InPheRNo-ChIP: Inference of Phenotype-relevant Regulatory Networks with ChIP-seq Integration

InPheRNo-ChIP is a computational framework designed to infer phenotype-relevant gene regulatory networks (GRNs) by integrating RNA-seq and ChIP-seq data.

Running InPheC

Sequential Execution:

• Execute the scripts InPheC_step1.py, InPheC_step2.py, and InPheC_step3.py in sequence. Each script builds on the outputs from its predecessor, so complete each step before proceeding.

Replicating Published Results:

• The final inferred GRN relevant to hESC-to-dEN differentiation as analyzed in the manuscript can be found at ./step3/RnCnP_vary-Tp0.0909_nc4_ni4000_nt2000_nb400_minMax.csv.
• To replicate this GRN without the variability introduced by the Elastic Net in InPheC_step1.py, begin with step1 results in ./step1 and proceed with only InPheC_step2.py and InPheC_step3.py.

Quick Start

To begin using InPheRNo-ChIP, ensure you have the following prerequisites:

• Python 3.11.5: The software is developed and tested with Python 3.11.5.
• Conda Environment: For efficient management of dependencies, using Conda is recommended. An InPheC_Env.yml file is included in the repository to help set up a virtual environment with all required packages.

Inputs and Outputs

• Inputs: Files in the "./Data" folder.
• Outputs: Final GRN file is named with prefix RnCnP_ in the "./step3" directory.

Setting Up and Running InPheRNo-ChIP: Please follow these steps to clone the repository, set up the environment, and run the pipeline.

# Env set up
git clone https://github.com/Emad-COMBINE-lab/InPheRNo-ChIP.git
cd InPheRNo-ChIP
conda env create -f InpheC_0820.yml
conda activate inpheC

# Run the pipeline steps
python InPheC_Step1.py  
python InPheC_Step2.py  
python InPheC_Step3.py  

Detailed Data Descriptions

This section offers a quick description of what InPheC expects as inputs and outputs of each step.

InPheC_Step1.py

Input Data:

• Gene Expression Data (RNA-seq):

  • Location: ./Data/RNA_seq/
  • Input 1.1: RNAseq_expr_sample_gse981-gse361-gse371_voom-zScore.csv - Provides normalized gene expression values for various samples.
  • Input 1.2: RNAseq_pval_gene-phenotype_gse361-gse981-gse371_logFC2_FDR0.01.csv - Provides p-values indicating the association strengths between genes and specific phenotypes.
  • Input 1.3: RNAseq_pval_gene-phenotype_gse361-gse981-gse371_allGenes.csv - Provides gene-phenotype association p-values for a comprehensive set of genes.

• ChIP-seq Data:

  • Location: ./Data/ChIP_GSE61475_G/
  • Content Description: The directory contains multiple .bed.tsv files for different TFs across two lineages: dEN and h64. Each file represents ChIP-seq binding data for a specific TF.
  • Example Files: (Input 1.4x)
    • dEN_CTCF_GSM1505625_raw_pyBedTools.bed.tsv
    • h64_EOMES_GSM1505627_raw_pyBedTools.bed.tsv

• Transcription Factors List:

  • Input 1.5: mapped_hmTFs_legitTFs-HumanTFDB.csv
  • Purpose: Lists TFs included in the analysis to ensure that only relevant TFs are used for constructing the regulatory network.

Output Data:

• Location: ./step1/

• Output 1.1: ChIPseq_pval_gene-tf_minQ_h64_dEN.csv .

  • Details: Includes processed ChIP-seq data where only relevant TF-gene interactions are retained based on minimal p-values across peaks.

• Output 1.2: RNAseq_pval_gene-tf_intersect_chip_dEN.csv.

  • Details: Contains TF-Gene associations from RNA-seq data.

• Output 1.3: RNAseq_pval_gene-pheno_intersect_chip_dEN.csv.

  • Details: Updated gene-phenotype association data aligned with the results from the gene-TF association analysis to maintain consistency across analyses.

InPheC_Step2.py

Input Data: (Intermediate Outputs From InPheC_Step1.py)

• Utilizes the intermediate outputs from Step 1, located in ./step1/:
  • Intermediate Output 1.1: ChIPseq_pval_gene-tf_minQ_h64_dEN.csv
  • Intermediate Output 1.2: RNAseq_pval_gene-tf_intersect_chip_dEN.csv
  • Intermediate Output 1.3: RNAseq_pval_gene-pheno_intersect_chip_dEN.csv
• Additionally, uses Input 1.3 from Step 1 (RNAseq_pval_gene-phenotype_gse361-gse981-gse371_allGenes.csv) located in ./Data/RNA_seq/.

Output Data:

• Location: ./step2/
• Outputs are serialized Python objects (pickles) containing the results from each batch processed. Each file corresponds to a batch and contains PGM outputs, facilitating subsequent analyses or batch-specific reviews.
  • Examples:
    • RnCnP_vary-Tp0.0909_nc4_ni4000_nt2000_noburn_batch0.pkl
    • RnCnP_vary-Tp0.0909_nc4_ni4000_nt2000_noburn_batch1.pkl
    • ... up to the last batch.
  • A sub-directory named tmp contains intermediate files used during processing, such as in2pgm_woCtrl.csv, for debugging or process validation purpose.

InPheC_Step3.py

Input Data: (Intermediate Outputs From InPheC_Step2.py)

• Utilizes all .pkl files from ./step2/.

Output Data:

• Location: ./step3/
• Output 3.1: RnCnP_vary-Tp0.0909_nc4_ni4000_nt2000_nb400_minMax.csv is the final GRN that is phenotype-relevant.

Other Documentation

• Using your own data: Guidelines on preparing and integrating your own datasets with InPheRNo-ChIP.
• Script Reference Guide: Detailed information on function parameters and code documentation for each script.

Getting Help

For assistance with InPheRNo-ChIP, we offer 2 channels:

• GitHub Issues: For bug reports or feature requests, please use the Issues section.
• GitHub Discussions: For general questions, ideas, or community discussions, the Discussions section is the perfect place.

Your questions and feedback are invaluable to us, and we encourage you to utilize these channels to enhance your experience with InPheRNo-ChIP.

License

InPheRNo-ChIP

Inference of Phenotype-relevant Regulatory Networks with ChIP-seq Integration

Copyright (C) 2024 Chen Su, Amin Emad

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.