The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduce scConfluence, a method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377776 | PMC |
| http://dx.doi.org/10.1038/s41467-024-51382-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
From basic scientific research to the development of new drugs for pulmonary arterial hypertension: insights from activin-targeting agents.
Breathe (Sheff)
January 2025
Université Paris-Saclay, INSERM UMR_S 999, Hypertension Pulmonaire: Physiopathologie et Innovation Thérapeutique (HPPIT), Faculté de Médecine, Le Kremlin-Bicêtre, France.
Pulmonary arterial hypertension (PAH) is a severe disorder of the pulmonary vasculature leading to right ventricular failure. This pulmonary vascular remodelling leads to increased pulmonary vascular resistance and high pulmonary arterial pressures. Despite the development of new therapies, many patients continue to experience significant morbidity and mortality.
View Article and Find Full Text PDFNonlinear feature selection for support vector quantile regression.
Neural Netw
January 2025
School of Economics, Zhejiang University of Technology, Hangzhou 310023, China; Institute for Industrial System Modernization, Zhejiang University of Technology, Hangzhou 310023, China; Zhijiang College, Zhejiang University of Technology, Shaoxing 312030, China. Electronic address:
This paper discusses the nuanced domain of nonlinear feature selection in heterogeneous systems. To address this challenge, we present a sparsity-driven methodology, namely nonlinear feature selection for support vector quantile regression (NFS-SVQR). This method includes a binary-diagonal matrix, featuring 0 and 1 elements, to address the complexities of feature selection within intricate nonlinear systems.
View Article and Find Full Text PDFBlending deep-learning and observers for improved state of charge estimation in vanadium flow batteries.
ISA Trans
December 2024
Group of Power Systems, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre, 1, 08930, Sant Adrià del Besòs, Spain. Electronic address:
This paper presents the design and implementation of a deep-learning-based observer for accurately estimating the State of Charge (SoC) of a vanadium flow battery. The novelty of the proposal lies in its direct use of terminal voltage and the application of a machine learning algorithm to model the battery's overpotentials, leading to greater accuracy and reduced complexity compared to classical models. The overpotentials model consists of a neural network trained using data generated by a classical observer that estimates species concentration using a physical electrochemical model and the open-circuit voltage measurement.
View Article and Find Full Text PDFOn-Chip Elastic Wave Manipulations Based on Synthetic Dimension.
Phys Rev Lett
December 2024
Key Laboratory of Materials Physics of Ministry of Education, School of Physics, Zhengzhou University, Zhengzhou 450001, China.
Article Synopsis
- Researchers are focusing on manipulating elastic waves in lower-dimensional mechanical metamaterials to design miniaturized elastic devices, but controlling these waves in higher dimensions is challenging.
- This study introduces a structural parameter to explore on-chip Weyl physics in a silicon-on-insulator system, creating an in-plane pseudomagnetic field that facilitates robust energy transport through chiral Landau levels.
- Unique boundary states are observed near the corners, differing from traditional topological states, and the use of synthetic dimensions allows for advanced multidimensional elastic wave manipulation and exploration of higher-dimensional physics on integrated platforms.
Path integral Monte Carlo in a discrete variable representation with Gibbs sampling: Dipolar planar rotor chain.
J Chem Phys
January 2025
Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
In this work, we propose a path integral Monte Carlo approach based on discretized continuous degrees of freedom and rejection-free Gibbs sampling. The ground state properties of a chain of planar rotors with dipole-dipole interactions are used to illustrate the approach. Energetic and structural properties are computed and compared to exact diagonalization and numerical matrix multiplication for N ≤ 3 to assess the systematic Trotter factorization error convergence.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!