Enhancer-inclusive gene regulatory networks allow us to characterize and provide functional understanding of regulatory interactions underlying phenotypes in a cell-type specific manner.GRaNIE is a method that infers gene regulatory networks including enhancers from bulk transcriptomics (RNA-seq) and chromatin accessibility (ATAC-seq) data by reconstructing tripartite networks which describe transcription factor-enhancer-gene associations. Bulk data mask cell-type- and cell-state-specific gene expression and chromatin accessibility data as the counts represent an average of the activity of a population of cells. Hence, to overcome these limitations the approach requires its adjustment at the single-cell resolution to take into account inter-cell variation and consequently truly capture specific cell-type and cell-state regulatory differences. Therefore, a proper benchmark at the single-cell layer of the approach remains to be accomplished. We propose a way of benchmarking GRaNIE at the single-cell resolution through a validation-based approach that assesses the network's ability to predict real regulatory interactions from cell-type-specific promoter capture Hi-C, eQTL and ChIP-Seq data. Furthermore, we report further validation through a network biological comprehensive analysis alongside an evaluation of the eGRNs predictive power by means of GRaNPA, a machine learning framework that assesses the network's capability of predicting cell-type-specific differential gene expression data. The results show GRaNIE, applied to single-cell hiPSC-neuron timecourse datasets, is capable of inferring neuronal differentiation and development relevant regulatory associations as well as predicting cell-type specific iPSC against neuron DGE at the single-cell level.

• 📄 timecourse_preprocessing.R

  Preprocessing performed on the timecourse dataset.

• 📄 combined_preprocessing.R

  Preprocessing performed on the combined dataset.

• 📄 data_extraction.R

  Extraction of raw data (RNA counts, ATAC counts, metadata) from the standardly preprocessed dataset-specific-seurat objects for further dataset-customized preprocessing.

• 📄 SeuratToAnndata.R

  Conversion from seurat objects to AnnData objects to permit the usage of the preprocessed datasets for eGRN inference via SCENIC+ which is only available in Python/Scanpy.

• 📄 get_fragments_from_archr.R

  Get the ATAC fragments from the ArchR projects for each dataset.

• 📄 GRaNIE_batch_mode.R

  Run GRaNIE in batch mode (across a wide range of cluster resolutions on the integrated WNN space) generating an eGRN per resolution.

• 📄 GRaNIE_specific_resolution.R

  Run GRaNIE in for a specific cluster resolution.

• 📄 GRaNIE_helper_functions.R

  Helper functions to execute GRaNIE for single-cell datasets. Functions to preprocess the data, run GRaNIE for a specific metadata column and in batch mode.

• 📄 GRaNIE_metadata.R

  Run GRaNIE pseudobulking the cells according a metadata column e.g. celltype.

• 📄 timecourse_scGRaNIE_test.R

  Run GRaNIE on the timecourse dataset for initial size tests, different cell type annotations and batch mode are utilized.

• 📄 network_enrichment_analysis.R

  Perform gene enrichment and TF enrichment analyses on a given eGRN.

• 📄 custom_peakgene_QC_plots.R

  Customize GRaNIE original code that creates the peak-gene quality control plots in a qay that different eGRNs can be analyzed at the same time.

• 📄 MergePeakGeneQCPDFs.sh

  Create a single pdf document with the final peak-gene quality control plots for different eGRNs (each network-specific QC plot in a different page)

• 📄 CRISPR_screen_GRaNIE.R

  Run GRaNIE for separate scRNA and scATAC modalities for a NPC-Neuron datasets perturbed for a set of Schizophrenia markers.

• Validations

• 📄 ChiPSeq_setup.R

  ChiP-seq data preprocessing.

• 📄 ChiPSeq_validation.R

  Validation of network TF-peak links with preprocessed ChiP-Seq data.

• 📄 pcHiC_setup.R

  Promoter capture Hi-C data preprocessing.

• 📄 pcHiC_validation.R

  Validation of network peak-gene links with preprocessed promoter capture Hi-C data.

• 📄 eQTL_enrichment.R

  eQTL preprocessing and enrichment / validation analysis on network peak-gene links.

• 📄 GRN_validation_analyses.R

  Integration and visualization of networks validation.

• GRaNPA

• 📄 GRaNPA_network_evalutation.R

  Networks evaluation for predicting cell-type-specific DGE using GRaNPA.

• 📄 RNA_preprocessing_DGE_analysis.R

  Bulk RNA preprocessing and DGE analysis from an independent hiPSC to Neuron time course dataset.

• eGRN Analyses

• 📄 Network_visualizations.R

  Visualization of a GRaNIE network and TF/Gene enichment analyses.

• 📄 celltype_GRNstats_comparison.R

  Comparison of the effects of different cell-type annotations on GRaNIE networks.

• 📄 pearson_vs_spearman_analysis.R

  In depth study of pearson and spearman correlation algorithms for links inference in GRaNIE.

• 📄 extensive_GRaNIE_networks_analyses.R

  GRaNIE networks analyses based on pseudobulking resolutions, overlaps on links across resolutions, networks general stats, pcHi-C validations, robust links and pcHI-C validated links relationhsip, and further pearson vs spearman correlation analysis.

• 📄 overlap_analysis.R

  Overlap analysis of different type of links (TF-peak-gene, TF-peak and peak-gene) across cluster resolutions.

• SCENIC+

• 📄 scRNA-seq_Preprocessing.ipynb

  Timecourse RNA preprocessing using Scanpy.

• 📄 scRNA-seq_setup_timecourse.ipynb

  Import the preprocessed timecourse Anndata objects and format it for further steps.

• 📄 scATAC-seq_preprocessing_timecourse.ipynb

  Generate pseudobulk ATAC-seq profiles, call peaks and generate a consensus peak set for the timecourse dataset.

• 📄 timecourse_cistopic.ipynb

  Perform CisTopic modelling, candidate enhancer regions inference, motif enrichment analysis using pycisTarget for the timecourse dataset.

• 📄 timecourse_scenicplus.ipynb

  eGRN inference using SCENIC+ on the timecourse dataset and further network analysis.

• 📄 scRNA-seq_setup_combined.ipynb

  Import the preprocessed combined Anndata objects and format it for further steps.

• 📄 scATAC-seq_preprocessing_combined.ipynb

  Generate pseudobulk ATAC-seq profiles, call peaks and generate a consensus peak set for the combined dataset.

• 📄 combined_cistopic.ipynb

  Perform CisTopic modelling, candidate enhancer regions inference, motif enrichment analysis using pycisTarget for the combined dataset.

• 📄 combined_scenicplus.ipynb

  eGRN inference using SCENIC+ on the combined dataset and further network analysis.

• Pando

• 📄 Pando.R

  eGRN inference using the Pando method.