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Table 2 PROMO’s key features

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

CategoryKey Features
Data import▪ Importing genomic data from tabular CSV files
▪ Importing UCSC’s XENA genome matrix and phenotype files
▪ Importing GEO series files
▪ Adding clinical labels from file
Preprocessing▪ Flooring, ceiling and row normalization
▪ Filtering of samples by clinical labels
▪ Filter features by range, variance, gene symbols or by an external list
Data exploration and visualization▪ PCA, t-SNE
▪ Data distribution plots
▪ Survival Analysis (Kaplan Meier, Log rank)
▪ Multi-label expression matrix figures
Sorting▪ Sorting samples and features based on genomic data
▪ Sorting samples based on clinical labels
Clustering▪ Clustering both samples and features using K-means [27], hierarchical clustering [28], and Click [29]
▪ Browsing clustering history and zooming into specific clusters
Sample cluster analysis▪ Automated multi-label enrichment test for detecting enrichment of clinical labels
Feature cluster analysis▪ Gene ontology enrichment analysis
Biomarker discovery▪ Applying statistical tests for detecting differentially expressed genes/features
▪ Filter results by FDR corrected p-value and fold change
▪ Rank genes based on survival prediction (COX regression)
Classifier generation▪ Automatic generation of decision tree classifiers for selected sample labels
Integrative multi-omic analysis▪ Assembly of dataset collection
▪ Multi-omic clustering using SNF [39], NEMO [40] or Consensus Clustering [41]
▪ Inter-omic correlation identification