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pySCENIC ctx auc Specifications

Process Details

  • Name: pySCENIC ctx auc
  • Process UUID: 3de0ead0574e4edea0757ad035349916
  • Process Group: SingleCell

Overview

This process performs gene regulatory network inference and activity scoring using pySCENIC (Python Single-Cell rEgulatory Network Inference and Clustering). It identifies transcriptional regulons from single-cell expression data and calculates their activity scores across individual cells using the AUCell algorithm.

This process is implemented in Bash, which invokes Python scripts for integrating pySCENIC outputs and generating comprehensive results.

Key Functionality

  • Regulon Inference: Uses the ctx module to identify co-expressed gene modules and prune them using cis-regulatory motif analysis
  • Activity Scoring: Calculates Area Under the Curve (AUC) scores for each regulon in individual cells using the AUCell algorithm
  • Output Integration: Combines pySCENIC results with original expression data and exports activity matrices in multiple formats

Input/Output Specification

Inputs

Required Inputs

  • csvFile

    • Description: Gene expression matrix or metadata file in CSV format
    • Format: CSV

  • inputDir

    • Description: Directory containing database files and reference data for regulon inference
    • Format: Directory

  • inputFileTxt

    • Description: Text file containing additional configuration or gene list information
    • Format: TXT

  • outputFileOut

    • Description: Output specification file defining the analysis parameters
    • Format: OUT

Outputs

  • zip_file

    • Description: Compressed archive containing adjacencies matrix, regulons list, and AUCell activity matrix
    • Format: ZIP

  • loom

    • Description: Integrated loom file containing original expression data combined with regulon activity scores
    • Format: LOOM

Parameters & Settings

These parameters can be adjusted in the Foundry UI when running this process.

  • Mask Dropouts

    • Description: Using this option excludes zero-expression cells when calculating TF-target correlations, focusing only on cells where both genes are expressed. This affects how the correlation between TF and target genes is calculated
    • Available options: false (default), true

  • AUC Threshold

    • Description: Fraction of the ranked gene list in each cell that is used when computing the AUC (Area Under the Curve) score for each regulon
    • Default value: 0.05

References & Resources

  • Tool Documentation: Contact the team for details on integrate_pyscenic_output.py
  • Related Papers: Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nature Methods 14, 1083–1086 (2017). https://doi.org/10.1038/nmeth.4463