Xiang Zhou

Subcellular-resolution spatial transcriptomics can locate transcripts inside cells, but earlier methods targeted single analysis tasks and lacked a unified framework for characterizing and predicting gene localization in a cell-specific, context-aware way. SVC represents each gene's within-cell spatial expression as an image patch and uses other genes in the same cell as context, learning cross-gene spatial dependencies through self-supervised masking. On seqFISH+ mouse embryonic fibroblast data, SVC beat four image-based baselines on all six metrics for predicting subcellular patterns of unmeasured genes, with median gains of 5.2%–21.0% over the more challenging baseline pair.

Subcellular-resolution spatial transcriptomics can locate transcripts inside cells, but earlier methods targeted single analysis tasks and lacked a unified framework for characterizing and predicting gene localization in a cell-specific, context-aware way. SVC represents each gene's within-cell spatial expression as an image patch and uses other genes in the same cell as context, learning cross-gene spatial dependencies through self-supervised masking. On seqFISH+ mouse embryonic fibroblast data, SVC beat four image-based baselines on all six metrics for predicting subcellular patterns of unmeasured genes, with median gains of 5.2%–21.0% over the more challenging baseline pair.