pycistarget-motif-enrichment

Motif enrichment on scATAC-seq region sets is performed using pycistarget to identify transcription factor binding sites and derive cistromes. You apply cisTarget and DEM methods with precomputed databases to compute enrichments from region sets derived from pycisTopic.

The cisTarget method computes motif enrichment using NES-based scoring, while DEM uses a Wilcoxon-based approach. Both methods are available in pycistarget to process scATAC-seq region sets and generate distinct per-TF region associations.

Yes, precomputed databases are required to run motif enrichment for SCENIC+ analysis. The pycistarget workflow requires these databases along with configured species and annotations to ensure reproducible cistrome extraction results.

Per-TF region associations are extracted from scATAC-seq topic or metacell sets by running pycistarget motif enrichment. This process generates TF-bound region sets, known as cistromes, which are structured for downstream SCENIC+ GRN inference.

Yes, pycistarget motif enrichment results are structured for standalone SCENIC+ workflows and Snakemake pipelines. The outputs provide explicit database and annotation guidelines suitable for seamless downstream regulatory network integration.

To define scATAC cistromes using pycistarget, you need scATAC-seq region sets derived from pycisTopic, precomputed motif databases, and configured species annotations. These inputs ensure accurate transcription factor cistrome extraction.