End-to-end analysis of the PBMC 3k single-cell RNA-seq dataset using Python and Scanpy, including preprocessing, UMAP visualization, Leiden clustering, cell type annotation, and marker gene identification.
Comparison of Seurat normalization workflows and manual vs. SeuratData cluster labeling on the PBMC3k dataset.
Annotated Seurat v5 walkthrough of the 10x Genomics PBMC 3k dataset: QC, normalization, feature selection, PCA, graph-based clustering, UMAP, marker discovery, and annotation of 2,638 cells into 8 immune populations. Each step documents parameter rationale and common failure modes. R 4.4.2 / Seurat 5.5.1.
An end-to-end scRNA-seq analysis pipeline processing raw PBMC data through QC, dimensionality reduction, and clustering, deployed as an interactive, production-ready R Shiny dashboard.
scGPT fine-tuning on PBMC3k · 7-method benchmark · Nature Methods 2024 framework
Reproducible benchmark of four scRNA-seq cell type annotation methods (SingleR, CellTypist, ACTINN, scANVI) on PBMC3K
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