FlexScience

Spatial perturbation transcriptomics aims to measure how a genetically edited cell affects surrounding tissue. I investigated whether these effects form a point-spread-function-like spatial response, bringing a mathematical idea from electrical engineering into an unfamiliar biomedical field. Using a public mouse-brain Spatial Perturb-seq dataset containing 229,775 cells, I found a consequential replication problem. Treating 24,453–31,189 source-recipient pairs as independent produced 16 of 20 nominal program-level hits. Keeping the biological comparisons and point estimates fixed, but computing uncertainty at the 78 or 101 independent perturbation and control sources, increased standard errors by a median of 5.5× and eliminated every hit: 0/20. Wild-cluster bootstrap, source-label permutation, equal-source weighting, non-overlap analysis, alternative adjustments, and leave-one-mouse-out checks agreed. Two Lrrk2 contrasts remain power-limited, method-dependent leads rather than findings. I packaged the central check as a reusable command-line audit that produces naive, source-aware, wild-bootstrap, and equal-source results from a source-recipient table. This matters because neighbour-level pseudoreplication can create false confidence across spatial screens. The resulting design guidance is concrete: count independent sources, balance them across animals, and power experiments at the source level. The broader demonstration is that Claude can help an outsider enter a new research field and contribute rigorously without lowering the standard of evidence.

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