Hi, I am curious about prefiltering with DESeq2. I understand from this site and reading the DESeq2 vignette that prefiletering is really unnecessary as DESeq2 has a stringent filtering that it does. However, I'm seeing better results with filtering (much higher # DEGs with sig adj p-value after DESeq2).

I have a one factor design and three samples in two different groups. I do have a sample that's a bit noisier than the others but doesn't seem there is any significant grounds to remove it. With prefiltering, I find the highest number of significant DEGs when I filter out anything below 50 normalized counts for each of the six samples.

My question is, is this wrong? Is creating such a high prefiltering cutoff affecting my false positivity rate somehow? Though I love that I get triple the amount of significant DEGs, I don't want to go down the wrong path in my subsequent pathway analsyis due to a prefiltering error.

Thank you for your time.

My code

#dds <- DESeqDataSetFromMatrix(countData=countdata, colData=sampleData, design = ~ condition, tidy= FALSE)
#dds <- estimateSizeFactors(dds)
#keep <- rowSums(counts(dds, normalized=TRUE) >= 50 ) >=6
#dds <- dds[keep,]
#dds <- DESeq(dds)



sessionInfo( )
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] dplyr_1.0.8                 DESeq2_1.34.0              
 [3] SummarizedExperiment_1.24.0 Biobase_2.54.0             
 [5] MatrixGenerics_1.6.0        matrixStats_0.61.0         
 [7] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
 [9] IRanges_2.28.0              S4Vectors_0.32.3           
[11] BiocGenerics_0.40.0         BiocManager_1.30.16        

loaded via a namespace (and not attached):
 [1] locfit_1.5-9.4         Rcpp_1.0.8             lattice_0.20-45       
 [4] png_0.1-7              Biostrings_2.62.0      assertthat_0.2.1      
 [7] utf8_1.2.2             R6_2.5.1               RSQLite_2.2.9         
[10] httr_1.4.2             ggplot2_3.3.5          pillar_1.7.0          
[13] zlibbioc_1.40.0        rlang_1.0.1            rstudioapi_0.13       
[16] annotate_1.72.0        blob_1.2.2             Matrix_1.3-4          
[19] splines_4.1.2          BiocParallel_1.28.3    geneplotter_1.72.0    
[22] RCurl_1.98-1.6         bit_4.0.4              munsell_0.5.0         
[25] DelayedArray_0.20.0    compiler_4.1.2         pkgconfig_2.0.3       
[28] tidyselect_1.1.2       KEGGREST_1.34.0        tibble_3.1.6          
[31] GenomeInfoDbData_1.2.7 XML_3.99-0.8           fansi_1.0.2           
[34] crayon_1.5.0           bitops_1.0-7           grid_4.1.2            
[37] xtable_1.8-4           gtable_0.3.0           lifecycle_1.0.1       
[40] DBI_1.1.2              magrittr_2.0.2         scales_1.1.1          
[43] cli_3.1.1              cachem_1.0.6           XVector_0.34.0        
[46] genefilter_1.76.0      ellipsis_0.3.2         vctrs_0.3.8           
[49] generics_0.1.2         RColorBrewer_1.1-2     tools_4.1.2           
[52] bit64_4.0.5            glue_1.6.1             purrr_0.3.4           
[55] parallel_4.1.2         fastmap_1.1.0          survival_3.2-13       
[58] AnnotationDbi_1.56.2   colorspace_2.0-2       memoise_2.0.1         
>

Pre-filtering is fine, so you can go with your current settings and report these results. Probably what is happening is that you have a lot of high dispersion low count features that are changing the dispersion trend. It's reasonable to filter in this case. In the user guide we should really make it clear that filtering is not necessary for consideration of multiple testing burden, because of independent filtering.