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 |