"""
scRBP — Single-cell RNA Binding Protein regulon inference

scRBP is a computational framework that reconstructs RNA-binding protein (RBP)-mediated
regulatory networks (regulons) from single-cell transcriptomes, integrating motif-binding
evidence and polygenic signals from genome-wide association studies (GWAS) to prioritize
trait-associated RBP programs across diverse cellular contexts.

The pipeline comprises ten steps:
  1. getSketch      – Stratified GeoSketch cell downsampling
  2. getGRN         – GRNBoost2/GENIE3 RBP → Gene/Isoform network inference
  3. getMerge_GRN   – Consensus network construction from multi-seed GRN runs
  4. getModule      – Regulon candidate extraction (Top-N / percentile strategies)
  5. getPrune       – Rank-based motif-enrichment pruning of RBP-target modules
  6. getRegulon     – Export high-confidence regulons to GMT format
  7. mergeRegulons  – Merge region-specific GMT files across transcript regions
  8. ras            – Regulon Activity Score (RAS) computation via AUCell
  9. rgs            – Regulon-level Genetic Association Score (RGS) via MAGMA
  10. trs           – Trait Relevance Score (TRS) integrating RAS and RGS

Reference:
    Ma Y. et al. Decoding disease-associated RNA-binding protein-mediated regulatory
    networks through polygenic enrichment across diverse cellular contexts. (2026)
"""

__version__ = "0.1.3.3"
__author__ = "Yunlong Ma et al., University of Pennsylvania"
__email__ = ""
__description__ = "Single-cell RNA Binding Protein regulon inference"

from scrbp.commands.get_sketch import stratified_geosketch
from scrbp.commands.merge_regulons import scRBP_mergeRegulons

__all__ = [
    "__version__",
    "__author__",
    "stratified_geosketch",
    "scRBP_mergeRegulons",
]
