Brain_WGCNA

Scripts for the manuscript:

Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions

"Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812" [https://www.science.org/doi/10.1126/sciadv.ade2812]

Scripts for the "Fixed-windows" and "Sliding-windows" studies (NC samples only):

• Code1(NC).R: Preprocess tissue rse data for NC samples
• Code2(NC).R: Remove confounders effect
• Code3(NC).R: Estimate beta for WGCNA via connectivity match
• Code4(NC).R: Perform WGCNA to obtain age-parsed and non-parsed networks
• prepare_WGCNAnetwork_script.R: Combine WGCNA network output (module assignments, connectivity etc) in a single file
• prepare_OTHERnetwork_script.R: Preprocess and collate network data from various published networks
• prepare_ALL_WGCNA_network_script.R: Combine WGCNA output from our networks and other previous published networks
• new_wide_form_data_script.R: Create a combined wide_data_file with WGCNA output from our and other networks, plus genestats, MAGMA stats etc
• get_gene_module_list script.R: Get a gene_module_list for all networks from the wide_data_file to be used for enrichment analysis
• Enrichments.online.script.R:
  • Script to run various enrichments on gene_module_list: SCZ, DEGs, DMGs, TWAS, GO, MAGMA, Cell Specificity etc
  • Script to make sankey plots for DLPFC, HP and CN SCZ risk genes
  • Prepare and format data for other visualisations
• Enrichment Visualisation.online.script.R:
  • Script to plot main enrichment plots for SCZ risk modules for all networks
  • Script to plot GO enrichment plots for SCZ risk modules for all networks
  • Script to plot TF enrichment plots for SCZ risk modules for all networks
  • Script to plot other Pathology enrichment plots for SCZ risk modules for all networks

Scripts for the "MAGMA prediction model":

• MAGMA_prediction.py: Code to perfrom ML MAGMA prediction for Age-parsed and non-parsed networks

Scripts for the "Cell population enrichment" study:

• matched-sample-HP-DG(Code1).R: Preprocess samples match rse data for HP-DG
• matched-sample-HP-DG(Code2,noqsva).R: Remove confounders effect
• matched-sample-HP-DG(Code2,qsva).R: Remove confounders effect + QSV
• matched-sample-HP-DG(Code3,noqsva).R: Estimate beta for WGCNA via connectivity match
• matched-sample-HP-DG(Code3,qsva).R: Estimate beta for WGCNA via connectivity match [QSVA pipeline]
• matched-sample-HP-DG(Code4,noqsva).R: Perform WGCNA to obtain samples match HP-DG networks
• matched-sample-HP-DG(Code4,qsva).R: Perform WGCNA to obtain samples match HP-DG networks [QSVA pipeline]

Scripts for the "Sliding-windows" study:

• sliding_window(Code1,SCZ).R: Preprocess tissue rse data (SCZ samples only)
• sliding_window(Code2,SCZ).R: Remove confounders effect
• sliding_window(Code3,NC).R: Arrange samples in sliding age windows and estimate beta for WGCNA via connectivity match (NC samples only)
• sliding_window(Code3,SCZ).R: Arrange samples in sliding age windows and estimate beta for WGCNA via connectivity match (SCZ samples only)
• sliding_window(Code4,NC).R: Perform WGCNA to obtain sliding window networks on the NC samples
• sliding_window(Code4,SCZ).R: Perform WGCNA to obtain sliding window networks on the SCZ samples
• Enrichments and Visualisation(Sliding Window).R: Script to plot SCZ enrichment measures for sliding window networks (NC + SCZ)
• Visualisation(Sliding Window).R: Script for additional sliding window plots (max.Fold change vs median age with rugs)

Scripts for the "Sliding-windows enrichment":

• module_enrichments_MAGMA.R: Calculate enrichment of MAGMA per module using genesettest
• module_enrichments_DRONC.R: Calculate enrichment of DRONC celltypes per module using genesettest
• module_enrichments_GO.R: Calculate GO term ratio per module
• combine_MAGMA.R: Add MAGMA module enrichments to GO term ratio and DRONC celltype enrichment dataframes
• module_enrichments_p_values_DRONCtoMAGMA.R: Calculate across module association of MAGMA enrichment to cell type enrichment
• module_enrichments_p_values_GOtoMAGMA.R: Calculate across module association of MAGMA enrichment to GO term ratio

Scripts for the "replication in human iPSC data":

• stemcell(Code1).R: Prepare and preprocess iPSC data
• stemcell(Code2).R: Remove confounders effect
• stemcell(Code3)[noKO].R: Estimate beta for WGCNA via connectivity match
• stemcell(Code4)[noKO].R: Perform WGCNA to obtain iPSC networks [noKO]
• Enrichment and Visualisation (stemcells).R: Script to plot enrichment in PGC lists vs enrichment in consensus list for the iPSC networks [noKO and shuffled/KO]

Scripts for the "Consensus environment":

• Consensus environment enrichment for GO and SCZ.R: Run GO and SCZ enrichments on consensus gene environment
• Consensus environment enrichment for KEGG.R: Run KEGG enrichment on consensus gene environment

Uncategorized scripts:

• Miscellenous scripts: Consensus genes computation, Jaccard index calculations etc

Additional links:

• Zenodo: Interactive Sankey files, wide_form data files, modulewise SCZ enrichment results, preprocessed data files etc
• NETS@LIBD: Future project updates and general updates of our research group at Lieber Institute for Brain Development will be available here.

For any data or code inquiries please contact Giulio Pergola: [Giulio.Pergola@libd.org] [https://www.libd.org/team/giulio-pergola-phd/]