Biology subjects

Bendall, S.

Publications and source records attributed to Bendall, S..

2 recordsLinked to original sources

bioRxiv2025

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics Data

Single-cell and spatial proteomic technologies capture complementary biological information, yet no single platform can measure all modalities within the same cell. Most existing integration methods are optimized for transcriptomic data and rely on a large set of shared, strongly linked features, an assumption that often fails for low-dimensional proteomic modalities. We present CellFuse, a deep learning-based, modality-agnostic integration framework designed specifically for settings with limited feature overlap. CellFuse leverages supervised contrastive learning to learn a shared embedding space, enabling accurate cell type prediction and seamless integration across modalities and experimental conditions. Across a range of datasets including healthy PBMCs, bone marrow, CAR-T-treated lymphoma, and healthy and tumor tissues--CellFuse consistently outperforms existing methods in both integration quality and runtime efficiency. It maintains high accuracy even in the presence of missing markers and rare cell types, and performs robustly in cross-dataset comparisons, making it a powerful tool for scalable and high-fidelity single-cell data integration in basic and translational research.

bioinformatics↗

bioRxiv2020

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics↗