FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution.

ABSTRACT:

Motivation

Factor analysis is a widely used tool for unsupervised dimensionality reduction of high-throughput datasets in molecular biology, with recently proposed extensions designed specifically for spatial transcriptomics data. However, these methods expect (count) matrices as data input and are therefore not directly applicable to single molecule resolution data, which are in the form of coordinate lists annotated with genes and provide insight into subcellular spatial expression patterns. To address this, we here propose FISHFactor, a probabilistic factor model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single molecule resolution data. In addition, FISHFactor shar

SUBMITTER: Walter FC 

PROVIDER: S-EPMC10176502 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature