.png){kind=icon}
返回
Alignment of spatial genomics data using deep Gaussian processes
.png){kind=icon}
OA
AI
DOI：10.1038/s41592-023-01972-2.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Gaussian Process Spatial Alignment (GPSA) aligns multiple spatially resolved genomics and histology datasets and improves downstream analysis. Spatially resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals and technologies. Here, we propose a probabilistic model that aligns spatially-resolved samples onto a known or unknown common coordinate system (CCS) with respect to phenotypic readouts (for example, gene expression). Our method, Gaussian Process Spatial Alignment (GPSA), consists of a two-layer Gaussian process: the first layer maps observed samples' spatial locations onto a CCS, and the second layer maps from the CCS to the observed readouts. Our approach enables complex downstream spatially aware analyses that are impossible or inaccurate with unaligned data, including an analysis of variance, creation of a dense three-dimensional (3D) atlas from sparse two-dimensional (2D) slices or association tests across data modalities.
Keyword:
TALAIRACH ATLAS
EXPRESSION
SPACE
MODEL
SEQ
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:
32.1
论文数:
7.2K
被引数:
12.7W
机构
引用论文
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO使用MEFISTO从多模态数据中识别变化的时空模式.png)
NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏分享收藏A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal CortexNEURON
OAAI
NEURONIF15
OAAI分享收藏
查看更多内容