Seurat老用户上手sclet：还用 PBMC3k 跑通单细胞基础分析全流程
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TL;DR - A hands-on tutorial ports the standard PBMC3k single-cell RNA-seq workflow from Seurat to sclet. It shows that sclet offers familiar APIs while using Bioconductor’s SingleCellExperiment data model.
- Covers QC, normalization, variable-feature selection, PCA, clustering, UMAP, marker detection, and annotation.
- The workflow retained 2,638 cells, identified nine clusters, and recovered canonical PBMC markers.
- sclet stores assays as layers and automatically uses
logcountsrather than scaled data for marker testing and SingleR. - Manual marker-based annotation remains primary; SingleR is presented as supporting evidence, with 31 low-confidence cells pruned.
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Seurat老用户上手sclet：还用 PBMC3k 跑通单细胞基础分析全流程
Public signals
N/A
TL;DR - A hands-on tutorial ports the standard PBMC3k single-cell RNA-seq workflow from Seurat to sclet. It shows that sclet offers familiar APIs while using Bioconductor’s SingleCellExperiment data model.
- Covers QC, normalization, variable-feature selection, PCA, clustering, UMAP, marker detection, and annotation.
- The workflow retained 2,638 cells, identified nine clusters, and recovered canonical PBMC markers.
- sclet stores assays as layers and automatically uses
logcountsrather than scaled data for marker testing and SingleR. - Manual marker-based annotation remains primary; SingleR is presented as supporting evidence, with 31 low-confidence cells pruned.