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  "Title": "Identifying Synergistic Gene Pairs in Single-Cell and Spatial\nTranscriptomics",
  "Description": "Discovers synergistic gene pairs in single-cell RNA-seq\nand spatial transcriptomics data. Unlike conventional pairwise\nco-expression analyses that rely on a single correlation\nmetric, scPairs integrates 14 complementary metrics across five\northogonal evidence layers to compute a composite synergy score\nwith optional permutation-based significance testing. The five\nevidence layers span cell-level co-expression (Pearson,\nSpearman, biweight midcorrelation, mutual information, ratio\nconsistency), neighbourhood-aware smoothing (KNN-smoothed\ncorrelation, neighbourhood co-expression, cluster pseudo-bulk,\ncross-cell-type, neighbourhood synergy), prior biological\nknowledge (GO/KEGG co-annotation Jaccard, pathway bridge\nscore), trans-cellular interaction, and spatial co-variation\n(Lee's L, co-location quotient). This multi-scale design\nenables researchers to move beyond simple co-expression towards\na comprehensive characterisation of cooperative gene regulation\nat transcriptomic and spatial resolution. For more information,\nsee the package documentation at\n<https://github.com/zhaoqing-wang/scPairs>.",
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