AcImpute：一种用于估算单细胞RNA测序数据的基于约束增强平滑的方法。

AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data.

作者信息

Zhang Wei, Liu Tiantian, Zhang Han, Li Yuanyuan

机构信息

School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China.

出版信息

Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btae711.

DOI:10.1093/bioinformatics/btae711

PMID:40037523

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11890269/

Abstract

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for studying cellular heterogeneity and complexity. However, dropout events in single-cell RNA-seq data severely hinder the effectiveness and accuracy of downstream analysis. Therefore, data preprocessing with imputation methods is crucial to scRNA-seq analysis.

RESULTS

To address the issue of oversmoothing in smoothing-based imputation methods, the presented AcImpute, an unsupervised method that enhances imputation accuracy by constraining the smoothing weights among cells for genes with different expression levels. Compared with nine other imputation methods in cluster analysis and trajectory inference, the experimental results can demonstrate that AcImpute effectively restores gene expression, preserves inter-cell variability, preventing oversmoothing and improving clustering and trajectory inference performance.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/Liutto/AcImpute.

摘要

动机

单细胞RNA测序（scRNA-seq）为研究细胞异质性和复杂性提供了一个强大的工具。然而，单细胞RNA-seq数据中的缺失事件严重阻碍了下游分析的有效性和准确性。因此，使用插补方法进行数据预处理对于scRNA-seq分析至关重要。

结果

为了解决基于平滑的插补方法中的过度平滑问题，本文提出了AcImpute，这是一种无监督方法，通过约束不同表达水平基因的细胞间平滑权重来提高插补准确性。在聚类分析和轨迹推断中与其他九种插补方法相比，实验结果表明AcImpute能够有效地恢复基因表达，保留细胞间变异性，防止过度平滑并提高聚类和轨迹推断性能。