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owen.donohoe
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@owendonohoe-18047
Last seen 4.9 years ago
Hi
Below is a sample of my output from Deseq2. IDs are Entrez, I'd lke to use them to do pathway analysis with GAGE
> head(deseq2.res)
log2 fold change (MLE): Infection.Type Susceptible.Intected vs Susceptible.Non.Intected
Wald test p-value: Infection.Type Susceptible.Intected vs Susceptible.Non.Intected
DataFrame with 6 rows and 7 columns
baseMean log2FoldChange lfcSE stat pvalue
<numeric> <numeric> <numeric> <numeric> <numeric>
105333743 15.835268 2.5407024 1.0263944 2.4753665 0.013309952
105333775 4.730554 -0.7931533 1.5979284 -0.4963635 0.619637996
105334150 7.294318 -0.8172277 1.1966521 -0.6829284 0.494652087
105334256 9.666313 -1.4400413 0.7404432 -1.9448370 0.051794597
105334292 12.375891 0.2330889 1.0664642 0.2185623 0.826990998
105334343 4.961679 -5.1985084 1.3283420 -3.9135316 0.000090956
padj weight
<numeric> <numeric>
105333743 0.0341735692 1
105333775 0.7239544639 1
105334150 0.6144798713 1
105334256 0.1049475388 1
105334292 0.8813991828 1
105334343 0.0004646112 1
I prepared fold change data and gene set for GAGE as such
deseq2.fc=deseq2.res$log2FoldChange names(deseq2.fc)=rownames(deseq2.res)
exp.fc=deseq2.fc > head(exp.fc) 105333743 105333775 105334150 105334256 105334292 105334343 2.5407024 -0.7931533 -0.8172277 -1.4400413 0.2330889 -5.1985084 > str(exp.fc) Named num [1:13190] 2.541 -0.793 -0.817 -1.44 0.233 .. - attr(*, "names")= chr [1:13190] "105333743" "105333775" "105334150" "105334256" ...>kg.oyster=kegg.gsets(species = "crg", id.type = "kegg")
> head(kg.oyster) $kg.sets $kg.sets$`crg00010 Glycolysis / Gluconeogenesis` [1] "105317497" "105317783" "105318439" "105318496" "105319260" "105319828" [7] "105320252" "105321314" "105322313" "105322747" "105323106" "105323467" [13] "105323703" "105323969" "105324578" "105325277" "105325828" "105326051" [19] "105326107" "105326193" "105327683" "105328152" "105330573" "105330800" [25] "105330845" "105330868" "105330927" "105331349" "105332291" "105332305" [31] "105332343" "105334225" "105334471" "105335306" "105335383" "105335442" [37] "105335816" "105335896" "105336470" "105336484" "105336761" "105338126" [43] "105338756" "105339245" "105339247" "105339502" "105340365" "105340512" [49] "105343230" "105344389" "105344593" "105345431" "105345955" "105346675" [55] "105346996" "105347640" "105347662" "105348082" "109620123" $kg.sets$`crg00020 Citrate cycle (TCA cycle) [1] "105317044" "105317497" "105319941" "105320391" "105322330" "105323405" [7] "105326051" "105326107" "105326324" "105326362" "105326646" "105328068" [13] "105328773" "105328869" "105330227" "105330573" "105331349" "105332143 [19] "105332343" "105333137" "105334026" "105334926" "105335210" "105335901 [25] "105337089" "105338659" "105338756" "105338798" "105339502" "105341248" [31] "105343391" "105343888" "105345431" "105345932" "105347017" "105349203"
However when I use this in GAGE analysis all I gen back are NAs/NaNs ??
> fc.kegg.p <- gage(exp.fc, gsets = kg.oyster, same.dir=TRUE)
> lapply(fc.kegg.p, head)
$greater
p.geomean stat.mean p.val q.val set.size exp1
kg.sets NA NaN NA NA 0 NA
sigmet.idx NA NaN NA NA 0 NA
sig.idx NA NaN NA NA 0 NA
met.idx NA NaN NA NA 0 NA
dise.idx NA NaN NA NA 0 NA
$less
p.geomean stat.mean p.val q.val set.size exp1
kg.sets NA NaN NA NA 0 NA
sigmet.idx NA NaN NA NA 0 NA
sig.idx NA NaN NA NA 0 NA
met.idx NA NaN NA NA 0 NA
dise.idx NA NaN NA NA 0 NA
$stats
stat.mean exp1
kg.sets NaN NA
sigmet.idx NaN NA
sig.idx NaN NA
met.idx NaN NA
dise.idx NaN NA
At the moment I'm unsure why this is happening, I would appreciate any help with this.
Did you figure out the problem? I'm experiencing this now as well...