Deep scSTAR: Leveraging Deep Learning for the Extraction and Enhancement of Phenotype-Associated Features from Single-Cell RNA Sequencing and Spatial Transcriptomics Data

Information Science, Artificial Intelligence, Medicine, Life Sciences, Cell Biology, Immunology,
Single-cell RNA sequencing   Spatial transcriptomics   Deep learning   Phenotype-associated features   Tumor microenvironment  

In recent years, cutting-edge technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have greatly advanced the development of life sciences and clinical medicine. These technologies have revealed cellular heterogeneity and brought novel insights into major fields such as disease, development, and immunity. Howe...

Benchmarking Copy Number Aberrations Inference Tools Using Single-Cell Multi-Omics Datasets

Information Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Cell Biology, Bioengineering, Genetics,
Single-cell RNA sequencing   Copy number aberrations   Tumor heterogeneity   Benchmarking   Multi-omics  

1. Research Background and Significance In the fields of oncology and genomics, chromosomal copy number alterations (CNAs) are a key type of genetic variation driving the occurrence and progression of cancer. CNAs not only determine tumor heterogeneity but also play a crucial role in early tumor detection, subclone evolution analysis, research on d...

Contrastive Mapping Learning for Spatial Reconstruction of Single-Cell RNA Sequencing Data

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Bioengineering,
Single-cell RNA sequencing   Spatial transcriptomics   Contrastive learning   Cell correspondence   Gene expression  

Single-cell RNA sequencing (scRNA-seq) technology enables high-throughput transcriptomic profiling at single-cell resolution, significantly advancing research in cell biology. However, a notable limitation of scRNA-seq is that it requires tissue dissociation, resulting in the loss of the original spatial location information of cells within tissues...

ACImpute: A Constraint-Enhancing Smooth-Based Approach for Imputing Single-Cell RNA Sequencing Data

Information Science, Computer Science, Life Sciences, Bioengineering,
Single-cell RNA sequencing   Data imputation   Constraint-enhancing smooth   Cluster analysis   Trajectory inference  

Single-cell RNA sequencing (scRNA-seq) technology has been widely applied in biological and medical research in recent years. It can reveal the transcriptomic information of individual cells, helping scientists better understand cellular heterogeneity and complexity. However, a common issue in scRNA-seq data is “dropout events,” which result in man...

Organ-Specific Microenvironments Drive Divergent T Cell Evolution in Acute Graft-Versus-Host Disease

Medicine, Rheumatology and Immunology, Hepatobiliary System, Respiratory System, Gastroenterology, Hematology, Life Sciences, Immunology,
Graft-versus-host disease   T cell evolution   Organ microenvironment   Immunopathology   Single-cell RNA sequencing  

Study on Organ-Specific T Cell Differentiation in Acute Graft-Versus-Host Disease (AGVHD) Academic Background Acute Graft-Versus-Host Disease (AGVHD) is a common and severe complication following Allogeneic Hematopoietic Stem Cell Transplantation (HCT), caused by the immune attack of donor T cells on host tissues. Although the pathological mechanis...