Paper List
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
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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.