Comparative analysis of cell–cell communication at single-cell resolution
, which indicates that these circuits may induce downstream Notch signaling. In sum, these data illustrate how the single-cell resolution of Scriabin’s CCC analysis workflow can perform integrated longitudinal analyses, nominating hypotheses for experimental validation.Most existing CCC methodologies aggregate ligand and receptor expression values at the level of the cell type or cluster, potentially obscuring biologically valuable information.
A major challenge of single-cell-resolved CCC analysis is data inflation: because CCC analysis fundamentally involves performing pairwise calculations on cells or cell groups, it is frequently computationally prohibitive to analyze every sender–receiver cell pair. Some existing tools, such as NICHES, support single-cell resolution CCC analysis but involve subsampling strategies when applied at scale.
We observe that aggregation obscures potentially biologically meaningful subsets of T cells in SCC as well as in RRs in leprosy granulomas. Owing to the degree of transcriptional perturbation in T cells during RRs, subclustering is not always a tenable approach to increasing the resolution of CCC analyses because it, in turn, can preclude analysis at a per-sample level.
As the throughput of scRNA-seq workflows continues to increase, it will be important that single-cell-resolution CCC methods are scalable to any dataset size. We introduce two complementary workflows to address this challenge.
As an alternative, we also introduce a second single-cell-resolution CCC workflow that is scalable to datasets of any size. The interaction program discovery workflow of Scriabin accomplishes this by focusing first on common patterns of ligand–receptor pair co-expression rather than individual cell–cell pairs. Individual cells can be scored for expression of these interaction programs post hoc, enabling downstream comparative analyses with a comprehensive view of CCC structure.
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