differential-accessibility-interpretation

differential-accessibility-interpretation

Summary

Interpret differentially accessible peaks from single-cell ATAC-seq data by applying motif enrichment analysis to identify overrepresented transcription factor binding motifs in peak regions that distinguish cell types or conditions. This skill bridges differential testing results to regulatory mechanism discovery.

When to use

After identifying differentially accessible peaks (via tl.diff_test) between cell populations or conditions in single-cell ATAC-seq data, use this skill to uncover which transcription factors may be driving the observed chromatin accessibility differences and to validate that differential regions are biologically meaningful and not artifacts.

When NOT to use

• Input is a count matrix or raw accessibility matrix rather than a pre-computed set of differentially accessible peaks with significance values.
• Downstream goal is protein-level validation or regulatory network inference without intermediate motif interpretation.
• Peak set is too small (<100 peaks) or filtered by arbitrary thresholds rather than statistical testing; enrichment statistics become unreliable.

Inputs

• differentially accessible peak set with p-values and test statistics (output from tl.diff_test)
• CIS-BP motif database with position-weight matrices (datasets.cis_bp)
• genome reference for background peak set or accessibility model

Outputs

• motif enrichment table with columns: motif ID, motif name, enrichment score, p-value, adjusted p-value
• ranked list of overrepresented transcription factor binding motifs
• statistical significance metrics for each motif

How to apply

Load the set of differentially accessible peaks (output from tl.diff_test, typically with p-values and fold-change statistics) as a feature set into SnapATAC2. Retrieve motif definitions and position-weight matrices from the CIS-BP motif database via datasets.cis_bp. Invoke tl.motif_enrichment on the peak set, which scans for motif occurrences within differential regions and computes enrichment statistics (e.g., enrichment scores, p-values) against a background model derived from accessible chromatin. Retrieve the resulting motif enrichment table and validate that all required columns (motif IDs, enrichment scores, p-values) are present and non-null, with p-values reflecting statistical significance of motif overrepresentation. Sort by adjusted p-value or enrichment score to prioritize candidate regulatory factors for downstream validation.