Paper List
-
Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
-
CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
-
On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
-
EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
-
Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
-
Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
-
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
-
scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
Not specified in provided content
The 30-Second View
IN SHORT: This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective comparison and selection of appropriate methods for specific biological contexts.
Innovation (TL;DR)
- Methodology Introduces scCluBench, the first comprehensive benchmarking framework that systematically evaluates 16 clustering methods across four categories (traditional, deep learning-based, graph-based, and foundation models) on 36 standardized datasets.
- Methodology Establishes standardized protocols for biological interpretation, including reproducible pipelines for marker gene identification and two distinct cell type annotation approaches (best-mapping and marker-overlap), validated with gold-standard references.
- Methodology Provides a unified and modular benchmarking workflow covering data preprocessing, clustering, and annotation with standardized input-output formats, ensuring reproducibility and fair comparison.
Key conclusions
- scCDCG (a cut-informed graph embedding model) achieved the highest average clustering accuracy (81.29 ± 1.45) across 36 datasets, outperforming other graph-based, deep learning, and traditional methods.
- Biological foundation models (scGPT, GeneFormer, GeneCompass) showed strong performance in classification tasks (e.g., scGPT achieved 98.14% ACC on Sapiens Ear Crista Ampullaris) but underperformed in direct clustering, highlighting a trade-off between general representation and task-specific optimization.
- The benchmark reveals method-specific limitations: traditional methods struggle with sparse data, deep learning models may fail to capture cell relationships, and graph-based models can suffer from over-smoothing, while most methods decouple embedding learning from clustering optimization.
Abstract: Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance, benchmarks for scRNA-seq clustering methods remain fragmented, often lacking standardized protocols and failing to incorporate recent advances in artificial intelligence. To fill these gaps, we present scCluBench, a comprehensive benchmark of clustering algorithms for scRNA-seq data. First, scCluBench provides 36 scRNA-seq datasets collected from diverse public sources, covering multiple tissues, which are uniformly processed and standardized to ensure consistency for systematic evaluation and downstream analyses. To evaluate performance, we collect and reproduce a range of scRNA-seq clustering methods, including traditional, deep learning-based, graph-based, and biological foundation models. We comprehensively evaluate each method both quantitatively and qualitatively, using core performance metrics as well as visualization analyses. Furthermore, we construct representative downstream biological tasks, such as marker gene identification and cell type annotation, to further assess the practical utility. scCluBench then investigates the performance differences and applicability boundaries of various clustering models across diverse analytical tasks, systematically assessing their robustness and scalability in real-world scenarios. Overall, scCluBench offers a standardized and user-friendly benchmark for scRNA-seq clustering, with curated datasets, unified evaluation protocols, and transparent analyses, facilitating informed method selection and providing valuable insights into model generalizability and application scope.222All datasets, code, and the Extended version for scCluBench are available at the link: https://github.com/XPgogogo/scCluBench. More details for each stage are provided in the extended version.