archr

name	archr
domain	compbio
description	R package for single-cell ATAC-seq analysis. Use for chromatin accessibility analysis, peak calling, motif enrichment, and integration with gene expression.
license	MIT license
metadata	{"skill-author":"Eric Yiru"}

ArchR: Single-Cell ATAC-Seq Analysis

Overview

ArchR is a powerful R package for analyzing single-cell ATAC-seq (Assay for Transposase-Accessible Chromatin sequencing) data. It provides a complete workflow from raw reads to interpretation, including dimensionality reduction, clustering, peak calling, motif analysis, and integration with gene expression data.

When to Use This Skill

This skill should be used when:

• Analyzing single-cell ATAC-seq data
• Performing chromatin accessibility analysis
• Identifying cell types based on chromatin state
• Finding regulatory elements and TF motifs
• Integrating scATAC-seq with scRNA-seq
• Creating pseudo-bulk accessibility tracks

Quick Start

Installation

# Install ArchR
install.packages("ArchR")

# Or from GitHub
devtools::install_github("GreenleafLab/ArchR", ref="master")

# Install dependencies
install.packages("BSgenome.Hsapiens.UCSC.hg38")

Basic Setup

library(ArchR)
library(ggplot2)
library(dplyr)

# Set genome
addArchRGenome("hg38")

Creating ArchR Project

From Fragments

# Create input files
inputFiles <- c(
  "sample1" = "path/to/sample1.fragments.tsv.gz",
  "sample2" = "path/to/sample2.fragments.tsv.gz"
)

# Create Arrow files
ArrowFiles <- createArrowFiles(
  inputFiles = inputFiles,
  sampleNames = names(inputFiles),
  filterTSS = 4,          # Minimum TSS enrichment
  filterFrags = 1000,     # Minimum fragments
  addTileMat = TRUE,      # Add tile matrix
  addGeneScoreMat = TRUE  # Add gene score matrix
)

# Create ArchR project
proj <- ArchRProject(
  ArrowFiles = ArrowFiles,
  outputDirectory = "ArchR_Project"
)

Analysis Workflow

Dimensionality Reduction

# Add dimensionality reduction
proj <- addIterativeLSA(
  ArchRProj = proj,
  useMatrix = "TileMatrix",
  name = "IterativeLSI",
  clusterParams = list(
    resolution = c(0.2, 0.4, 0.8),
    sampleCells = 10000,
    n.start = 10
  )
)

Clustering

# Add clusters
proj <- addClusters(
  input = proj,
  reducedDims = "IterativeLSI",
  method = "Seurat",
  name = "Clusters",
  resolution = 0.8
)

UMAP

# Add UMAP
proj <- addUMAP(
  ArchRProj = proj,
  reducedDims = "IterativeLSI",
  name = "UMAP",
  minDist = 0.3,
  metric = "cosine"
)

# Plot
p1 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Clusters")

Marker Peaks

# Find marker peaks
markersPeaks <- getMarkerFeatures(
  ArchRProj = proj,
  useMatrix = "PeakMatrix",
  groupBy = "Clusters",
  bias = c("TSSEnrichment", "log10(nFrags)"),
  testMethod = "wilcoxon"
)

# View top markers
markerList <- getMarkers(markersPeaks, returnGRanges = TRUE)

Gene Scores

# Gene score track
p2 <- plotEmbedding(
  proj,
  colorBy = "GeneScoreMatrix",
  name = c("CD4", "CD8A"),
  embedding = "UMAP"
)

Peak Analysis

Peak Calling

# Add peak matrix
proj <- addPeaks(
  proj,
  groupBy = "Clusters",
  cutOff = 0.05,
  extend = -500
)

# Or with MACS2 (requires installation)
proj <- addPeaks(
  proj,
  groupBy = "Clusters",
  method = "macs2",
  macs2Path = "path/to/macs2"
)

Peak Annotation

# Annotate peaks
proj <- addPeakAnnotations(
  proj,
  resources = ArchRResources,
  names = "Motifs",
  analysis = "motifEnrichment"
)

Integration

With scRNA-seq

# Create gene expression matrix from ATAC
# (in absence of scRNA-seq)
proj <- addGeneIntegrationMatrix(
  ArchRProj = proj,
  useMatrix = "GeneScoreMatrix",
  nameX = "predictedRNA",
  nameY = "RNA",
  reduceDims = "IterativeLSI"
)

Cross-modality

# Integration with Seurat
library(Seurat)

# Export ArchR to Seurat
atac <- ArchRtoSeurat(proj)

# Standard Seurat integration
atac <- RunPCA(atac)
atac <- RunUMAP(atac, dims = 1:30)
atac <- FindNeighbors(atac)
atac <- FindClusters(atac)

Visualization

Browser Tracks

# Export to IGV
exportToBrowser(
  ArchRProj = proj,
  exportAs = "bedGraph",
  peaks = TRUE,
  folder = "Browser"
)

Heatmaps

# Marker peak heatmap
heatmapPeaks <- markerHeatmap(
  markersPeaks,
  cutOff = 5,
  nLabel = 10
)

Key Parameters

createArrowFiles

• filterTSS: Minimum TSS enrichment (default: 4)
• filterFrags: Minimum fragment count (default: 1000)
• addTileMat: Add tile matrix (default: TRUE)
• addGeneScoreMat: Add gene score matrix (default: TRUE)

Best Practices

1. Quality control: Check TSS enrichment and fragment counts
2. Filter properly: Adjust thresholds based on data quality
3. Peak calling: Use sufficient cell numbers per group
4. Motif analysis: Requires appropriate genome

Additional Resources

• ArchR paper: https://www.nature.com/articles/s41587-021-00993-w
• GitHub: https://github.com/GreenleafLab/ArchR
• Tutorials: https://greenleaflab.github.io/ArchR_2020/