Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?

ABSTRACT: Recent advancements in spatially resolved transcriptomics (SRT) have enabled near single-cell resolution, providing rich spatial context crucial for uncovering biological insights. However, high-resolution SRT datasets remain sparse and prone to dropout events that may impede accurate interpretation. Computational imputation methods are often employed to recover missing values, yet existing state-of-the-art (SOTA) techniques-designed for tabular, single-cell RNA, or general SRT data-have not been systematically benchmarked on datasets produced by newer SRT technologies. In this study, we evaluate seven SOTA imputation methods across five emerging SRT platforms encompassing 23 datasets. Our results reveal that no single method consistently excels, with most struggling to accurately identify

SUBMITTER: Hafiz F 

PROVIDER: S-EPMC12862982 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature