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http://dx.doi.org/10.1186/s12872-024-04172-8 | DOI Listing |
NAR Genom Bioinform
September 2025
[This corrects the article DOI: 10.1093/nar/lqaf063.].
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Biostatistics, Yale School of Public Health, New Haven, 06511, CT, US.
Semi-supervised methods for single-cell RNA-seq integration promise to improve batch correction and biological signal preservation by leveraging cell-type labels. However, their reported benefits often rely on overly idealized settings. Here, we present the first systematic benchmark of five leading semi-supervised methods (scANVI, scGEN, ssSTACAS, scDREAMER, ItClust) against five widely used unsupervised baselines across six diverse datasets.
View Article and Find Full Text PDFFront Immunol
August 2025
The First Hospital of Anhui University of Science and Technology (Huainan First People's Hospital), Huainan, Anhui, China.
[This corrects the article DOI: 10.3389/fimmu.2025.
View Article and Find Full Text PDFCNS Neurol Disord Drug Targets
September 2025
Department of Pharmacy, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China.
Introduction: Alzheimer's disease (AD) lacks effective biomarkers and diseasemodifying therapies. This study explored transcriptomic dysregulation, immune-metabolic crosstalk, and drug repurposing opportunities in AD.
Methods: Transcriptomic datasets (GSE109887, GSE5281) were harmonized using batch correction.
Bioinformatics
September 2025
Institutional Research Core Program-Biological Data Science Core, University of Alabama at Birmingham, Birmingham, AL United States.
Motivation: Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement.
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