Preprocessing Scmultiomicsgrn

As a bioinformatics tutor, I'll guide you through this complex pipeline step-by-step. This is a sophisticated tool for integrating ATAC-seq and scRNA-seq data to identify transcription factor binding sites (TFBS) and build gene regulatory networks.

Pipeline Overview

This pipeline performs multi-omics integration by:

Processing ATAC-seq data to identify accessible chromatin regions
Finding transcription factor binding sites using motif scanning
Building gene regulatory networks from scRNA-seq data
Integrating both modalities to create cross-modal networks

Let's break down each component:

Core Dependencies and Tools

External Bioinformatics Tools

# Required command-line tools
- perl (for sequence extraction)
- fimo (MEME suite - for motif scanning)
- sort-bed (BEDOPS - for genomic interval operations) 
- bedops (BEDOPS - for set operations on genomic intervals)
- docker (for MAESTRO analysis)

Python Libraries

# Core scientific computing
import numpy as np
import pandas as pd
from scipy import sparse as sp_sparse
from scipy import io as sio
from sklearn import metrics
 
# Bioinformatics specific
import h5py  # For 10X Genomics format
from tqdm import tqdm  # Progress bars
from arboreto.algo import genie3, grnboost2  # Gene regulatory network inference

Step-by-Step Breakdown

1. ATAC-seq Peak Filtering (`ATAC_filter_from_CSV`/`ATAC_filter_from_MTX`)

Purpose: Filter peaks based on accessibility across cells

def ATAC_filter_from_CSV(filename, threshold=0.1):
    # Read ATAC-seq count matrix
    # Calculate accessibility rate per peak: n_accessible_cells / total_cells  
    # Keep peaks with rate > threshold
    # Output BED format: chr start end accessibility_rate

Key Concept: Only keep peaks that are accessible in >10% of cells (reduces noise)

2. Sequence Extraction (`extract_chromosome`)

Purpose: Extract DNA sequences from filtered peaks

def extract_chromosome(bed_file, genome_file):
    # Uses Perl script GetSequence.pl
    # Input: BED file with genomic coordinates
    # Output: FASTA file with sequences

What happens: Converts genomic coordinates to actual DNA sequences for motif scanning

3. Motif Scanning with FIMO (`fimo`)

Purpose: Find transcription factor binding motifs in sequences

def fimo(fasta_file, threshold=1e-4, motif_dir):
    # For each TF motif in motif_dir:
    #   - Run FIMO to find matches in sequences
    #   - Filter by p-value threshold
    #   - Output binding site coordinates

Key Insight: Uses position weight matrices (PWMs) to predict where TFs might bind

4. TFBS Region Processing (`get_TFBS_region`)

Purpose: Convert FIMO output to genomic coordinates

def get_TFBS_region(fimo_dir, threshold=1e-6):
    # Parse FIMO results
    # Convert relative positions to absolute genomic coordinates
    # Create BED files for each TF
    # Merge all TF binding sites

5. Promoter Region Integration (`get_TFBS_from_promoter`)

Purpose: Identify TF binding sites in gene promoter regions

def get_TFBS_from_promoter(promoter_file, motif_dir):
    # Filter promoter regions for TFs that have motifs
    # Create mapping between TFs and their target gene promoters

6. Overlap Analysis (`get_contained_region`)

Purpose: Find TFBS that overlap with TF promoter regions

def get_contained_region(atac_region_file, promoter_region_file):
    # Use BEDOPS to find overlapping regions
    # Identifies TF-TF regulatory relationships

Biological Significance: TFs regulating other TFs create regulatory cascades

7. Graph Construction (`build_Graph`)

Purpose: Build TF-TF regulatory network from overlaps

def build_Graph(atac_region_file, promoter_region_file):
    # Create adjacency matrix where A[i,j] = 1 if TF_i binds to TF_j's promoter
    # Output: TF-TF regulatory graph

8. ATAC Feature Extraction (`extract_atac_feature`)

Purpose: Calculate regulatory potential scores using MAESTRO

def extract_atac_feature(atac_file, node_file):
    # Convert to 10X format
    # Run MAESTRO in Docker container
    # Calculate TF regulatory potential scores
    # Output: TF activity scores per cell

MAESTRO: Integrates chromatin accessibility with TF motifs to predict TF activity

9. scRNA-seq Feature Extraction (`extract_scrna_feature2`)

Purpose: Infer gene regulatory networks from expression data

def extract_scrna_feature2(scrna_file, node_file, method="grnboost2"):
    # Use GRNBoost2 or GENIE3 algorithms
    # Infer TF-target relationships from co-expression
    # Output: TF-gene regulatory network weights

Algorithms:

GRNBoost2: Gradient boosting-based method (faster)
GENIE3: Random forest-based method (more established)

10. Cross-modal Integration (`build_cross_graph`)

Purpose: Integrate ATAC and scRNA features

def build_cross_graph(graph_file, atac_feature_file, scrna_feature_file):
    # Find intersection of TFs present in both modalities
    # Create unified feature matrices
    # Output: Integrated multi-omics network

Required External Files and Setup

1. Genome Reference Files

# Human genome (hg19/GRCh37)
genome_file = "hg19/index"  # FASTA format genome

2. TF Motif Database

motif_dir = "human_HOCOMO"  # Directory with .meme files
# Each file contains position weight matrices for TF motifs

3. Promoter Annotations

promoter_file = "gencode.v19.ProteinCoding_gene_promoter.txt"
# Format: chr start end strand gene_name

4. Docker Setup for MAESTRO

# Pull MAESTRO Docker image
docker pull winterdongqing/maestro:1.2.1

Data Input Formats

ATAC-seq Data

# CSV format: peaks × cells matrix
# Rows: genomic_regions (chr1_1000_2000)
# Columns: cell_barcodes
# Values: accessibility counts
 
# MTX format: Matrix Market format
# - matrix.mtx: sparse count matrix
# - barcodes.tsv: cell identifiers  
# - peaks.tsv: peak coordinates

scRNA-seq Data

# Similar formats as ATAC-seq but with gene expression
# Rows: genes
# Columns: cells
# Values: expression counts/UMI

Running the Complete Pipeline

def run(atac_file, scrna_file, save_dir):
    # 1. Extract TFBS from ATAC-seq peaks
    atac_region_file = get_TFBS_from_ATAC(atac_file, motif_dir, genome_file)
    
    # 2. Process promoter regions  
    promoter_region_file = get_TFBS_from_promoter(promoter_file)
    
    # 3. Build TF-TF regulatory graph
    graph_file, node_file = build_Graph(atac_region_file, promoter_region_file)
    
    # 4. Extract features from both modalities
    atac_feature_file = extract_atac_feature(atac_file, node_file)
    scrna_feature_file = extract_scrna_feature(scrna_file, node_file)
    
    # 5. Integrate modalities
    cross_graph_file, cross_node_file, cross_atac_file, cross_scrna_file = \
        build_cross_graph(graph_file, atac_feature_file, scrna_feature_file)

Key Concepts to Understand

1. Regulatory Potential Score

Combines chromatin accessibility with TF motif presence
Higher score = more likely the TF is active in that cell type

2. Gene Regulatory Networks (GRNs)

Directed graphs where edges represent regulatory relationships
Inferred from co-expression patterns in scRNA-seq data

3. Multi-omics Integration

Combines complementary information from different data types
ATAC-seq: where regulation can happen (accessibility)
scRNA-seq: where regulation does happen (expression changes)

4. Chromatin Accessibility

Open chromatin regions where TFs can bind
Measured by ATAC-seq (Assay for Transposase-Accessible Chromatin)

This pipeline is quite sophisticated and represents current best practices in computational genomics for multi-omics integration. Each step builds upon the previous one to create a comprehensive model of gene regulation.

Would you like me to dive deeper into any specific component or help you set up the required external tools?

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