Dispersion plot for counts data

Hi,

I'm using DESeq2 to analyze counts data that doesn't come from standard RNA-seq. In this case, the data consist of artificial sequences expressed in samples, rather than genes.

DESeq2 automatically chose a local regression fit for the dispersion estimates instead of a parametric fit, which makes sense. The dispersion plot looks fine overall, but I noticed that dispersion does not decrease with increasing counts, unlike what is typically seen in gene-level RNA-seq data.

Is this behavior expected for non-gene count data, and does it affect how I should interpret the DESeq2 results?

Thanks!

sessionInfo( )

R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

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

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] DESeq2_1.48.0               SummarizedExperiment_1.38.1 Biobase_2.68.0             
 [4] MatrixGenerics_1.20.0       matrixStats_1.5.0           GenomicRanges_1.60.0       
 [7] GenomeInfoDb_1.44.0         IRanges_2.42.0              S4Vectors_0.46.0           
[10] BiocGenerics_0.54.0         generics_0.1.3              lubridate_1.9.4            
[13] forcats_1.0.0               stringr_1.5.1               dplyr_1.1.4                
[16] purrr_1.0.4                 readr_2.1.5                 tidyr_1.3.1                
[19] tibble_3.2.1                ggplot2_3.5.2               tidyverse_2.0.0            

loaded via a namespace (and not attached):
 [1] gtable_0.3.6            xfun_0.52               lattice_0.22-7          tzdb_0.5.0             
 [5] vctrs_0.6.5             tools_4.5.1             parallel_4.5.1          pkgconfig_2.0.3        
 [9] Matrix_1.7-3            RColorBrewer_1.1-3      lifecycle_1.0.4         GenomeInfoDbData_1.2.14
[13] compiler_4.5.1          farver_2.1.2            codetools_0.2-20        htmltools_0.5.8.1      
[17] yaml_2.3.10             pillar_1.10.2           crayon_1.5.3            BiocParallel_1.42.0    
[21] DelayedArray_0.34.1     abind_1.4-8             tidyselect_1.2.1        locfit_1.5-9.12        
[25] digest_0.6.37           stringi_1.8.7           labeling_0.4.3          cowplot_1.1.3          
[29] fastmap_1.2.0           grid_4.5.1              cli_3.6.5               SparseArray_1.8.0      
[33] magrittr_2.0.3          S4Arrays_1.8.0          withr_3.0.2             scales_1.4.0           
[37] UCSC.utils_1.4.0        bit64_4.6.0-1           timechange_0.3.0        rmarkdown_2.29         
[41] XVector_0.48.0          httr_1.4.7              bit_4.6.0               hms_1.1.3              
[45] evaluate_1.0.3          knitr_1.50              rlang_1.1.6             Rcpp_1.0.14            
[49] glue_1.8.0              rstudioapi_0.17.1       vroom_1.6.5             jsonlite_2.0.0         
[53] R6_2.6.1