Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Genome Biology, 2022 · DOI: https://doi.org/10.1186/s13059-022-02783-y · Published: October 4, 2022

Simple Explanation

Cell-cell interactions are crucial for communication between cells, underpinning many biological processes. This study evaluates computational methods that infer these interactions using single-cell RNA sequencing data, which reveals the genes each cell expresses. The study integrates spatial transcriptomics data, showing the physical locations of cells. The spatial distance between cell types indicates their likelihood of interaction, serving as a benchmark for assessing cell-cell interaction tools. By comparing predicted interactions with observed spatial relationships, the researchers benchmarked 16 cell-cell interaction methods, identifying those that best align with spatial tendencies and demonstrating software scalability.

Study Duration

Not specified

Participants

15 simulated and 5 real scRNA-seq and ST datasets

Evidence Level

Not specified

Key Findings

1
The study found that cell-cell interactions predicted by different tools are highly dynamic, suggesting variability in the methods' outputs.
2
Statistical-based methods generally outperformed network-based and spatial transcriptomics-based methods in predicting cell-cell interactions.
3
CellChat, CellPhoneDB, NicheNet, and ICELLNET showed superior performance in consistency with spatial tendency and software scalability compared to other tools.

Research Summary

This study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq data by integrating spatial information to benchmark their performance. The spatial distance between interacting cells was used to evaluate the consistency of predicted interactions, classifying them into short-range and long-range interactions. The results suggest that statistical-based methods generally perform better, and combining results from multiple CCI tools enhances the reliability of predictions.

Practical Implications

Tool Selection

Researchers should consider using statistical-based methods like CellChat and CellPhoneDB for more reliable cell-cell interaction predictions.

Integrative Analysis

Combining results from multiple cell-cell interaction tools can enhance the accuracy and confidence of identified interactions.

Spatial Context

Integrating spatial information with single-cell RNA sequencing data is crucial for evaluating the likelihood and relevance of predicted cell-cell interactions.

Study Limitations

1
The spatial resolution of current spatial transcriptomics technologies is limited, which may affect the accuracy of cell-cell interaction inference.
2
The thresholds and parameters used for different tools could significantly affect the results, potentially influencing the number of interactions predicted.
3
The interaction database used can significantly impact the final result of a CCI tool, with tools having few overlaps between ligands, receptors, and interactions.

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