Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Abstract

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

Characterization of Cell-cell Communication in Autistic Brains with Single Cell Transcriptomes

“…Analyzing the data by our global approach showed that the changes in L-R strengths between ASD and controls were highly correlated to what were obtained using all cells (Figure S8). In addition, we tested two other software (NATMI and CellPhoneDB) 29,31 and obtained significantly overlapping but not identical results, with the difference largely due to differences in both the collection of ligand-receptors and how interactions are quantified, as discussed by others 33 .…”

Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

“…It is also more accurate, resulting in a marked 10-20% higher classification accuracy of subjects with COVID-19 when compared to CellChat (Figures 3e-h), the only available tool that summarizes multiple pairwise comparisons of contexts. However, it is important to consider that benchmarking tools for predicting CCC is challenging due to the lack of a ground truth 5 , and it is hard to compare and evaluate tools because of the diversity of scoring approaches 57 . Indeed, the outputs and details offered by CellChat and Tensor-cell2cell differ.…”

“…For instance, our strategy can be readily applied to time series data by considering time points as the contexts, and to spatial transcriptomic datasets, by previously defining cellular niches or neighborhoods as the contexts. Moreover, we have also included Tensor-cell2cell as a part of our previously developed tool cell2cell 60 , enabling previous functionalities such as employing any list of LR pairs (even including protein complexes), multiple visualization options, and personalizing the communication scores to account for other signaling effects such as the (in)activation of downstream genes in a signaling pathway 23,61 , which could lead to different biological interpretations 57 . Lastly, we demonstrated that Tensor-cell2cell stands as a fast, low-memory and accurate method (Figure 3), which can be substantially accelerated when a GPU is available (Figure 3a).…”

Squidpy: a scalable framework for spatial omics analysis

“…Applied to the same dataset, it highlighted different ligand-receptor pairs between the 'Hippocampus' cluster and the two 'Pyramidal layer' clusters. Whether permutation-based tests of ligand-receptor interaction identification are able to pinpoint cellular communication and pathway activity is an open question 45 . However, it is useful to inform such results with a quantitative understanding of cluster co-occurrence.…”