- 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
- Installation Instructions for Seurat - Satija Lab
For versions of Seurat older than those not hosted on CRAN (versions 1 3 0 and 1 4 0), please download the packaged source code from our releases page and install from the tarball
- 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
- Analysis, visualization, and integration of spatial datasets with Seurat
While the analytical pipelines are similar to the Seurat workflow for single-cell RNA-seq analysis, we introduce updated interaction and visualization tools, with a particular emphasis on the integration of spatial and molecular information
- Seurat - Guided Clustering Tutorial - Satija Lab
Seurat offers several non-linear dimensional reduction techniques, such as tSNE and UMAP, to visualize and explore these datasets The goal of these algorithms is to learn underlying structure in the dataset, in order to place similar cells together in low-dimensional space
- Installation Instructions for Seurat - Satija Lab
Seurat does not require, but makes use of, packages developed by other labs that can substantially enhance speed and performance These include presto (Korunsky Raychaudhari labs), BPCells (Greenleaf Lab), and glmGamPoi (Huber Lab)
- Integrative analysis in Seurat v5 - Satija Lab
In Seurat v5, we introduce more flexible and streamlined infrastructure to run different integration algorithms with a single line of code This makes it easier to explore the results of different integration methods, and to compare these results to a workflow that excludes integration steps
- Using Seurat with multimodal data - Satija Lab
We have designed Seurat to enable for the seamless storage, analysis, and exploration of diverse multimodal single-cell datasets In this vignette, we present an introductory workflow for creating a multimodal Seurat object and performing an initial analysis
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