Hello. I am trying to run lfcShrink() on one of the contrasts created with the results() function but that contrast does not apprear in the resultsNames(dds), so the lfcShrink() gives me an error. Thanks for any help in advance!

dds <- DESeqDataSetFromMatrix( 
  countData = countdata, 
  colData = coldata, 
  design = ~ replicado + condition) 
dds
saveRDS(dds, "dds")

keep <- rowSums(counts(dds)) >= 10 
dds <- dds[keep,]

dds$condition <- factor(dds$condition, levels = c("UNT","E2","GW","E2GW"))
dds$condition

dds <- DESeq(dds)

SaveUPandDown = function(a,b){
  comp <- results(dds, contrast = c("condition", a, b), alpha=0.05)
  #write.csv(comp, paste0(a,"vs",b,".csv"))
  print(summary(comp))
  comp_sinNAs = comp[complete.cases(comp[,"padj"]),]

  comp_up = comp_sinNAs[comp_sinNAs$log2FoldChange>0 & comp_sinNAs$padj<0.05,]
  comp_up$ENSID = rownames(comp_up)
  comp_up = left_join(data.frame(comp_up), IDs_table, by="ENSID")
  comp_up = comp_up[, c("ENSID","SYMBOL","baseMean","log2FoldChange","lfcSE","stat","pvalue","padj")]
  comp_up = arrange(comp_up, desc(log2FoldChange))
  #write.csv(comp_up, paste0(a,"vs",b,"_up.csv"))

  comp_down = comp_sinNAs[comp_sinNAs$log2FoldChange<0 & comp_sinNAs$padj<0.05,]
  comp_down$ENSID = rownames(comp_down)
  comp_down = left_join(data.frame(comp_down), IDs_table, by="ENSID")
  comp_down = comp_down[, c("ENSID","SYMBOL","baseMean","log2FoldChange","lfcSE","stat","pvalue","padj")]
  comp_down = arrange(comp_down, log2FoldChange)
  #write.csv(comp_down, paste0(a,"vs",b,"_down.csv"))

  return(list("ALL"=comp , "UP"= comp_up, "DOWN"=comp_down))

}

E2vsUNT = SaveUPandDown("E2", "UNT")
GWvsUNT = SaveUPandDown("GW", "UNT")
E2GWvsUNT = SaveUPandDown("E2GW", "UNT")
E2GWvsE2 = SaveUPandDown("E2GW", "E2")
E2GWvsGW = SaveUPandDown("E2GW", "GW")

resultsNames(dds)
# [1] "Intercept"             "replicado_2_vs_1"      "condition_E2_vs_UNT"   "condition_GW_vs_UNT"   "condition_E2GW_vs_UNT"

E2GWvsE2_LFC <- lfcShrink(dds, coef="condition_E2GW_vs_E2", type="apeglm")
#Error in lfcShrink(dds, coef = "condition_E2GW_vs_E2", type = "apeglm") : 
# coef %in% resultsNamesDDS is not TRUE


with(E2GWvsE2$ALL, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot", xlim=c(-3,3)))
with(subset(E2GWvsE2$ALL, padj<.05 ), points(log2FoldChange, -log10(padj), pch=20, col="blue"))

Also, the shape of volcano plot of of E2GWvsE2 is a bit strange, with some genes with really large values of logFC. That is why I wanted to try the plot with the shrunken values, to see if the plot improves.

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                 purrr_0.3.4                
 [6] readr_2.1.2                 tidyr_1.2.0                 tibble_3.1.8                ggplot2_3.3.6               tidyverse_1.3.2            
[11] DESeq2_1.36.0               SummarizedExperiment_1.26.1 Biobase_2.56.0              MatrixGenerics_1.8.1        matrixStats_0.62.0         
[16] GenomicRanges_1.48.0        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     httr_1.4.4            
 [7] numDeriv_2016.8-1.1    tools_4.2.1            backports_1.4.1        utf8_1.2.2             R6_2.5.1               DBI_1.1.3             
[13] colorspace_2.0-3       apeglm_1.18.0          withr_2.5.0            tidyselect_1.1.2       bit_4.0.4              compiler_4.2.1        
[19] cli_3.3.0              rvest_1.0.3            xml2_1.3.3             DelayedArray_0.22.0    labeling_0.4.2         scales_1.2.1          
[25] mvtnorm_1.1-3          genefilter_1.78.0      digest_0.6.29          XVector_0.36.0         pkgconfig_2.0.3        bbmle_1.0.25          
[31] dbplyr_2.2.1           fastmap_1.1.0          rlang_1.0.4            readxl_1.4.1           rstudioapi_0.14        RSQLite_2.2.16        
[37] farver_2.1.1           generics_0.1.3         jsonlite_1.8.0         BiocParallel_1.30.3    googlesheets4_1.0.1    RCurl_1.98-1.8        
[43] magrittr_2.0.3         GenomeInfoDbData_1.2.8 Matrix_1.4-1           Rcpp_1.0.9             munsell_0.5.0          fansi_1.0.3           
[49] lifecycle_1.0.1        stringi_1.7.8          MASS_7.3-58            zlibbioc_1.42.0        plyr_1.8.7             grid_4.2.1            
[55] blob_1.2.3             parallel_4.2.1         bdsmatrix_1.3-6        crayon_1.5.1           lattice_0.20-45        Biostrings_2.64.1     
[61] haven_2.5.1            splines_4.2.1          annotate_1.74.0        hms_1.1.2              KEGGREST_1.36.3        locfit_1.5-9.6        
[67] pillar_1.8.1           geneplotter_1.74.0     codetools_0.2-18       admisc_0.29            reprex_2.0.2           XML_3.99-0.10         
[73] glue_1.6.2             renv_0.15.5            modelr_0.1.9           png_0.1-7              vctrs_0.4.1            tzdb_0.3.0            
[79] cellranger_1.1.0       gtable_0.3.0           assertthat_0.2.1       emdbook_1.3.12         cachem_1.0.6           xtable_1.8-4          
[85] broom_1.0.0            coda_0.19-4            survival_3.4-0         googledrive_2.0.0      gargle_1.2.0           AnnotationDbi_1.58.0  
[91] memoise_2.0.1          ellipsis_0.3.2

Don't worry about resultsNames not showing the exact contrast you want. It's just showing the ones against the reference level, but you can still use contrast to compare what you want.