Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

TL;DR

This paper reviews and categorizes 33 computational methods for detecting spatially variable genes in spatial transcriptomics data, highlighting their differences, applications, and challenges to guide future research and benchmarking.

Contribution

It provides a comprehensive categorization and analysis of existing SVG detection methods, clarifying their underlying assumptions and testing strategies.

Findings

Methods are categorized into three SVG types: overall, cell-type-specific, and spatial-domain-marker.

The review explains the underlying hypotheses and trade-offs of different methods.

It discusses challenges and proposes future directions for SVG detection improvements.

Abstract

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.