.png){kind=icon}
Return
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics
.png){kind=icon}
OA
AI
DOI：10.1093/bib/bbae171.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.
Keywords:
diffusion model
spatial transcriptomics data
scRNA-seq data
imputation
Journal
IF:
7.7
Papers:
5.8K
Citations:
2.7W
Organization
Cited Papers
ShareSaveUranium release from different size fractions of sediments in Hanford 300 area, Washington, USA
PREAI
ShareSaveIntegration of spatial and single-cell transcriptomic data elucidates mouse organogenesis.png)
NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveShareSaveShareSaveShareSaveNon-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmiaSCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAIShareSaveShareSave
View more