Melanoma Multi-omics Data Analyses

Scripts

Data_prep

• Cleaning.R: preprocessing for proteomics, phosphoproteomics, transcriptomics, clinical, and histological data
• ICA_subtype_prep.R: prepare data frame to add proteomics subtypes for ICA
• post_ICA.R: ICA output formatting
• OLA_prep.R: prepare data frame for outlier analyses
• IHC_model.R: prepare IHC validation data frames; build IHC validation regression models; plot IHC validation results

Figures

• Aggregate.R: summarize results from ICA, outlier analyses, and Cox regression analyses
• density.R: make density plots of proteomics and phosphoproteomics data for each sample
• graph.R: make graphs to represent the relationship between ICs and pathways
• graphlite.R: make graphs to represent the relationship between ICs and pathways (filtered)
• GSEA.R: IC pre-ranked GSEA; make heatmap of the top-ranked proteins
• ica_pca_comparison.R: ICA and PCA analyses comparison
• Kaplan-Meier6.R: draw KM curves based on different AUROC cutoffs
• OLA_HM.R: make heatmap showing the expression level of all the outliers found by outlier analyses across all samples
• QC_figure.R: quality control for omics data (Figure S1)
• RNA-PROT-cor.R: protein and RNA expression correlations
• top-to-pathway.R: significant pathways found by ICA that involve the 10 selected proteins

ICA

• clinical_association_template.R: find association between ICs and clinical variables
• ICA_Clusters_Functions.R: ICA clustering helper functions
• ICA_subtype_HM.R: Heatmaps of top proteins' expression ranked by ICs that correlate with the proteomics subtypes
• ICA_template.R: independent component analysis

outlier_analysis

• outlier analysis (Blacksheep); please refer to the document files in the folder
• https://www.biorxiv.org/content/10.1101/825067v2.full.pdf