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scPairs

Identifying Synergistic Gene Pairs in Single-Cell and Spatial Transcriptomics

v0.1.8 · Mar 5, 2026 · MIT + file LICENSE

Description

Discovers synergistic gene pairs in single-cell RNA-seq and spatial transcriptomics data. Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at <https://github.com/zhaoqing-wang/scPairs>.

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NOTE 10 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
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Dependency Network

Dependencies Reverse dependencies data.table ggplot2 ggraph ggrepel igraph Matrix patchwork Seurat SeuratObject tidygraph tidyr scPairs

Version History

new 0.1.8 Mar 9, 2026