Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach.

ABSTRACT: Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of

SUBMITTER: Biran H 

PROVIDER: S-EPMC11525848 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature