Skip to main content

Table 1 PROMO’s main analysis types

From: PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets

 Analysis typeBiomedical goalRelevant PROMO features
1General exploration and visualizationExplore the genomic dataset vis-à-vis the clinical labels
Prepare the dataset for downstream analysis, test its consistency and visualize its properties
• Variance-based feature filtering
• Label-based sample filtering
• Normalization
• Sorting by sample label or mean expression
• Visualizing data distribution
• PCA, t-SNE
2Focus on genes of interestExplore the expression profiles of specific genes vis-à-vis multiple clinical labels
Identify co-expressed genes
• Filter features based on gene symbols
• Rank genes by correlation to a given gene symbol
• Multi-label matrix visualization
3Disease subtype identificationLook for clinically significant sample clusters• Sample clustering
• Label enrichment analysis
• Survival analysis
• Classification
4Co-regulated feature group identificationIdentify groups of similar features, characterize each group by function• Feature clustering
• GO Enrichment analysis
5Biomarker discoveryFind features that distinguish among sample groups, correlate groups with survival and other clinical data• Statistical tests for identifying differentially expressed genes
• Biomarker-based survival analysis
• Rank genes by survival prediction
6Integrative multi-omic analysisStratify patients and identify coherent feature groups by integrating data from different omics• Multi-omic sample clustering
• Inter-omic feature correlation