Clustering spatial transcriptomics data.

ABSTRACT:

Motivation

Recent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types, we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types.

Results

FICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated

SUBMITTER: Teng H 

PROVIDER: S-EPMC8796363 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature