Hi ,

Based on a selection of gene ID , to find the overrepresentation of pathway, 
we could use: 1. find_enriched_pathway function (KEGG profile) or 2. spia ( SPIA package) where PNDE gives an overrepresentation. These functions works very well. However (Based on a same  selection of gene ID!),
 I get some differents results.
If I compare the top ten list, I have only one pathwayID. I expected a similar result with a little potential differents!

A. If I look inside the function to compute the PVALUE  the function seems the same : 

KEGG profile : 
pvalue[x] <- phyper(kegg_result_length[x], keggpathway2gene_length[x], 
            length(unique(unlist(keggpathway2gene))) - kegg_result_length[x], 
            length(unique(unlist(kegg_result))), lower.tail = F)

And SPIA:
ph[i] <- phyper(q = noMy - 1, m = pSize[i], n = length(all) - 
                pSize[i], k = length(de), lower.tail = FALSE) 
 


HENCE, the compute of pvalues seems the same.

B. The compute of pvalues seems the same ! Not really : the reference of compute the overepresentation .
KEGG profile: 
the reference is based on keggpathway2gene

And SPIA:
the reference is based on "all" . All is all id  present on the chips. In my case ( Illumina HT6 v2 , this chips is considered as pangenomic. 


HENCE, the reference muste be the same in this case.



My question.
 In your opinion,
Why this MAJOR difference between these both methods?
Actually, I offer the both results  but I need to justify the difference. 

If the authors of these methods ( or others) could be given me some explications or explain to me where I'm wrong , I will appreciate that !

Greg Montréal



> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=French_Canada.1252  LC_CTYPE=French_Canada.1252   
[3] LC_MONETARY=French_Canada.1252 LC_NUMERIC=C                  
[5] LC_TIME=French_Canada.1252    

attached base packages:
[1] grid      tcltk     stats     graphics  grDevices utils     datasets  methods  
[9] base     

other attached packages:
 [1] xtable_1.7-0              GO.db_2.8.0               NbClust_1.2              
 [4] gplots_2.11.0             MASS_7.3-17               KernSmooth_2.23-7        
 [7] caTools_1.13              bitops_1.0-4.1            gdata_2.12.0             
[10] gtools_2.7.0              pls_2.3-0                 Mfuzz_2.16.0             
[13] DynDoc_1.36.0             widgetTools_1.36.0        e1071_1.6-1              
[16] class_7.3-3               KEGGprofile_1.0.0         KEGG.db_2.8.0            
[19] TeachingDemos_2.8         png_0.1-4                 SPIA_2.8.0               
[22] KEGGgraph_1.14.0          XML_3.95-0.1              annotate_1.36.0          
[25] GOstats_2.24.0            graph_1.36.0              Category_2.24.0          
[28] lumiHumanAll.db_1.18.0    org.Hs.eg.db_2.8.0        lumiHumanIDMapping_1.10.0
[31] RSQLite_0.11.2            DBI_0.2-5                 AnnotationDbi_1.20.0     
[34] limma_3.14.0              lumi_2.10.0               nleqslv_1.9.4            
[37] Biobase_2.18.0            BiocGenerics_0.4.0       

loaded via a namespace (and not attached):
 [1] affy_1.36.0           affyio_1.26.0         AnnotationForge_1.0.0
 [4] BiocInstaller_1.8.3   colorspace_1.1-1      genefilter_1.40.0    
 [7] GSEABase_1.20.0       IRanges_1.6.17        lattice_0.20-10      
[10] Matrix_1.0-6          methylumi_2.4.0       mgcv_1.7-13          
[13] nlme_3.1-103          preprocessCore_1.20.0 RBGL_1.34.0          
[16] splines_2.15.0        stats4_2.15.0         survival_2.36-12     
[19] tkWidgets_1.36.0      tools_2.15.0          zlibbioc_1.4.0  
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