Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data

TL;DR

This paper reviews various computational packages and algorithms for analyzing Spatially Resolved Transcriptomics data, emphasizing the need for standardized benchmarks and highlighting current challenges in identifying spatially variable genes.

Contribution

It provides a comprehensive comparison of existing tools for SRT data analysis and discusses the importance of establishing ground truth for benchmarking purposes.

Findings

Multiple packages for SRT analysis are evaluated.

Challenges in standardizing ground truth are discussed.

The need for benchmarking frameworks is highlighted.

Abstract

The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised 'ground truth' in the biological data for benchmarking.