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  • Seurat Extension Packages - Satija Lab
    Signac Signac is an R toolkit that extends Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets The software supports the following features:
  • Integrating scRNA-seq and scATAC-seq data • Seurat
    This vignette makes extensive use of the Signac package, recently developed for the analysis of chromatin datasets collected at single-cell resolution, including scATAC-seq Please see the Signac website for additional vignettes and documentation for analyzing scATAC-seq data
  • PBMC scATAC-seq Vignette - Satija Lab
    Update February 2020: we now have developed a separate package, Signac, for the analysis and integration of scATAC-seq data See the Signac website for up-to-date vignettes and documentation for analysing scATAC-seq data In this vignette, we demonstrate our new data transfer method in the context of scATAC-seq to Classify cells measured with scATAC-seq based on clustering results from scRNA
  • Using Seurat with multimodal data - Satija Lab
    In this vignette, we present an introductory workflow for creating a multimodal Seurat object and performing an initial analysis For example, we demonstrate how to cluster a CITE-seq dataset on the basis of the measured cellular transcriptomes, and subsequently discover cell surface proteins that are enriched in each cluster We note that Seurat also enables more advanced techniques for the
  • Dictionary Learning for cross-modality integration • Seurat
    Load the bridge, query, and reference datasets We start by loading a 10x multiome dataset, consisting of ~12,000 PBMC from a healthy donor The dataset measures RNA-seq and ATAC-seq in the same cell, and is available for download from 10x Genomics here We follow the loading instructions from the Signac package vignettes Note that when using Signac, please make sure you are using the latest
  • Installation Instructions for Seurat - Satija Lab
    Signac: analysis of single-cell chromatin data SeuratData: automatically load datasets pre-packaged as Seurat objects Azimuth: local annotation of scRNA-seq and scATAC-seq queries across multiple organs and tissues SeuratWrappers: enables use of additional integration and differential expression methods
  • Weighted Nearest Neighbor Analysis • Seurat - Satija Lab
    As described in the Signac motifs vignette, there are a few ways to do this, but we will use the chromVAR package from the Greenleaf lab This calculates a per-cell accessibility score for known motifs, and adds these scores as a third assay (chromvar) in the Seurat object
  • Installation Instructions for Seurat - Satija Lab
    SeuratData: automatically load datasets pre-packaged as Seurat objects Azimuth: local annotation of scRNA-seq and scATAC-seq queries across multiple organs and tissues SeuratWrappers: enables use of additional integration and differential expression methods Signac: analysis of single-cell chromatin data
  • Gene expression markers for all identity classes
    Finds markers (differentially expressed genes) for each of the identity classes in a dataset
  • Gene expression markers of identity classes — FindMarkers
    Finds markers (differentially expressed genes) for identity classes





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