Hi,

I was running for Deseq2 on the same set of genes and samples with varying alpha as shown below.

Why do adjust p-value changes for the same gene when alpha changes, even though the pvalue remains same?

I thought the alpha is the fdr cutoff and the "NA" means that it didn't cleared the FDR cutoff. Is my interpretation correct?

dds <- DESeq(dds, parallel=TRUE )

alpha <- 0.05

Con2vsCon1 <- results(dds,contrast=c(“Condition”,”Con2”,”Con1”),alpha=alpha)

write.table(Con2vsCon1, "Con2vsCon1_0.05.xls", sep="\t")

The result file:

alpha <- 0.01

Con2vsCon1 <- results(dds,contrast=c(“Condition”,”Con2”,”Con1”),alpha=alpha)

write.table(Con2vsCon1, "Con2vsCon1_0.01.xls", sep="\t")

Session info

R version 3.3.1 (2016-06-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
locale:
[1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
[3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
[5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
[7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
[9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] pheatmap_1.0.7             DESeq2_1.12.4             
[3] SummarizedExperiment_1.4.0 Biobase_2.32.0            
[5] GenomicRanges_1.26.1       GenomeInfoDb_1.8.7        
[7] IRanges_2.8.0              S4Vectors_0.12.0          
[9] BiocGenerics_0.20.0       

loaded via a namespace (and not attached):
[1] Rcpp_0.12.8          RColorBrewer_1.1-2   plyr_1.8.4          
[4] XVector_0.12.1       tools_3.3.1          zlibbioc_1.18.0     
[7] rpart_4.1-10         RSQLite_1.0.0        annotate_1.50.0     
[10] tibble_1.2           gtable_0.2.0         lattice_0.20-34     
[13] Matrix_1.2-6         DBI_0.4-1            gridExtra_2.2.1     
[16] genefilter_1.56.0    cluster_2.0.5        locfit_1.5-9.1      
[19] grid_3.3.1           nnet_7.3-12          data.table_1.10.0   
[22] AnnotationDbi_1.36.0 XML_3.98-1.4         survival_2.39-4     
[25] BiocParallel_1.6.6   foreign_0.8-67       latticeExtra_0.6-28
[28] Formula_1.2-1        geneplotter_1.50.0   ggplot2_2.2.0       
[31] Hmisc_3.17-4         scales_0.4.1         splines_3.3.1       
[34] assertthat_0.1       colorspace_1.3-1     xtable_1.8-2        
[37] acepack_1.3-3.3      lazyeval_0.2.0       munsell_0.4.3

What changes when you change alpha is the independent filtering. If you look in ?results for the explanation of the argument alpha:

   alpha: the significance cutoff used for optimizing the independent
          filtering (by default 0.1). If the adjusted p-value cutoff
          (FDR) will be a value other than 0.1, ‘alpha’ should be set
          to that value.

The recommendation is to set alpha to the value that will be used to threshold adjusted p-values, because that is what the independent filtering is trying to optimize (and likewise for IHW). For independent filtering, this chosen value affects the filter on mean of normalized counts for discarding genes with too little signal for testing. Removing more genes will lower the adjusted p-values for genes that survive the filter.