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  • Tools for Single Cell Genomics • Seurat - Satija Lab
    Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data
  • Georges Seurat - Wikipedia
    Seurat's artistic personality combined qualities that are usually thought of as opposed and incompatible: on the one hand, his extreme and delicate sensibility, on the other, a passion for logical abstraction and an almost mathematical precision of mind [7]
  • CRAN: Package Seurat
    A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data
  • Seurat - 上海交大超算平台用户手册 - SJTU
    Seurat 简介 Seurat是由 New York Genome Center, Satija Lab 开发的单细胞数据分析集成软件包。 其功能不仅包含基本的数据分析流程，如质控，细胞筛选，细胞类型鉴定，特征基因选择，差异表达分析，数据可视化等。
  • Introduction to scRNA-Seq with R (Seurat) - Cancer
    Seurat, brought to you by the Satija lab, is a kind of one-stop shop for single cell transcriptomic analysis (scRNA-seq, multi-modal data, and spatial transcriptomics)
  • Seurat package - RDocumentation
    A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data
  • Seurat: Tools for Single Cell Genomics
    'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data
  • Seurat: Tools for Single Cell Genomics — Seurat-package
    A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data





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