APEC

APEC performs clustering and analysis of genome-wide chromatin accessibility at single-cell resolution to characterize epigenomic heterogeneity, predict gene expression, identify regulatory motifs and super enhancers, and infer pseudotime trajectories.

Key Features:

• Epigenomic Clustering: Classifies individual cells using "accessons," which are clusters of accessible regions exhibiting synergistic signal patterns for unsupervised single-cell clustering.
• Implementation and Integration: Implemented in Python and integrates with other analytical tools.
• Gene Expression Prediction: Predicts gene expression from chromatin accessibility patterns.
• Motif Enrichment Analysis: Identifies differentially enriched transcription factor motifs from accessibility data.
• Super Enhancer Discovery: Detects super enhancers as large clusters of enhancers associated with key regulatory genes.
• Pseudotime Trajectory Projection: Projects pseudotime trajectories to infer developmental progression from epigenomic states.

Scientific Applications:

• Per-cell regulome dynamics in mouse thymocytes (ftATAC-seq): Applied to fluorescent tagmentation-based single-cell ATAC-seq (ftATAC-seq) data from mouse thymocytes to reveal epigenomic heterogeneity, characterize developmental trajectories, and predict regulators of maturation.

Methodology:

Clusters cells by assembling "accessons" from genome-wide chromatin accessibility patterns, predicts gene expression, performs motif enrichment analysis, identifies super enhancers, and projects pseudotime trajectories; implemented in Python.