Pretrained gene representations transfer mean expression more broadly than spatial patterns in virtual spatial transcriptomics
Models that combine tissue images with pretrained gene representations aim to predict spatial expression for genes not used to fit the downstream predictor. Yet success on held-out genes can reflect two capabilities: estimating a gene's mean expression across tissue locations and recovering its spatial variation. Across four cohorts spanning three human brain regions and HER2-positive breast…
We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.
