Alignment of spatial genomics data using deep Gaussian processes.
Nat Methods
; 20(9): 1379-1387, 2023 09.
Article
em En
| MEDLINE
| ID: mdl-37592182
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Genômica
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Nat Methods
Assunto da revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos