Accurate Imputation of Pathway-Specific Gene Expression in Spatial Transcriptomics With Pasta

Authors

Ruoxing Li
Peng Yang
Mauro Di Pilato
Jianjun Zhang
Christopher R Flowers
Lulu Shang
Ziyi Li

Language

English

Publication Date

12-16-2025

Journal

Nature Communications

DOI

10.1038/s41467-025-67421-0

PMID

41397970

PMCID

PMC12820036

PubMedCentral® Posted Date

12-16-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Mapping the entire transcriptome at single-cell resolution under its natural spatial context is essential for investigating the oncogenesis and progression of diseases. The recently emerged targeted in-situ technologies retain the spatial organization of cells at high resolution, although they remain limited in the number of genes that can be simultaneously measured. To overcome this obstacle, numerous computational methods have been developed to predict unmeasured gene expression in spatial transcriptomics data by leveraging scRNA-seq data. Most of these methods focus on the expression of individual genes and usually generate highly variable predictions. In this study, we introduce PASTA (PAthway-oriented Spatial gene impuTAtion), a spatial pathway expression imputation method that leverages cell type and spatial proximity to enhance prediction accuracy. PASTA assumes that nearby cells and cells of the same type exhibit similar expression patterns, along with pathway information integrated into the imputation process, which improves prediction robustness and enhances biological relevance in spatial transcriptomics data. We demonstrate PASTA's superior performance across both simulated and real-world datasets, highlighting its ability to impute pathway gene expression with improved stability and biological significance.

Keywords

Transcriptome, Gene Expression Profiling, Humans, Single-Cell Analysis, Computational Biology, Algorithms, RNA-Seq, Computational models, Genetics research, Next-generation sequencing, Gene expression profiling

Published Open-Access

yes

Recommended Citation

Li, Ruoxing; Yang, Peng; Di Pilato, Mauro; et al., "Accurate Imputation of Pathway-Specific Gene Expression in Spatial Transcriptomics With Pasta" (2025). Faculty, Staff and Student Publications. 5536.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5536