2024年11月29日 · Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an...

2025年2月6日 · CD8 T cells were captured by SCT-dextramers from 62 HLA-matched COVID-19 participants for scRNAseq and TCR sequencing. Gene expression UMAP (GEX-UMAP) was generated based on scRNAseq.

Eight major cell clusters were identified, including CD4+ T cells (CD3D+CD4+), CD8+ T cells (CD3D+CD8A+), γδ T cells (CD3D+TRDC+), NKT-like cells (CD3E+KLRF1+), NK cells (GNLY+NKG7+), B cells...

2024年12月20日 · The uniform manifold approximation and projection (UMAP) representation of the Leiden clusters stratified memory CD8 + T cells primarily according to tissue of origin (Figures 6B and 6C). Yet, cells from each tissue were partitioned into several distinct clusters.

UMAP dimensionality reduction of CD8⁺T cells. a UMAP dimensionality reduction of lung CD8⁺T cells isolated from naive (n = 3) and PbAluc-infected (n = 3) mice at 7 dpi was color-coded...

2021年6月3日 · By carrying out a unified analysis of over 300 assay for transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-seq) experiments from 12 studies of CD8 T cells in cancer and infection, we defined a shared differentiation trajectory toward dysfunction and its underlying transcriptional drivers and revealed a universal earl...

2024年11月29日 · Using this tool, we established a comprehensive, million-scale, pan-disease human CD8 + T cell reference atlas, which incorporates information on transcriptomes and the diversity of the αβ T cell...

Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8 + T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell recepto...

2023年6月20日 · We further visualized the data and clustered it using the T-distribution stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) methods. We identified subpopulations of CD8 + T cells by examining the expression of canonical markers, including Cd3d, Cd3e, Cd4 , and Cd8a , to separate CD8 + T cells from ...

Uniform Manifold Approximation and Projection (UMAP) analysis was performed using Seurat’s built-in function RunUMAP and the umap-learn algorithm, in addition to the Leiden algorithm. To visualize the results, we utilized dimplot, featureplot, violin, and dotplot.