ICML 2025

scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Olga Ovcharenko * 1 Florian Barkmann * 2 Philip Toma * 2 Imant Daunhawer 2 Julia E. Vogt 2
Sebastian Schelter † 1 Valentina Boeva † 2 3 4
Abstract
Self-supervised learning (SSL) has proven to be
a powerful approach for extracting biologically
meaningful representations from single-cell data.
To advance our understanding of SSL methods
applied to single-cell data, we present scSSL-
Bench, a comprehensive benchmark that evalu-
ates nineteen SSL methods. Our evaluation spans
nine datasets and focuses on three common down-
stream tasks: batch correction, cell type anno-
tation, and missing modality prediction.
Fur-
thermore, we systematically assess various data
augmentation strategies.
Our analysis reveals
task-specific trade-offs: the specialized single-cell
frameworks, scVI, CLAIRE, and the finetuned
scGPT excel at uni-modal batch correction, while
generic SSL methods, such as VICReg and Sim-
CLR, demonstrate superior performance in cell
typing and multi-modal data integration. Random
masking emerges as the most effective augmenta-
tion technique across all tasks, surpassing domain-
specific augmentations. Notably, our results indi-
cate the need for a specialized single-cell multi-
modal data integration framework. scSSL-Bench
provides a standardized evaluation platform and
concrete recommendations for applying SSL to
single-cell analysis, advancing the convergence
of deep learning and single-cell genomics.
1. Introduction
Recent progress in single-cell RNA sequencing (scRNA-
seq) and multi-omics sequencing technologies has trans-
formed our understanding of cellular heterogeneity by en-
*† Equal contribution 1BIFOLD & TU Berlin, Berlin, Ger-
many 2Department of Computer Science, ETH Zurich, Zurich,
Switzerland
3Swiss Institute of Bioinformatics,
Lausanne,
Switzerland 4Paris Cit´e University, Cochin Institute, INSERM
U1016, Paris, France. Correspondence to: Olga Ovcharenko
<ovcharenko@tu-berlin....