Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
Natalie Charitakis, Agus Salim, Adam T. Piers, Kevin I. Watt, Enzo R. Porrello, David A. Elliott, Mirana Ramialison
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.