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Table 3 A comparison of MGSEA with other GSEA extensions

From: MGSEA – a multivariate Gene set enrichment analysis

Algorithm MGSEA moGSA MONA GeneTrail2 Klebanov et. al. SetRank PAGE
Data modality Multimodal Multimodal Multimodal Unimodal Unimodal Unimodal Unimodal
Input List of sorted genes based on mutual information scores for each feature (CNV, methylation, or mRNA) Different measurements of gene activity (CNV, mRNA, protein) Lists indicating whether genes are differentially expressed based on distinct measurement methods (mRNA, protein, microRNA) A GSE file with data from both sample and reference group, or two GDS files for sample and reference group, or a list of genes and their scores Single measurement of gene activity Single measurement of gene activity Single measurement of gene activity
Output Graphical display of conditional random walk and its statistical significance List of pathways and its statistical significance, and inferred activity in individual samples List of pathways and its statistical significance List of pathways and its statistical significance List of pathways and its statistical significance List of pathways and its statistical significance List of pathways and its statistical significance
Main Features Evaluates the combinatorial enrichment relation between biological features Integration of multiple measurements of a gene into a single score for gene set enrichment analysis Regulatory relations between features (such as microRNA and its targets) are considered Each feature (gene, protein, microRNA, and SNP) is tested independently
for pathway enrichment
Multivariate
N-statistic for multivariate significance testing
Reduces false positives by discarding gene sets identified as significant due to high overlap with another significant gene set Parametric analysis of GSEA which is less computational intensive and more statistically sensitive