scientific-scatac-signac

Run the scatac pipeline by providing a fragment file and an optional RNA h5ad file. The pipeline executes end-to-end scATAC-seq analysis using Signac, covering data preprocessing, peak calling, motif enrichment, and gene activity scoring.

Yes, you can integrate scRNA-seq data with scATAC-seq for multimodal analysis. Provide a matching scRNA-seq dataset to cluster cells, interpret regulatory programs, and map regulatory relationships to gene expression using WNN-like integration.

To calculate gene activity scores from chromatin accessibility data, use the Signac-based pipeline. It derives gene activity scores from accessible regions to enable multimodal interpretation and combine them with scRNA-seq data.

Yes, the scATAC-seq pipeline supports peak calling and motif enrichment. It identifies accessible regions and enriches transcription factor motifs as core features of the Signac-based workflow.

Yes, you can use SnapATAC2 and episcanpy alongside Signac. The pipeline uses these tools to preprocess data, call peaks, analyze motif enrichment, and compute gene activity scores in diverse organisms and genomes.

scATAC-seq data preprocessing and QC involve normalizing, filtering, and preparing data for downstream analysis. The pipeline handles these steps to ensure data quality before peak calling and motif analysis.