Hello! I calculated DGE with or without filtering by LFC. The thing that concerns me is that, in the results table, even though the baseMean, the log2FoldChange, the lfcSE, the stat value is different, and because of that the pvalue and the padj are also different.

Here is an example of one gene. The first row is the result of results(dds, contrast = c("condition", "E2", "UNT"), alpha=0.05) and the second row is results(dds, contrast = c("condition", "E2", "UNT"), alpha=0.05, lfcThreshold = 1)

ENSID            SYMBOL     baseMean         log2FoldChange         lfcSE               stat                  pvalue                 padj
ENSG00000103257 SLC7A5  35252.0316507722    1.14628745148079    0.0571527021487077  20.0565749017119    1.76854266047002E-89    5.25586898452226E-87
ENSG00000103257 SLC7A5  35252.0316507722    1.14628745148079    0.0571527021487077  2.55958941539029    0.0104795893216153         1


sessionInfo( )
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=es_AR.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=es_AR.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=es_AR.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_AR.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] venn_1.11                   forcats_0.5.2               stringr_1.4.1               dplyr_1.0.9                
 [5] purrr_0.3.4                 readr_2.1.2                 tidyr_1.2.0                 tibble_3.1.8               
 [9] ggplot2_3.3.6               tidyverse_1.3.2             DESeq2_1.36.0               SummarizedExperiment_1.26.1
[13] Biobase_2.56.0              MatrixGenerics_1.8.1        matrixStats_0.62.0          GenomicRanges_1.48.0       
[17] GenomeInfoDb_1.32.3         IRanges_2.30.1              S4Vectors_0.34.0            BiocGenerics_0.42.0        

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           fs_1.5.2               lubridate_1.8.0        bit64_4.0.5            RColorBrewer_1.1-3    
 [6] httr_1.4.4             tools_4.2.1            backports_1.4.1        utf8_1.2.2             R6_2.5.1              
[11] DBI_1.1.3              colorspace_2.0-3       withr_2.5.0            tidyselect_1.1.2       bit_4.0.4             
[16] compiler_4.2.1         cli_3.3.0              rvest_1.0.3            xml2_1.3.3             DelayedArray_0.22.0   
[21] labeling_0.4.2         scales_1.2.1           genefilter_1.78.0      digest_0.6.29          XVector_0.36.0        
[26] pkgconfig_2.0.3        dbplyr_2.2.1           fastmap_1.1.0          limma_3.52.2           rlang_1.0.4           
[31] readxl_1.4.1           rstudioapi_0.14        RSQLite_2.2.16         farver_2.1.1           generics_0.1.3        
[36] jsonlite_1.8.0         BiocParallel_1.30.3    googlesheets4_1.0.1    RCurl_1.98-1.8         magrittr_2.0.3        
[41] GenomeInfoDbData_1.2.8 Matrix_1.4-1           Rcpp_1.0.9             munsell_0.5.0          fansi_1.0.3           
[46] lifecycle_1.0.1        stringi_1.7.8          zlibbioc_1.42.0        grid_4.2.1             blob_1.2.3            
[51] parallel_4.2.1         crayon_1.5.1           lattice_0.20-45        Biostrings_2.64.1      haven_2.5.1           
[56] splines_4.2.1          annotate_1.74.0        hms_1.1.2              KEGGREST_1.36.3        locfit_1.5-9.6        
[61] pillar_1.8.1           admisc_0.29            geneplotter_1.74.0     codetools_0.2-18       reprex_2.0.2          
[66] XML_3.99-0.10          glue_1.6.2             renv_0.15.5            modelr_0.1.9           png_0.1-7             
[71] vctrs_0.4.1            tzdb_0.3.0             cellranger_1.1.0       gtable_0.3.0           assertthat_0.2.1      
[76] cachem_1.0.6           xtable_1.8-4           broom_1.0.0            survival_3.4-0         googledrive_2.0.0     
[81] gargle_1.2.0           AnnotationDbi_1.58.0   memoise_2.0.1          ellipsis_0.3.2

You're testing 2 different null hypotheses, which is why the statistics are different:

1. results(dds, contrast = c("condition", "E2", "UNT"), alpha=0.05) is testing a null hypothesis of logFC = 0 vs. an alternative hypothesis of logFC != 0.
2. results(dds, contrast = c("condition", "E2", "UNT"), alpha=0.05, lfcThreshold = 1) is testing a null hypothesis of |logFC| <= 1 vs. an alternative hypothesis of |logFC| > 1

See also the altHypothesis argument of results() for other tweaks that can be made to the definition of the alternative hypothesis.