[2603.24626] A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data

Quantitative Biology > Genomics arXiv:2603.24626 (q-bio) [Submitted on 25 Mar 2026] Title:A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data Authors:Yuichiro Iwashita, Ahtisham Fazeel Abbasi, Muhammad Nabeel Asim, Andreas Dengel View a PDF of the paper titled A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data, by Yuichiro Iwashita and 3 other authors View PDF HTML (experimental) 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 m...