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Seurat(v4)官方教程 | Introduction to scRNA-seq integration
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Seurat v4 包含一组方法，用于跨数据集匹配 (或"对齐")共享的细胞群。 这些方法首先识别处于匹配生物状态的交叉数据集细胞 ("锚")，可以用于纠正数据集之间的技术差异 (即批效应校正)，并在不同实验条件下执行比较scRNA-seq分析。
Introductory Vignettes For new users of Seurat, we suggest starting with a guided walk through of a dataset of 2,700 Peripheral Blood Mononuclear Cells (PBMCs) made publicly available by 10X Genomics. This tutorial implements the major components of a standard unsupervised clustering workflow including QC and data filtration, calculation of high-variance genes, dimensional reduction, graph ...
As v5 is still in beta, the CRAN installation install.packages ("Seurat") will continue to install Seurat v4, but users can opt-in to test Seurat v5 by following the instructions in our INSTALL page.
Updates with Seurat v5 Seurat v5 introduced the following new features: Integrative multi-modal analysis with bridge integration 'Sketch'-based analysis of large data sets methods for spatial transcriptomics assay layers You can read about major changes between Seurat v5 and v4 here. How much R programming do I need to know to use Seurat?
**Seurat v4**包含一组方法，用于跨数据集匹配 (或"对齐")共享的细胞群。 这些方法首先识别处于匹配生物状态的交叉数据集细胞 ("锚")，可以用于纠正数据集之间的技术差异 (即批效应校正)，并在不同实验条件下执行比较scRNA-seq分析。
什么是Seurat？ Seurat是R语言中被用于单细胞RNA-seq质控、分析的一个r包。从而时用户可以鉴定来自单细胞转录本测定的异质性来源。 其中，Seurat包的应用主要包含了：数据导入、数据过滤、数据归一化、特征选择、…
1.1 Introduction The aim of these materials is to demonstrate how to use the Seurat R package to process scRNA-seq data. They have been designed as a supplement to the Introduction to Single-cell RNA-seq Analysis course developed by University of Cambridge/CRUK. Here we use the same dataset and follow the same general steps, but using Seurat as an alternative to the Bioconductor packages used ...
单细胞RNA测序 (scRNA-seq)已经成为了被广泛使用的技术，能够让研究人员在单细胞水平测得基因表达图谱和研究分子生物学机制。
Introduction SEURAT is a software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data.
In Seurat v5, we introduce flexible and diverse support for a wide variety of spatially resolved data types, and support for analytical techniqiues for scRNA-seq integration, deconvolution, and niche identification. Vignette: Analysis of spatial datasets (Sequencing-based) Vignette: Analysis of spatial datasets (Imaging-based)