ArchR

ArchR performs processing and analysis of single-cell ATAC-seq data to characterize chromatin accessibility, identify regulatory DNA elements and transcription factor binding, and link chromatin features to gene activity.

Key Features:

• Doublet Removal: Efficiently identifies and removes doublets to preserve single-cell resolution.
• Single-Cell Clustering and Cell Type Identification: Performs clustering to identify cell types based on chromatin accessibility profiles.
• Robust Peak Set Generation: Generates reproducible peak sets for downstream analyses.
• Cellular Trajectory Identification: Infers developmental trajectories and lineage relationships from accessibility data.
• DNA Element to Gene Linkage: Links accessible DNA elements to putative target genes to elucidate regulatory relationships.
• Transcription Factor Footprinting: Detects transcription factor binding sites via footprinting within accessible chromatin.
• mRNA Expression Level Prediction from Chromatin Accessibility: Predicts mRNA expression levels from chromatin accessibility measurements.
• Multi-Omic Integration with scRNA-seq: Integrates single-cell ATAC-seq with scRNA-seq for joint analysis of regulatory state and gene expression.
• Scalability: Processes large datasets, demonstrated on over 1.2 million single cells in approximately eight hours on a standard Unix laptop.

Scientific Applications:

• Cell Type Identification: Classifies cell types using chromatin accessibility signatures.
• Trajectory and Lineage Reconstruction: Reconstructs developmental pathways and lineage relationships from single-cell accessibility data.
• Regulatory Element–Gene Linking: Maps regulatory DNA elements to target genes to study gene regulation mechanisms.
• Transcription Factor Binding Mapping: Identifies transcription factor binding sites within accessible chromatin.
• Expression Prediction: Infers mRNA expression levels from chromatin accessibility to connect epigenetic state with transcriptional output.
• Multi-Omic Integration: Combines ATAC-seq and scRNA-seq data for integrated analyses of regulation and expression.
• Large-Scale Regulatory Landscape Profiling: Enables profiling of regulatory chromatin landscapes across diverse biological contexts at single-cell resolution.

Methodology:

Explicitly stated computational methods include doublet identification and removal, single-cell clustering, peak set generation, trajectory inference, DNA element-to-gene linkage, transcription factor footprinting, prediction of mRNA expression from accessibility, and integration with scRNA-seq.