DESeq2 effect of interaction when using a grouping variable
Entering edit mode
Last seen 5 days ago
United States

Hi, I am interested in the interaction effect using a grouping variable. For example, using a classic DESeq2 example, is the difference in conditions A vs B differs by genotypes I vs II?

dds <- makeExampleDESeqDataSet(n=100,m=18)
dds$genotype <- factor(rep(rep(c("I","II","III"),each=3),2))
dds$group <- factor(paste0(dds$genotype, "_", dds$condition))
design(dds) <- ~  group
dds <- DESeq(dds)

# Comparison of interest: (I_A vs I_B) vs (II_A vs_B)
# Code below doesn't work, but is there a solution for this? 
results(dds, contrast=list(c("group", "I_A", "I_B"),
                           c("group", "II_A", "II_B")))

sessionInfo( )
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 

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

other attached packages:
 [1] biomaRt_2.50.3              ggplot2_3.3.6               edgeR_3.36.0                DESeq2_1.34.0              
 [5] SummarizedExperiment_1.24.0 Biobase_2.54.0              MatrixGenerics_1.6.0        matrixStats_0.62.0         
 [9] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1         IRanges_2.28.0              S4Vectors_0.32.4           
[13] BiocGenerics_0.40.0         limma_3.50.3                stringr_1.4.1               readxl_1.4.1               
[17] tximport_1.22.0             plyr_1.8.7                 

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           bit64_4.0.5            filelock_1.0.2         RColorBrewer_1.1-3     progress_1.2.2        
 [6] httr_1.4.4             tools_4.1.2            utf8_1.2.2             R6_2.5.1               DBI_1.1.3             
[11] colorspace_2.0-3       withr_2.5.0            tidyselect_1.1.2       prettyunits_1.1.1      bit_4.0.4             
[16] curl_4.3.2             compiler_4.1.2         textshaping_0.3.6      cli_3.4.1              xml2_1.3.3            
[21] DelayedArray_0.20.0    labeling_0.4.2         scales_1.2.1           genefilter_1.76.0      rappdirs_0.3.3        
[26] systemfonts_1.0.4      digest_0.6.29          rmarkdown_2.16         XVector_0.34.0         pkgconfig_2.0.3       
[31] htmltools_0.5.3        dbplyr_2.2.1           fastmap_1.1.0          rlang_1.0.6            rstudioapi_0.14       
[36] RSQLite_2.2.18         farver_2.1.1           generics_0.1.3         BiocParallel_1.28.3    dplyr_1.0.10          
[41] RCurl_1.98-1.9         magrittr_2.0.3         GenomeInfoDbData_1.2.7 Matrix_1.5-1           Rcpp_1.0.9            
[46] munsell_0.5.0          fansi_1.0.3            lifecycle_1.0.2        stringi_1.7.8          yaml_2.3.5            
[51] zlibbioc_1.40.0        BiocFileCache_2.2.1    grid_4.1.2             blob_1.2.3             parallel_4.1.2        
[56] crayon_1.5.2           lattice_0.20-45        Biostrings_2.62.0      splines_4.1.2          annotate_1.72.0       
[61] hms_1.1.2              KEGGREST_1.34.0        locfit_1.5-9.6         knitr_1.40             pillar_1.8.1          
[66] geneplotter_1.72.0     XML_3.99-0.11          glue_1.6.2             evaluate_0.16          png_0.1-7             
[71] vctrs_0.4.2            cellranger_1.1.0       gtable_0.3.1           purrr_0.3.4            assertthat_0.2.1      
[76] cachem_1.0.6           xfun_0.33              xtable_1.8-4           ragg_1.2.3             survival_3.4-0        
[81] tibble_3.1.8           AnnotationDbi_1.56.2   memoise_2.0.1          statmod_1.4.37         ellipsis_0.3.2

Thank you!

DESeq2 • 70 views
Entering edit mode
swbarnes2 ★ 1.2k
Last seen 21 hours ago
San Diego

Keep reading the interaction section of the vignette. I think you do not want the group method this time. I think you want Genotype + Condition + Genotype:Condition design.


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