signac

name	signac
domain	compbio
description	R package for chromatin analysis. Use for single-cell ATAC-seq analysis, chromatin accessibility, and integration with Seurat.
license	MIT license
metadata	{"skill-author":"Eric Yiru"}

Signac: Chromatin Analysis in R

Overview

Signac is an R package for analyzing single-cell chromatin data, particularly scATAC-seq. It integrates seamlessly with Seurat for combined analysis of gene expression and chromatin accessibility.

When to Use This Skill

This skill should be used when:

• Analyzing single-cell ATAC-seq data
• Performing chromatin accessibility analysis
• Creating gene activity matrices from ATAC
• Integrating with Seurat for multi-modal analysis
• Finding cell type-specific regulatory elements

Quick Start

Installation

# Install Signac
install.packages("Signac")

# From GitHub
devtools::install_github("satijalab/Signac")

Basic Setup

library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(ggplot2)

Creating Objects

From Fragments

# Create Seurat object from fragments
counts <- Read10X_h5(filename = "filtered_peak_bc_matrix.h5")
fragfile <- "atac_v1_pbmc_10k.fragments.tsv.gz"

# Create object
chrom_assay <- CreateChromatinAssay(
  counts = counts,
  fragments = fragfile,
  min.cells = 10,
  min.features = 200
)

# Create Seurat object
pbmc <- CreateSeuratObject(
  counts = chrom_assay,
  assay = "ATAC"
)

Analysis Workflow

Peak Information

# Add peak information
Annotation(pbmc) <- annotations

# Compute nucleosome signal
pbmc <- NucleosomeSignal(pbmc)

# Compute TSS enrichment
pbmc <- TSSEnrichment(pbmc, fast = FALSE)

Dimensionality Reduction

# Feature selection
pbmc <- FindTopFeatures(pbmc, min.cells = 10)

# Run TF-IDF
pbmc <- RunTFIDF(pbmc)

# Run SVD
pbmc <- FindSVD(pbmc, assay = "ATAC")

Clustering

# Find neighbors
pbmc <- FindNeighbors(pbmc, reduction = "lsi", dims = 2:50)

# Find clusters
pbmc <- FindClusters(pbmc, resolution = 0.4)

# Run UMAP
pbmc <- RunUMAP(pbmc, reduction = "lsi", dims = 2:50)

# Visualize
DimPlot(pbmc, label = TRUE)

Gene Activity

Create Gene Activity Matrix

# Create gene activity matrix
pbmc <- GeneActivity(
  pbmc,
  assay = "ATAC"
)

# Add to object
pbmc[["RNA"]] <- CreateAssayObject(counts = pbmc@assays$GeneActivity)
pbmc <- NormalizeData(pbmc, assay = "RNA")

Visualization

# Plot gene activity
FeaturePlot(
  pbmc,
  features = c("MS4A1", "CD3D"),
  assay = "RNA"
)

Integration with RNA

Multi-Modal Integration

# Load RNA data
rna <- CreateSeuratObject(counts = rna_counts, assay = "RNA")

# Find anchors
anchors <- FindIntegrationAnchors(
  object.list = list(rna = rna, atac = pbmc),
  assay = c("RNA", "ATAC"),
  reduction = "cca"
)

# Integrate
combined <- IntegrateData(
  anchorset = anchors,
  assay = c("RNA", "ATAC")
)

# Analysis
DefaultAssay(combined) <- "integrated"
combined <- ScaleData(combined)
combined <- RunPCA(combined)
combined <- RunUMAP(combined)

Peak Analysis

Find Markers

# Find differential peaks
da_peaks <- FindAllMarkers(
  pbmc,
  assay = "ATAC",
  test.use = "chisq"
)

Coverage Plots

# Plot accessibility around genes
CoveragePlot(
  pbmc,
  region = "CD4",
  extend.upstream = 1000,
  extend.downstream = 1000
)

Key Parameters

CreateChromatinAssay

• counts: Peak-by-cell matrix
• fragments: Fragment file path
• min.cells: Minimum cells per peak
• min.features: Minimum peaks per cell

Best Practices

1. Filter quality cells: Check nucleosome signal and TSS enrichment
2. Use correct genome: Ensure annotation matches
3. Peak calling: Use appropriate peak caller first (MACS2)

Additional Resources

• GitHub: https://github.com/satijalab/Signac
• Tutorials: https://satijalab.org/seurat/articles/atac_vignette.html
• Related: Seurat, ArchR