.png){kind=icon}
返回
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}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
diffusion model
spatial transcriptomics data
scRNA-seq data
imputation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.7
论文数:
5.8K
被引数:
2.7W
机构
引用论文
Spatial organization of the somatosensory cortex revealed by osmFISHosmFISH揭示的体感皮层的空间组织.png)
NATURE METHODS
NATURE METHODSIF32.1
PREAI
分享收藏Uranium release from different size fractions of sediments in Hanford 300 area, Washington, USA
PREAI
分享收藏From Louvain to Leiden: guaranteeing well-connected communities从鲁汶到莱顿: 保证良好连接的社区SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis空间和单细胞转录组数据的整合阐明了小鼠器官发生NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏分享收藏Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmiaSCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAI分享收藏
查看更多内容