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

Yuichiro Iwashita; Ahtisham Fazeel Abbasi; Koichi Kise; Andreas Dengel; Muhammad Nabeel Asim

q-bio.GNMar 25, 2026

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

Authors: Yuichiro Iwashita, Ahtisham Fazeel Abbasi, Koichi Kise, Andreas Dengel, Muhammad Nabeel Asim

Organizations: RPTU University Kaiserslautern-Landau, Department of Computer Science, 67663 Kaiserslautern, Germany. · German Research Center for Artificial Intelligence (DFKI GmbH), 67663 Kaiserslautern, Germany. · Graduate School of Informatics, Osaka Metropolitan University, Osaka 599-8531, Japan. · intelligentX GmbH (intelligentX.com), 67663 Kaiserslautern, Germany.

Abstract

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 a comprehensive benchmark of 15 scRNA-seq imputation methods spanning 7 methodological categories, including traditional and DL-based methods. Methods are evaluated across 30 datasets from 10 experimental protocols on 6 downstream analyses. Results show that traditional methods, such as model-based, smoothing-based, and low-rank matrix-based methods, generally outperform DL-based methods, including 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, including cell clustering, differential expression analysis, marker gene analysis, trajectory analysis, and cell type annotation. Furthermore, method performance varies substantially across datasets, protocols, and downstream analyses, with no single method consistently outperforming others. Conclusions: Our findings provide practical guidance for selecting imputation methods tailored to specific analytical objectives and underscore the importance of task-specific evaluation when assessing imputation performance in scRNA-seq data analysis.

Figures & tables

Study	Methods							Datasets	Protocols	Tasks
	Traditional			DL-based
	Model- based	Smoothing- based	Low-rank Matrix-based	Diffusion- based	GAN- based	GNN- based	AE- based
Hou et al. (2020)	6	3	3	0	0	0	6	16	5	4
Dai et al. (2022)	2	3	2	0	1	1	3	8	1	4
Cheng et al. (2023)	2	4	1	0	0	0	4	16	3	3
This Study	2	3	3	2	2	1	2	30	10	6

Table 1: Summary of existing benchmarking studies on scRNA-seq data imputation methods

Figure 1: Distribution of LND between imputed and ground truth expression values for each imputation method. The x-axis represents LND values, and the y-axis represents different imputation methods.

Figure 2: Numerical gene expression recovery performance. a – b MAE and MedAE, respectively. The x-axis represents different imputation methods, and the y-axis represents error values in a log scale. c Protocol-wise total MAE and MedAE. The x-axis represents different imputation methods, and the y-axis represents total error values in a log scale. d – e PCC. The x-axis represents different imputation methods, and the y-axis represents PCC values. f MCC. The x-axis represents different cell lines, and the y-axis represents different imputation methods.

Figure 3: Cell clustering consistency and coherency performance. a ARI. Each plot shows ARI scores of different imputation methods. Each point in a plot represents different methods. The horizontal dashed line represents the masked baseline value. b SC. Each plot shows SC scores of different imputation methods. Each point in a plot represents different methods. The horizontal dashed line represents the masked baseline value.

Figure 4: UMAP visualization of the cell clustering using 4 real datasets with different protocols. Each plot shows the UMAP visualization of the method. Different colors represent different clusters.

Figure 5: UMAP visualization of the cell clustering using 4 simulated datasets with different dropout rates. Each plot shows the UMAP visualization of the method. Different colors represent different clusters.

Figure 6: The overview of 3 complementary analyses of DE analysis. a DE enrichment analysis, b null DE analysis, and c effect size analysis.

Figure 7: DE analysis performance. a – c DE enrichment analysis. IoU between DEGs identified from imputed scRNA-seq data and bulk RNA-seq data using MAST and the Wilcoxon rank-sum test. The dashed line represents equal performance. Each point represents an imputation method. d – f Null DE analysis. Average number of false positive DEGs across 6 different sample sizes for A549 ( d ), GM12878 ( e ), and monocyte ( f ). g – h Effect size analysis. IoU between DEGs identified from imputed scRNA-seq data and high- ( g ) or low- ( h ) LFC genes from bulk RNA-seq data.

Figure 8: Comparison of imputation methods for marker gene expression in hca_10x_tissue. Violin plots show the distribution of expression levels for 8 marker genes across 4 cell types, T cell, B cell, NK cell, and monocyte (shown as Mono). The y-axis represents gene expression values.

Figure 9: Marker gene expression performance on chu_cell_type. a UMAP visualizations with 7 cell type labels, namely DEC, EC, H1, H9, HFF, NPC, and TB, colored by cell type. Each plot represents an imputation method. b Heatmaps of 10 marker gene expression values across the 7 cell types. The x-axis represents individual cells ordered by cell type, and the y-axis represents marker genes.

Figure 10: Trajectory analysis performance. a – b POS and KRCC between the inferred pseudotime from imputed data and the true cellular development time label, respectively. The x-axis and y-axis represent the performance of petropoulos and chu_time_course, respectively. The dashed line represents equal performance across the 2 datasets. Each point in the plot represents an imputation method. c UMAP visualizations with inferred pseudotime trajectories for petropoulos (top) and chu_time_course (bottom). Cells are colored by inferred pseudotime.

Figure 11: Cell type annotation performance. Dumbbell plots comparing ACC, PR, RC, and F1 achieved by 1D-CNN (green circles) and scGPT Ding et al. (2025) (orange triangles) across 6 datasets, including sc_10x_5cl, hca_10x_tissue, chu_cell_type, baron, encode_fluidigm_5cl, and bladder. The x-axis represents the value of each evaluation measure, and the y-axis represents different imputation methods. Each horizontal line connects the 1D-CNN and scGPT performances for a given method.

Category	Method	Numerical gene expression recovery	Cell clustering	DE analysis	Marker gene analysis	Trajectory analysis	Cell type annotation
Model-based	PbImpute	$\triangle$	$\triangle$	$\triangle$	$\triangle$	$\bigstar$	$\triangle$
Model-based	scImpute	$\triangle$	$\triangle$	$\triangle$	$\bigstar$	$\bigstar$	$\triangle$
Smoothing- based	AcImpute	$\triangle$	$\triangle$	$\bigstar$	$\triangle$	$\triangle$	$\triangle$
	scTsI	$\bigstar$	$\triangle$	$\triangle$	$\triangle$	$\triangle$	$\triangle$
	MAGIC	$\triangle$	$\bigstar$	$\triangle$	$\bigstar$	$\triangle$	$\bigstar$
Low-rank matrix-based	PBLR	$\bigstar$	$\times$	$\times$	$\times$	$\triangle$	$\triangle$

Table 4: Summary of the performance of imputation methods.

\bigstar

\triangle

, and

\times

indicate best, moderate, and worst performance, respectively.

Figure 12: Data Imputation Benchmarking Framework.

Dataset	Description	Source	Size ( $\mathrm{\mathbf{Cells}}\times\mathrm{\mathbf{Genes}}$ )	Sparsity Rates (%)	Protocol
ad_case Grubman et al. (2019)	Human brain cells with Alzheimer’s disease	GSE138852	$10278\times 13214$	$94.93$	10x Chromium
jurkat	Human Jurkat T-cell leukemia cell line scRNA-seq dataset	10x Genomics 1 1 1 https://www.10xgenomics.com/datasets/jurkat-cells-1-standard-1-1-0	$1740\times 13494$	$81.19$	10x Chromium
293t	Human HEK293T embryonic kidney cell line scRNA-seq dataset	10x Genomics 2 2 2 https://www.10xgenomics.com/datasets/293-t-cells-1-standard-1-1-0	$2868\times 16290$	$81.20$	10x Chromium
pbmc4k	Peripheral blood mononuclear cells (PBMCs) from a healthy donor.	10x Genomics 3 3 3 https://www.10xgenomics.com/datasets/4-k-pbm-cs-from-a-healthy-donor-2-standard-2-1-0	$4220\times 16412$	$92.26$	10x Chromium
sc_10x Tian et al. (2019) ; Tian et al. (2018)	Single cells from three human lung adenocarcinoma cell lines	GSM3022245	$902\times 16468$	$45.02$	10x Chromium
sc_10x_5cl Tian et al. (2019) ; Tian et al. (2018)	Single cells from five human lung adenocarcinoma cell lines	GSM3618014	$3913\times 11786$	$63.04$	10x Chromium

Table 5: Details of the used scRNA-seq datasets

Figure 13: The overview of downstream tasks used for benchmarking imputation methods. a Numerical gene expression recovery, b cell clustering, c DE analysis, d marker gene analysis, e trajectory analysis, and f cell type annotation.

Incomplete Data Imputation

Incomplete data imputation estimates unobserved values while accounting for data structure, missingness patterns, and uncertainty.

See all 74 papers

Parameter-free representations outperform single-cell foundation models on downstream benchmarks
Feb 18, 2026Huan Souza, Pankaj MehtaSingle-Cell RNA Sequencing Linear Representation Hypothesis
Department of Physics, Boston University, Boston, MA, 02215, USA · Faculty of Computing and Data Science, Boston University, Boston, MA, 02215, USA
scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Jun 17, 2026Jinke Wu, Yifan Wang, Siyu Yi +5Graph SSL Clustering
Sichuan University · University of International Business and Economics · Great Bay University +1
When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data
May 6, 2026Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika +2Causal Discovery Gene Regulatory Network Inference
Faculty of Engineering University of Deusto Bilbao, Spain

Feb 18, 2026J/K move · Enter open · S save

Parameter-free representations outperform single-cell foundation models on downstream benchmarks

Huan Souza, Pankaj Mehta

Department of Physics, Boston University, Boston, MA, 02215, USA · Faculty of Computing and Data Science, Boston University, Boston, MA, 02215, USA

Single-Cell RNA Sequencing Linear Representation Hypothesis OOD Generalization

Open paper AskPDF arXiv

Single-cell RNA sequencing (scRNA-seq) data exhibit strong and reproducible statistical structure. This has motivated the development of large-scale foundation models, such as TranscriptFormer, that use transformer-based architectures to learn a generative model for gene expression by embedding genes into a latent vector space. These embeddings have been used to obtain state-of-the-art (SOTA) performance on downstream tasks such as cell-type classification, disease-state prediction, and cross-species learning. Here, we ask whether similar performance can be achieved without utilizing computationally intensive deep learning-based representations. Using simple, interpretable pipelines that rely on careful normalization and linear methods, we obtain SOTA or near SOTA performance across multiple benchmarks commonly used to evaluate single-cell foundation models, including outperforming foundation models on out-of-distribution tasks involving novel cell types and organisms absent from the training data. Our findings highlight the need for rigorous benchmarking and suggest that the biology of cell identity can be captured by simple linear representations of single cell gene expression data.

Abstract

Incomplete Data Imputation

Explore similar work

Parameter-free representations outperform single-cell foundation models on downstream benchmarks

scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data