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  4. Vol 9, Issue 1, 2025

A Review of scRNA-seq Imputation Methods

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DOI: 10.23977/acss.2025.090112 | Downloads: 16 | Views: 759

Author(s)

Zhiqiang Zhang 1

Affiliation(s)

1 School of Information, Yunnan Normal, University, Kunming, China

Corresponding Author

Zhiqiang Zhang

ABSTRACT

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for profiling gene expression at the individual cell level, enabling the discovery of cellular heterogeneity that traditional bulk RNA sequencing cannot capture. However, technical limitations such as low transcript capture efficiency, amplification biases, and limited sequencing depth have led to pervasive dropout events, where true gene expression is obscured by excessive zero counts. This review systematically examines and compares the principal imputation methods developed to address these challenges in scRNA-seq data analysis. We categorize these approaches into two broad groups: model-based methods and deep learning methods. Model-based techniques utilize probabilistic models or matrix factorization to exploit similarities among cells and genes—either independently or in combination—to predict and restore missing values. In contrast, deep learning methods leverage the capabilities of autoencoders, graph neural networks, and other innovative network architectures, including generative adversarial networks, to capture complex nonlinear relationships within high-dimensional, noisy data. While model-based approaches offer greater interpretability through explicit statistical assumptions, they are often limited by their sensitivity to noise and data sparsity. Deep learning strategies, although computationally intensive and less interpretable, excel in recovering intricate data structures in large-scale datasets. By providing a comprehensive overview of these imputation strategies, this review aims to guide researchers in selecting the most appropriate methods for their specific datasets and downstream analyses, and to suggest future directions for improving imputation accuracy and integrating multi-omics data.

KEYWORDS

Single-cell RNA Sequencing (scRNA-seq); Imputation; Dropout; Model-based Methods; Deep Learning; Autoencoder; Graph Neural Network; Cellular Heterogeneity; Gene Expression; Data Recovery

CITE THIS PAPER

Zhiqiang Zhang, A Review of scRNA-seq Imputation Methods. Advances in Computer, Signals and Systems (2025) Vol. 9: 79-83. DOI: http://dx.doi.org/10.23977/acss.2025.090112.

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