bioRxivBioinformaticsPreprint
MMCCI: multimodal integrative analysis of single-cell and spatial cell-type communications to uncover overarching and condition-specific ligand-receptor interaction pathways
Levi HockeyiD, Onkar MulayiD, Zherui XiongiD, Samuel X. TaniD, Kiarash KhosrotehraniiD, Christian Maximilian NefzgeriD, Quan Hoang NguyeniD
Abstract
Cell-cell interaction (CCI) analyses are an indispensable tool for harnessing the detail and depth of spatial and single-cell transcriptomics datasets by inferring inter-cellular communications, but no methods to integrate CCI results across samples exist currently. To address this, we have developed a computational pipeline, Multimodal CCI (MMCCI), to statistically integrate and analyze CCI results from existing popular CCI tools. We benchmarked MMCCIs integration on single-cell spatial datasets and found it to be highly accurate compared to simpler methods. We utilized MMCCIs integration and downstream biological analyses to uncover global and differential interaction patterns in multimodal aging brain and melanoma spatial datasets.
- organism: human
- assay: spatial transcriptomics
- assay: scRNA-seq
- research focus: cell-cell communication
- research focus: benchmarking
- approach: statistical model
- Citations
- 2
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