Abstract
MotivationIntercellular communication plays an essential role in multicellular organisms and several algorithms to analyze it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret.ResultsWe developed Cell cOmmunication exploration with MUltiplex NETworks (COMUNET), a tool that streamlines the interpretation of the results from cell–cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication patterns between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment.Availability and implementationOur algorithm is implemented in an R package available from https://github.com/ScialdoneLab/COMUNET, along with all the code to perform the analyses reported here.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Single-cell RNA sequencing has been extensively used in the last few years to analyse intercellular communication in tissues (see, e.g., (Camp et al, 2017; Puram et al, 2017; Zepp et al, 2017; Zhou et al, 2017; Cohen et al, 2018; Halpern et al, 2018; Skelly et al, 2018; Vento-Tormo et al, 2018; Kumar et al, 2018; Schiebinger et al, 2019; Bonnardel et al, 2019; Sheikh et al, 2019))
We present COMUNET (Cell cOMmunication exploration with MUltiplex NETworks), a new tool to visualize and interpret cell-cell communication that is based on multiplex networks
We compared the communication patterns in the two samples and revealed that 6 ligand-receptor pairs dramatically change their pattern of communication upon treatment. Among those we found TNFSF13-FAS and CCL5-CCR5, for which both the ligand and the receptor play a role in Acute Myeloid Leukemia (AML) and other cancers (Chapellier et al, 2019; Gmeiner et al, 2015; Brenner et al, 2016; Mollica Poeta et al, 2019; Aldinucci and Colombatti, 2014)(Figure 1D, Suppl Fig 2, Suppl Table 3)
Summary
Single-cell RNA sequencing (scRNA-seq) has been extensively used in the last few years to analyse intercellular communication in tissues (see, e.g., (Camp et al, 2017; Puram et al, 2017; Zepp et al, 2017; Zhou et al, 2017; Cohen et al, 2018; Halpern et al, 2018; Skelly et al, 2018; Vento-Tormo et al, 2018; Kumar et al, 2018; Schiebinger et al, 2019; Bonnardel et al, 2019; Sheikh et al, 2019)). Several algorithms to perform these analyses have been published (for instance, (Efremova et al, 2019; Vento-Tormo et al, 2018; Rieckmann et al, 2017; Boisset et al, 2018; Ramilowski et al, 2015) and they all start from a database of interacting partners (e.g., ligand and receptor pairs) to infer, from their expression patterns, a list of potential communication pathways between cell types. Results are visualized with graphs or heatmaps; with a large number of potentially communicating cell types and ligand-receptor pairs, these visualization strategies become busy, poorly interpretable and hinder data-driven hypothesis generation. COMUNET allows unsupervised clustering of ligand-receptor pairs, search for specific patterns of communication and comparison between two biological conditions, aiding the interpretability of the results and the identification of promising candidate molecules to follow up on
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