Hi,

I have done a differential expression analysis using DESeq2 and am now trying to visualize my results with a volcano plot. In my plot I would like all DE genes with log2FC >2 and padj < 0.001, and the top 20 genes based on log2FC to be labeled. However, it is not the genes with highest absolute log2FC that get labeled, some genes seem to get labeled based on their lower p-value. Is it possible to adjust the settings for this? Or will I have to label the specific genes using 'selectLab'?

Thanks in advance!

//Sara

        EnhancedVolcano(res,
    +                 lab = rownames(res),
    +                 x = 'log2FoldChange',
    +                 y = 'pvalue',
    +                 title = 'Volcano plot',
    +                 pCutoff = 6e-8,
    +                 FCcutoff = 2,
    +                 pointSize = 3.0,
    +                 labSize = 6.0)

sessionInfo( )

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Matrix products: default


locale:
[1] LC_COLLATE=English_Sweden.utf8  LC_CTYPE=English_Sweden.utf8    LC_MONETARY=English_Sweden.utf8
[4] LC_NUMERIC=C                    LC_TIME=English_Sweden.utf8    

time zone: Europe/Stockholm
tzcode source: internal

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

other attached packages:
 [1] EnhancedVolcano_1.18.0      dplyr_1.1.2                 ggrepel_0.9.4               DESeq2_1.40.2              
 [5] SummarizedExperiment_1.30.2 Biobase_2.60.0              MatrixGenerics_1.12.2       matrixStats_1.0.0          
 [9] GenomicRanges_1.52.0        GenomeInfoDb_1.36.1         IRanges_2.34.1              S4Vectors_0.38.1           
[13] BiocGenerics_0.46.0         ggplot2_3.4.3              

loaded via a namespace (and not attached):
 [1] Matrix_1.6-1.1          gtable_0.3.4            compiler_4.3.1          crayon_1.5.2            Rcpp_1.0.11            
 [6] tidyselect_1.2.0        bitops_1.0-7            parallel_4.3.1          scales_1.2.1            BiocParallel_1.34.2    
[11] lattice_0.21-8          R6_2.5.1                XVector_0.40.0          labeling_0.4.3          S4Arrays_1.0.4         
[16] generics_0.1.3          tibble_3.2.1            DelayedArray_0.26.6     munsell_0.5.0           GenomeInfoDbData_1.2.10
[21] pillar_1.9.0            rlang_1.1.1             utf8_1.2.3              cli_3.6.1               withr_2.5.1            
[26] magrittr_2.0.3          zlibbioc_1.46.0         locfit_1.5-9.8          grid_4.3.1              rstudioapi_0.15.0      
[31] lifecycle_1.0.3         vctrs_0.6.3             glue_1.6.2              farver_2.1.1            codetools_0.2-19       
[36] RCurl_1.98-1.12         fansi_1.0.4             colorspace_2.1-0        tools_4.3.1             pkgconfig_2.0.3

You could just try this to see for yourself. I just did, using the example data.

example(EnhancedVolcano)
## now rerun the last bit of code, with selectLab specified
 EnhancedVolcano(res,
       lab = rownames(res),
       x = 'log2FoldChange',
       y = 'pvalue',
       pCutoff = 10e-4,
       FCcutoff = 1.333,
       xlim = c(-5.5, 5.5),
       ylim = c(0, -log10(10e-12)),
       pointSize = 1.5,
       labSize = 2.5,
       title = 'DESeq2 results',
       subtitle = 'Differential expression',
       caption = 'FC cutoff, 1.333; p-value cutoff, 10e-4',
       legendPosition = "right",
       legendLabSize = 14,
       col = c('grey30', 'forestgreen', 'royalblue', 'red2'),
       colAlpha = 0.9,
       drawConnectors = TRUE,
       hline = c(10e-8),
       widthConnectors = 0.5, selectLab = head(row.names(res)))

And there are only six genes labeled. So the answer is yes.