Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data

Linhua Wang, Mirjana Maletic-Savatic and Zhandong Liu ()
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Linhua Wang: Baylor College of Medicine
Mirjana Maletic-Savatic: Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital
Zhandong Liu: Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.

Date: 2022
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DOI: 10.1038/s41467-022-34567-0

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