A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
Background: Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at cellular resolution but is inherently affected by sparsity caused by dropout events, where expressed genes are recorded as zeros due to technical limitations. These artifacts distort gene expression distributions and compromise downstream analyses. Numerous imputation methods have been proposed to recover latent transcriptional signals. These methods range from traditional statistical models to deep learning (DL)-based methods. However, their comparative performance remains unclear, as existing benchmarks evaluate only a limited subset of methods, datasets, and downstream analyses. Results: We present ...
| Authors | Yuichiro Iwashita, Ahtisham Fazeel Abbasi, Koichi Kise et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 25 March 2026 |
| Last updated at source | 13 April 2026 |
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