Journal Article

Interpretable single-cell factor decomposition using sciRED

Pouyabahar, Delaram, Bader, Gary D., Andrews, Tallulah

2025

Overview

Single-cell RNA sequencing maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena, and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data. Single-cell RNA sequencing maps tissue-level gene expression heterogeneity but faces challenges in interpreting biological signals due to noise and technical confounders. Here, the authors present sciRED, a method that enhances the interpretation of scRNA-seq factorization by linking factors to known covariates and hidden biological phenomena.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 38 / 631/114/1305 / 631/114/2164 / 631/114/2415 / Animals / Biological activity / Datasets / Decomposition / Factor analysis / Factorization / Female / Gene expression / Gene Expression Profiling - methods / Gene mapping / Gene sequencing / Heterogeneity / Humanities and Social Sciences / Humans / Kidney - cytology / Kidney - metabolism / Leukocytes, Mononuclear - metabolism / Liver / Liver - cytology / Liver - metabolism / Male / multidisciplinary / Peripheral blood mononuclear cells / Rats / Ribonucleic acid / RNA / RNA-Seq - methods / Science / Science (multidisciplinary) / Sequence Analysis, RNA - methods / Single-Cell Analysis - methods / Zonation