A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data

2026-03-24

AI Summary

Single-cell RNA sequencing data are commonly affected by technical noise, leading to “dropout” events that induce sparsity and hinder downstream analyses. This study presents the first systematic evaluation of 15 imputation methods—including statistical models and deep learning approaches such as autoencoders, GANs, GNNs, and diffusion models—across a large-scale, multidimensional benchmark encompassing 30 datasets, 10 experimental protocols, and 6 types of downstream tasks. The results demonstrate that no single method consistently outperforms others across all settings; performance is highly dependent on both the specific task and data characteristics. Notably, conventional methods generally surpass deep learning-based ones, and improvements in numerical recovery often do not translate into enhanced biological interpretability. This work provides an empirical foundation and benchmark framework for task-oriented selection of imputation strategies.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Single-cell RNA sequencing (scRNA-seq) is inherently affected by sparsity caused by dropout events, in which expressed genes are recorded as zeros due to technical limitations. These artifacts distort gene expression distributions and can compromise downstream analyses. Numerous imputation methods have been proposed to address this, and these methods encompass a wide range of approaches from traditional statistical models to recently developed deep learning (DL)-based methods. However, their comparative performance remains unclear, as existing benchmarking studies typically evaluate only a limited subset of methods, datasets, and downstream analytical tasks. Here, we present a comprehensive benchmark of 15 scRNA-seq imputation methods spanning 7 methodological categories, including traditional and modern DL-based methods. These methods are evaluated across 30 datasets sourced from 10 experimental protocols and assessed in terms of 6 downstream analytical tasks. Our results show that traditional imputation methods, such as model-based, smoothing-based, and low-rank matrix-based methods, generally outperform DL-based methods, such as diffusion-based, GAN-based, GNN-based, and autoencoder-based methods. In addition, strong performance in numerical gene expression recovery does not necessarily translate into improved biological interpretability in downstream analyses. Furthermore, the performance of imputation methods varies substantially across datasets, protocols, and downstream analytical tasks, and no single method consistently outperforms others across all evaluation scenarios. Together, our results provide practical guidance for selecting imputation methods tailored to specific analytical objectives and highlight the importance of task-specific evaluation when assessing imputation performance in scRNA-seq data analysis.

Problem

• single-cell RNA sequencing
• dropout
• imputation
• sparsity
• benchmarking

Innovation

• imputation benchmarking
• single-cell RNA sequencing
• dropout correction
• deep learning
• downstream analysis