SpotClean adjusts for spot swapping in spatial transcriptomics data.
Zijian NiAman PrasadShuyang ChenRichard B HalbergLisa M ArkinBeth A DroletMichael A NewtonChristina KendziorskiPublished in: Nature communications (2022)
Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.
Keyphrases
- single cell
- rna seq
- gene expression
- genome wide
- copy number
- poor prognosis
- dna methylation
- primary care
- papillary thyroid
- high resolution
- magnetic resonance imaging
- binding protein
- young adults
- mass spectrometry
- single molecule
- climate change
- long non coding rna
- risk assessment
- squamous cell
- contrast enhanced
- lymph node metastasis