SpotClean Adjusts for Spot Swapping in Spatial Transcriptomics Data

Overview

Journal Nat Commun

Specialty Biology

Date 2022 May 27

PMID 35624112

Authors

Zijian Ni

Aman Prasad

Shuyang Chen

Richard B Halberg

Lisa M Arkin

Beth A Drolet

Michael A Newton

Christina Kendziorski

Affiliations

Soon will be listed here.

Abstract

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.

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