Biology subjects

Edelman, D.

Publications and source records attributed to Edelman, D..

2 recordsLinked to original sources

bioRxiv2026

Boosting Single-Cell Methods Significantly Enhances Cell-type Classification

Single-cell sequencing drives biological discovery by revealing key cellular processes, cell types, and cell states. Recent technological advances have accelerated atlas-scale data generation across laboratories. With the increasing amount of single-cell RNA-seq (scRNA-seq) data, new challenges emerge, including effective batch correction, cell-type annotation, rare cell-type and cell-state detection, as well as biological pathway discovery. Inspired by methods for boosting weak learners such as AdaBoost, here we present a novel paradigm, scBoost, that combines single-cell analysis methods using stacked ensembles to boost task performance beyond that of individual methods. We show that scBoost pretrained on Tabula Sapiens 2.0 dataset performs significantly better than existing methods in cell-type classification on the real-world Human Fetal Heart Map and Plaque Atlas. We also demonstrate that scBoost substantially improves the performance of cell-type classification and ranks at the top of the leaderboard on the OpenProblems benchmark, a standard-in-the-field. scBoost is modular and is adaptive to new single-cell methods, which will enable it to continue to serve as a resource to the community in the future.

bioinformatics↗

bioRxiv2026

BatchRefiner: fast, significant improvement in batch integration of single-cell embeddings with ensemble refinement

Data from single-cell RNA sequencing (scRNA-seq) and the Assay for Transposase-Accessible Chromatin (scATAC-seq) are high-dimensional, sparse, and undesirably capture technical variability between experiments or batches. Many analysis methods thus seek to produce a low-dimensional cell-by-feature embedding space that groups together biologically similar cells across batches while distancing dissimilar cells. Here, we introduce ensemble refinement for scRNA-seq and scATAC-seq embeddings, inspired by ensemble methods from statistical machine learning, and implement BatchRefiner, a fast post-processing tool to enhance batch integration. We extensively benchmark widely-used scRNA-seq embedding methods on both batch integration and biological conservation over a wide range of datasets, before and after the addition of BatchRefiner. We extend these benchmarking approaches to provide the first comprehensive benchmark of batch integration for scATAC-seq embedding methods, including BatchRefiner. Importantly, we formalize a significance statistic, which we use to demonstrate BatchRefiner's significant improvement in batch integration across a wide range of embedding methods, atlas-scale datasets, and established metrics.

bioinformatics↗