Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno.

ABSTRACT: In the analysis of both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, classifying cells/spots into cell/domain types is an essential analytic step for many secondary analyses. Most of the existing annotation methods have been developed for scRNA-seq datasets without any consideration of spatial information. Here, we present SpatialAnno, an efficient and accurate annotation method for spatial transcriptomics datasets, with the capability to effectively leverage a large number of non-marker genes as well as 'qualitative' information about marker genes without using a reference dataset. Uniquely, SpatialAnno estimates low-dimensional embeddings for a large number of non-marker genes via a factor model while promoting spatial smoothness among neighbo

SUBMITTER: Shi X 

PROVIDER: S-EPMC10711557 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature