# Interpretable single-cell factor decomposition using sciRED

## TL;DR

The paper introduces sciRED, a new method for analyzing single-cell RNA sequencing data that improves the interpretation of biological signals by accounting for confounding factors and identifying hidden patterns.

## Contribution

The novel contribution is sciRED, an interpretable factor decomposition method for scRNA-seq that enhances biological signal identification through confounder removal and factor mapping.

## Key findings

- sciRED identifies sex-specific variation in kidney scRNA-seq data.
- The method distinguishes strong and weak immune stimulation signals in PBMC datasets.
- sciRED reduces ambient RNA contamination in rat liver data to reveal strain variation.

## Abstract

Single-cell RNA sequencing (scRNA-seq) 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.