Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis

Table 1 Comparison of major features of PPEP in WPS with other related tools

	WPS PPEP	Babelomics	High-throughput GOminer	EASE/DAVID	GSEA
Number of gene lists to be analyzed at the same time for comparison	Multiple lists	1–2 lists	Multiple lists	one list a time	NA
Batch computation for multiple gene lists	Yes	No	Yes	No	No
Merging Batch Results for a matrix of data	Yes (*ES, ListHits, FDRs)	No	Yes (FDRs)	No	No
Annotation scopes	Many: Pathways; GO; miRNA Targets; TF targets; GSEA annotations	GO terms	Go terms	Many: Pathways, GO terms Interaction	GSEA annotations
Pattern extraction at pathway enrichment level	Yes	No	No	No	No
Provide matrix of data for clustering analysis at pathway level	*ES, ListHits, or FDRs	No	Only FDRs	No	No
Retrieve associated genes from selected terms	Yes	No	No	No	No
Network and data visualization of the associated genes and terms (GTANs)	Yes	No	No	No	No
Gene list sorting utility	Yes	No	No	No	No
Data manipulation utility	Yes	No	No	No	No
SLEPR pathway ranking method [22]	Yes	No	No	No	No
Group testing methods using gene sets	ORA	ORA	ORA	ORA	FCS

The Pathway Pattern Extraction Pipeline (PPEP) in WPS has many unique features not found in a single similar application. Five related tools (first row of the table) were selected for comparison of major features (first column of the table) with PPEP in WPS. The other 4 tools for comparison include: Babelomics (Fatigo) [13]; HTP GoMiner [14]; EASE/DAVID [12, 46]; GSEA [15, 16].
ORA: Over-representation analysis [21]; FCS: functional class scoring [47]; *ES: Enrichment score.

ISSN: 1471-2105