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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