SpatialDE: identification of spatially variable genes

By Valentine Svensson, Sarah A. Teichmann, Oliver Stegle. Nature Methods, 2018

646 citations recorded by the source corpus.

Technological advances have made it possible to measure spatially resolved gene expression at high throughput. However, methods to analyze these data are not established. Here we describe SpatialDE, a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data. SpatialDE also implements 'automatic expression histology', a spatial gene-clustering approach that enables expression-based tissue histology.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/56345ac9378727f64aaf2aad0573a5268334cb82

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Citations in the corpus

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (2020, positive): “SpatialDE: identification of spatially variable genes.”
Computational principles and challenges in single-cell data integration (2021, methodology): “This is the aim of SpatialDE 104 and spatial variance component analysis (SVCA) 105 , both of which build upon Gaussian process regression, a class of models commonly used in geostatistics.”
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, neutral): “We have not addressed major topics in the scRNA-Seq literature such as pseudotime inference [38], differential expression [39], and spatial analysis [40].”
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020, methodology): “Towards this goal, we explored the use of two gene sets: (1) genes exhibiting spatially variable expression patterns (SVGs) using the SpatialDE method (Svensson et al., 2018) within each of the 12 samples (Table S9), and (2) highly variable genes (HVGs) using the scran Bioconductor package ”
Best practices for single-cell analysis across modalities (2023, neutral): “Our work represents an entry point”
stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, methodology): “This assumption is supported by previous studies demonstrating correlation between gene expression and morphology 23 , or have reported genes with localised expression patterns 21 .”