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. 2009 May 27:10:161.
doi: 10.1186/1471-2105-10-161.

GAGE: generally applicable gene set enrichment for pathway analysis

Weijun Luo  1 , Michael S Friedman, Kerby Shedden, Kurt D Hankenson, Peter J Woolf
Affiliations

GAGE: generally applicable gene set enrichment for pathway analysis

Weijun Luo et al. BMC Bioinformatics. .

Abstract

Background: Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.

Results: To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature.

Conclusion: GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php.

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Figures

Figure 1
Figure 1
A schematic overview of the GAGE algorithm. GAGE has three major steps. (a) Step 1: input preparation. Separate gene sets into two categories: experimental sets and canonical pathways, for differential treatment in significant test. (b) Step 2: gene set differential expression tests based on one-on-one comparison between samples from the two experimental conditions. For each experiment-control pair, calculate differential expression in log based fold change for all genes. Test whether specific gene sets are significantly differentially expressed relative to the background whole set using two-sample t-test. (c) Step 3: summarization. For each gene set, derive a global p-value based on a meta-test on the negative log sum of p-values from all one-on-one comparisons. More details of GAGE are given in the Methods. Variables m, s and n are the mean fold change, standard deviation and number of genes in a gene set, M, S and N are those for the whole set. A similar schematic overview of the PAGE algorithm is shown in Additional file 1: Supplementary Figure 1.
Figure 2
Figure 2
A simulation study using microarray data and synthetic testing gene sets. (a-c) p-values on the differential expression of testing gene sets with increasing levels of enrichment of up-regulated genes, when GAGE (a, b), GSEA (a, b) and PAGE (c) were applied. (d) The series of beta distribution curves with 1 ≤ β ≤ 10 and fixed α = 1 used to sample the testing gene sets with increasing levels of up-regulation from a sorted whole gene list. For each β value, we generated testing gene sets of two different size n = 10 genes (small sets) and n = 50 genes (large sets), 100 gene sets each. We then applied GAGE, PAGE or GSEA to test the overall expression level up-regulation in these gene sets. Mean p-values plus with standard error were shown. See Methods and Results for details. Note that GAGE with both 1-on-1 and 1-on-grp options produces similar results, although only the former is shown here.
Figure 3
Figure 3
GAGE captured canonical pathways which are significantly perturbed towards both directions following 8 h BMP6 treatment in human MSC. (a) Gene expression level changes in the top 3 different significant canonical pathways inferred by GAGE and PAGE. (b) Gene expression level changes in the canonical TGF beta signaling pathway and (c) plotted in pseudo-color on the pathway topology derived from KEGG database. The solid horizontal line and dashed lines in (a-b) mark the mean fold changes of all genes and the positive/negative two times standard deviation from the mean respectively. Note that in (c), one KEGG node may correspond to multiple closely related genes with the same function, and the maximum fold changes among these genes are plotted as the color of the node.
Figure 4
Figure 4
Differential gene expression in the top 2 significant experimental sets inferred by GAGE or PAGE. Gene expression levels are log 2 based, and compared between human MSC with 8 hour BMP6 treatment vs control. Results for the first experiment are shown, and the second replicate experiment is similar.
Figure 5
Figure 5
Gene expression fold changes (log 2 based) in the top 3 significant experimental sets inferred by GAGE or PAGE. For each gene set, the bar height represents mean and error bar represent standard error of gene expression fold changes induced by 8 hour BMP6 treatment in human MSC. GAGE uses two-sample t-test and PAGE does one-sample z-test. PAGE frequently selected gene sets with extreme up or down regulation in a few genes and almost no changes in the rest. Such gene sets have too large within-group variances to be called significantly different from the background based on two-sample t-test, even though their mean fold changes are big.
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