I am trying to perform DGE analysis and to do so I want to calculate the Shrink log2 fold changes with the lfcShrink considering an alpha value of 0.05.

When I perform the analysis without considering the lfcShrink the code is as follows and works nicely:

res_wt_glu_vs_nocar <- results(dds, contrast = c("conditions", "wild_type_114_2_no_carbon", "wild_type_114_2_glucose"), alpha = 0.05)
summary(res_wt_glu_vs_nocar)

However, when wanting to perform the contrast calculating rhe Shrink log2 fold changes I get the results for default alpha 0.1 and not the 0.05:

res <- lfcShrink(dds, contrast = c("conditions", "wild_type_114_2_no_carbon", "wild_type_114_2_glucose"), type = "ashr", alpha = 0.05)
summary(res)
Then I get:

> summary(res)

out of 9010 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 3516, 39%
LFC < 0 (down)     : 3283, 36%
outliers [1]       : 1, 0.011%
low counts [2]     : 0, 0%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

```r

# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session

Should I instead perform the analysis as follows (first the non-lfcShrink and then perform the lfcShrink) if I want to consider alpha 0.05 and not the default?

res <- lfcShrink(dds, res=res_wt_glu_vs_nocar, type = "ashr")

I thought the ashr method allowed the perform the DEG analysis directly calculating the shrinking values without performing the non-shrink fc analysis before.

sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Ventura 13.6.7

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] ashr_2.2-63                 DESeq2_1.36.0               SummarizedExperiment_1.26.1 Biobase_2.56.0              MatrixGenerics_1.8.1        matrixStats_1.2.0          
 [7] GenomicRanges_1.48.0        GenomeInfoDb_1.32.4         IRanges_2.30.1              S4Vectors_0.34.0            BiocGenerics_0.42.0        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.11            locfit_1.5-9.8         invgamma_1.1           lattice_0.22-5         png_0.1-8              Biostrings_2.64.1      utf8_1.2.4            
 [8] truncnorm_1.0-9        R6_2.5.1               RSQLite_2.3.4          httr_1.4.7             ggplot2_3.5.1          pillar_1.9.0           zlibbioc_1.42.0       
[15] rlang_1.1.2            irlba_2.3.5.1          rstudioapi_0.17.1      annotate_1.74.0        blob_1.2.4             Matrix_1.5-4.1         splines_4.2.1         
[22] BiocParallel_1.30.4    geneplotter_1.74.0     mixsqp_0.3-54          RCurl_1.98-1.13        bit_4.0.5              munsell_0.5.1          DelayedArray_0.22.0   
[29] compiler_4.2.1         pkgconfig_2.0.3        SQUAREM_2021.1         tidyselect_1.2.0       KEGGREST_1.36.3        tibble_3.2.1           GenomeInfoDbData_1.2.8
[36] codetools_0.2-20       XML_3.99-0.16          fansi_1.0.6            crayon_1.5.3           dplyr_1.1.4            bitops_1.0-7           grid_4.2.1            
[43] xtable_1.8-4           gtable_0.3.6           lifecycle_1.0.4        DBI_1.2.3              magrittr_2.0.3         scales_1.3.0           cli_3.6.2             
[50] cachem_1.0.8           XVector_0.36.0         genefilter_1.78.0      vctrs_0.6.5            generics_0.1.3         RColorBrewer_1.1-3     tools_4.2.1           
[57] bit64_4.0.5            glue_1.6.2             parallel_4.2.1         fastmap_1.1.1          survival_3.5-7         AnnotationDbi_1.58.0   colorspace_2.1-0      
[64] memoise_2.0.1

summary() is just printing a particular alpha, but you can do:

summary(res, alpha = ...)