I would like to check if some gene signature are enriched as up-regulated on my rnaseq experiment.

I have  2 groups and I calculate the differential expression using Deseq2. I have a folder where I have some signature genes and I don't understand how to use geneSetTest. If i use pvalue I have some errors . Only if I use log2Foldchange  seems to work.

What is the right way to apply this method?

resSig<-res[which(res$pvalue < .05),]
files <- list.files(path=path, pattern="*.ensembl.csv", full.names=T, recursive=FALSE)
for (i in 1:length(files))
{

  print(files[i])
  listaGeni<- read.table(files[i],header=F,sep="\t")
signature1<-listaGeni$V1
index_gene<-match(signature1,resSig$ensembl)
knowres<-index_gene[!is.na(index_gene)]
a<-geneSetTest(knowres,res$log2FoldChange,"greater") #upregulated
b<-geneSetTest(knowres,res$log2FoldChange,"less") #downreglare
print(a)
print("###")
print(b)
}

a<-geneSetTest(knowres,res$pvalue,"greater")
Error in if (allsamesign) type <- "f" else type <- "t" :
  missing value where TRUE/FALSE needed

> sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=it_IT.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=it_IT.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=it_IT.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tximportData_1.0.2         tximport_1.0.3             gplots_3.0.1              
 [4] genefilter_1.54.2          limma_3.28.21              biomaRt_2.28.0            
 [7] reshape2_1.4.2             RColorBrewer_1.1-2         ggplot2_2.2.1             
[10] pheatmap_1.0.8             DESeq2_1.12.4              SummarizedExperiment_1.2.3
[13] Biobase_2.32.0             GenomicRanges_1.24.3       GenomeInfoDb_1.8.7        
[16] IRanges_2.6.1              S4Vectors_0.10.3           BiocGenerics_0.18.0       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.9          locfit_1.5-9.1       lattice_0.20-35      gtools_3.5.0        
 [5] assertthat_0.1       digest_0.6.12        plyr_1.8.4           backports_1.0.5     
 [9] acepack_1.4.1        RSQLite_1.1-2        zlibbioc_1.18.0      lazyeval_0.2.0      
[13] data.table_1.10.0    annotate_1.50.1      gdata_2.17.0         QoRTs_1.1.8         
[17] rpart_4.1-10         Matrix_1.2-8         checkmate_1.8.2      labeling_0.3        
[21] splines_3.3.2        BiocParallel_1.6.6   geneplotter_1.50.0   stringr_1.2.0       
[25] foreign_0.8-67       htmlwidgets_0.8      RCurl_1.95-4.8       munsell_0.4.3       
[29] base64enc_0.1-3      htmltools_0.3.5      nnet_7.3-12          tibble_1.2          
[33] gridExtra_2.2.1      htmlTable_1.9        Hmisc_4.0-2          XML_3.98-1.5        
[37] bitops_1.0-6         grid_3.3.2           xtable_1.8-2         gtable_0.2.0        
[41] DBI_0.6-1            magrittr_1.5         scales_0.4.1         KernSmooth_2.23-15  
[45] stringi_1.1.2        XVector_0.12.1       latticeExtra_0.6-28  Formula_1.2-1       
[49] tools_3.3.2          survival_2.40-1      AnnotationDbi_1.34.4 colorspace_1.3-2    
[53] cluster_2.0.6        caTools_1.17.1       memoise_1.0.0        knitr_1.15.1

I've seen users use goseq with DESeq2 gene results. Or I would also recommend using limma's roast or camera methods directly, starting with the counts matrix.