Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data

BMC Bioinformatics

Table 2 GRSN aids Gene Set Enrichment Analysis (GSEA).

SS dataset (mutant vs. control)	Without GRSN		With GRSN
Pathway	NES	FDR	NES	FDR
CR_CYTOSKELETON [28, 29]	0.9053	0.6021	1.2209	0.2131
vegfPathway [24]	Not found		1.1346	0.2775
cell_adhesion [30]	Not found		0.9004	0.6332
RS dataset (Myc vs. control)	Without GRSN		With GRSN
Pathway	NES	FDR	NES	FDR
Cell_Cycle [31]	1.6516	0.1171	1.7633	0.0240
CR_CELL_CYCLE [31]	1.5565	0.1221	1.6751	0.0488
G1_CELL_CYCLE [31]	1.6902	0.1461	1.6913	0.0398
DNA_DAMAGE_SIGNALLING [32]	1.5355	0.1353	1.6632	0.0490
cell_proliferation [31]	1.6101	0.1161	1.6207	0.0884
cell_cycle_checkpoint [31]	1.4528	0.1941	1.5852	0.1192
PROLIF_GENES [31]	1.5104	0.1539	1.5745	0.1139
cell_growth_and_or_maintenance [33]	1.4340	0.1984	1.5681	0.1099
HTERT_UP [33]	1.4436	0.2064	1.5566	0.1066
cellcyclePathway [31]	1.4618	0.1887	1.4981	0.1357
CR_DEATH [34]	0.9966	0.5629	1.2257	0.3680

GSEA is applied to the SS and RS datasets. Pathways known to be active in these datasets are shown and referenced. For each selected pathway, the Normalized Enrichment Score (NES) and the False Discovery Rate (FDR), as reported by GSEA, are shown. NES and FDR values are shown both for data processed with RMA alone (Without GRSN) and for data processed with RMA followed by GRSN (With GRSN) as indicated.

ISSN: 1471-2105