Work

PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns

Year: 2026

Type: article

Abstract: Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.

Source: Genome biology

DOI: 10.1186/s13059-026-03994-3

Authors: Haotian Zhuang, Zhicheng Ji

Institutions: Duke University, Duke Medical Center

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funder: National Institutes of Health