Hello everybody,

As I can read on the DESEQ2 manual, if we want an other p-adj diferent than 0.1 we have to select it by changing the alpha value. When I change it in the result formula:

alpha <- 0.05
res_0.05_filt <- results(dds1,contrast=c("condition","WT", "OE"),alpha=alpha)

Why I obtain more significant genes with p-adj 0.05 than 0.1? Why the mean count change in both cases? It is correct to do the results with 0.1 and them filter by 0.05?

here is the DESeq2 exit:

**> summary(res1)

out of 24732 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 17, 0.069%
LFC < 0 (down)     : 24, 0.097%
outliers [1]       : 0, 0%
low counts [2]     : 6672, 27%
(mean count < 17)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

> table(res1$padj<=0.05)

FALSE  TRUE 
18050    10 
> summary(res_0.05_filt)

out of 24732 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up)       : 8, 0.032%
LFC < 0 (down)     : 13, 0.053%
outliers [1]       : 0, 0%
low counts [2]     : 16679, 67%
(mean count < 239)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

> table(res_0.05_filt$padj<=0.05)

FALSE  TRUE 
 8032    21**

Here is the sessioninfo:

> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

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

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

other attached packages:
 [1] ggbiplot_0.55               scales_1.0.0                plyr_1.8.4                  corrplot_0.84               RColorBrewer_1.1-2         
 [6] pheatmap_1.0.12             sva_3.30.1                  genefilter_1.64.0           mgcv_1.8-28                 nlme_3.1-137               
[11] gplots_3.0.1.1              ggplot2_3.1.0               limma_3.38.3                DESeq2_1.22.2               SummarizedExperiment_1.12.0
[16] DelayedArray_0.8.0          BiocParallel_1.16.6         matrixStats_0.54.0          Biobase_2.42.0              GenomicRanges_1.34.0       
[21] GenomeInfoDb_1.18.2         IRanges_2.16.0              S4Vectors_0.20.1            BiocGenerics_0.28.0        

loaded via a namespace (and not attached):
 [1] bit64_0.9-7            splines_3.5.1          gtools_3.8.1           Formula_1.2-3          latticeExtra_0.6-28    blob_1.1.1            
 [7] GenomeInfoDbData_1.2.0 pillar_1.3.1           RSQLite_2.1.1          backports_1.1.3        lattice_0.20-38        digest_0.6.18         
[13] XVector_0.22.0         checkmate_1.9.1        colorspace_1.4-1       htmltools_0.3.6        Matrix_1.2-17          XML_3.98-1.19         
[19] pkgconfig_2.0.2        zlibbioc_1.28.0        xtable_1.8-3           gdata_2.18.0           htmlTable_1.13.1       tibble_2.1.1          
[25] annotate_1.60.1        withr_2.1.2            nnet_7.3-12            lazyeval_0.2.2         survival_2.43-3        magrittr_1.5          
[31] crayon_1.3.4           memoise_1.1.0          MASS_7.3-51.1          foreign_0.8-71         tools_3.5.1            data.table_1.12.0     
[37] stringr_1.4.0          munsell_0.5.0          locfit_1.5-9.1         cluster_2.0.7-1        AnnotationDbi_1.44.0   compiler_3.5.1        
[43] caTools_1.17.1.2       rlang_0.3.2            RCurl_1.95-4.12        rstudioapi_0.10        htmlwidgets_1.3        labeling_0.3          
[49] bitops_1.0-6           base64enc_0.1-3        gtable_0.3.0           DBI_1.0.0              gridExtra_2.3          knitr_1.22            
[55] bit_1.1-14             Hmisc_4.2-0            KernSmooth_2.23-15     stringi_1.4.3          Rcpp_1.0.1             geneplotter_1.60.0    
[61] rpart_4.1-13           acepack_1.4.1          xfun_0.5              
>

This is just what you would expect from the documentation. The alpha you provide to results() informs the independent filtering, and giving it the alpha you intend to use will give you the most rejections.