v 1.0.0; https://github.com/CostaLab/CrossTalkeR, (Nagai et al, 2021)).…”

Mapping the cardiac vascular niche in heart failure

“…To understand the cellular receptor-ligand interactions within the vascular and perivascular niche we utilized CrossTalkeR 49 . Here, we focused our analysis on selected subtypes of the niche and excluded clusters containing low cell numbers and clusters with minor transcriptional changes after TAC.…”

“…Aiming to increase the reliability of LR inference CPDB was fed with a database enhanced by the combination of five different LR data sources (CPDB 117 ; TalkLR 121 ; scTensor 122 ; SCA 123 ; iTALK 124 ), interactions that presented at least two consensus data sources were kept in the final LR database. Using the statistically significant interactions ( p value < 0.05) from CPDB output, ranking and the visualization were generated by CrossTalkeR 49 To perform the annotation of the LR interactions, the ligands/receptors set was splitted in the following two subsets (1) upregulated (i.e., MeanLR > 0) and (2) downregulated (i.e., MeanLR < 0). These subsets contain exclusive ligands and receptors; the genes which were at the intersection of this were disregarded.…”

Human pluripotent stem cell-derived kidney organoids for personalized congenital and idiopathic nephrotic syndrome modeling

“…To understand the observed maturation differences within the three clusters of podocytes, we further investigated the cell-cell communication between podocytes and endothelial cells by performing ligand-receptor analysis using CellPhoneDB and CrossTalkeR packages (Fig. 2H) (Efremova et al, 2020;Nagai et al, 2021). Podocyte cluster 3 showed enriched VEGFA interactions with the endothelial cell precursor receptors ephrin type-B receptor 2 (EPHB2), neuropilin 1 and neuropilin 2 (NRP1 and -2), which could not be identified in the other podocyte clusters (Fig.…”