SpotGF: Denoising spatially resolved transcriptomics data using an optimal transport-based gene filtering algorithm

Highlights•SpotGF demonstrates strong noise-reduction capabilities across diverse SRT datasets•SpotGF-denoised data exhibit enhanced performance in multiple downstream analyses•SpotGF avoids introduction of false positives and additional noise when denoising•SpotGF is user-friendly and computationally efficientSummarySpatially resolved transcriptomics (SRT) combines gene expression profiles with the physical locations of cells in their native states but suffers from unpredictable spatial noise due to cell damage during cryosectioning and exposure to reagents for staining and mRNA release. To address this noise, we developed SpotGF, an algorithm for denoising SRT data using optimal transport-based gene filtering. SpotGF quantifies diffusion patterns numerically, distinguishing widespread expression genes from aggregated expression genes and filtering out the former as noise. Unlike conventional denoising methods, SpotGF preserves raw sequencing data, thereby avoiding false positives that can arise from imputation. Additionally, SpotGF demonstrates superior performance in cell clustering, identifying potential marker genes, and annotating cell types. Overall, SpotGF has the potential to become a crucial preprocessing step in the downstream analysis of SRT data. The SpotGF software is freely available at GitHub. A record of this paper's transparent peer review process is included in the supplemental information.Graphical abstract