Hi all!
Using DEseq2 (v 1.30.0) I try to analyze a "complex" data set with 2 factors (A and B) harboring different levels. Factor A (named hereafter "line") has two levels (infected/non-infected) and factor 2 (named hereafter "group") has 4 (non-infected, mono-infected with 1, mono-infected with 2 and bi-infected with 1 and 2 at the same time). According to phenotype data, what drives my phenotype is A:B interaction. Thereby, I try to find the genes that are explained by interaction A:B. I guess that the trick is to use contrasts methods, but I am positively lost between all the lists of genes to find the one I am interested in (if there is only one), in addition to the fact that DEseq2 asks for a "reference" level which does not make really sense in an interaction model. Do you have any clues to help me? When I use the #resultsNames function, where should I look? What would be the correct coding for contrasts?
Thanks a lot for your help. -Vincent
Code should be placed in three backticks as shown below
cts <- read.delim("Pupalcountstotal.txt", header=TRUE, row.names="GeneID")
coldata <- read.delim("design.txt", header=TRUE) #24 obs
coldata$group <- as.factor(coldata$group)
coldata$line <- as.factor(coldata$line)
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata,
design= ~ group+line+group:line)
#remove <100 counts total per transcript
dds <- dds[ rowSums(counts(dds)) > 100, ] #reste 13169
#relevel factors
dds$group <- relevel(dds$group, ref = "GF")
dds$line <- relevel(dds$line, ref = "wolb")
dds <- DESeq(dds)
resultsNames(dds)
[1] "Intercept" "group_AP_vs_GF" "group_BI_vs_GF" "group_LP_vs_GF" "line_tet_vs_wolb" "groupAP.linetet" "groupBI.linetet"
[8] "groupLP.linetet"
sessionInfo( )
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] SARTools_1.7.3 kableExtra_1.3.1 emmeans_1.5.3 devtools_2.3.2 usethis_2.0.0
[6] edgeR_3.32.0 limma_3.46.0 DESeq2_1.30.0 SummarizedExperiment_1.20.0 Biobase_2.50.0
[11] MatrixGenerics_1.2.0 matrixStats_0.57.0 GenomicRanges_1.42.0 GenomeInfoDb_1.26.2 IRanges_2.24.1
[16] S4Vectors_0.28.1 BiocGenerics_0.36.0 gprofiler2_0.2.0 UpSetR_1.4.0 coxme_2.2-16
[21] bdsmatrix_1.3-4 scales_1.1.1 viridis_0.5.1 viridisLite_0.3.0 car_3.0-10
[26] carData_3.0-4 GGally_2.1.0 survival_3.2-7 lme4_1.1-26 Matrix_1.2-18
[31] gplots_3.1.1 knitr_1.30 reshape2_1.4.4 gridExtra_2.3 stringr_1.4.0
[36] plyr_1.8.6 ggplot2_3.3.3 MASS_7.3-53
loaded via a namespace (and not attached):
[1] readxl_1.3.1 lazyeval_0.2.2 splines_4.0.3 BiocParallel_1.24.1 TH.data_1.0-10 digest_0.6.27
[7] htmltools_0.5.0 fansi_0.4.1 magrittr_2.0.1 memoise_1.1.0 openxlsx_4.2.3 remotes_2.2.0
[13] annotate_1.68.0 sandwich_3.0-0 prettyunits_1.1.1 colorspace_2.0-0 ggrepel_0.9.0 rvest_0.3.6
[19] blob_1.2.1 haven_2.3.1 xfun_0.20 dplyr_1.0.2 callr_3.5.1 crayon_1.3.4
[25] RCurl_1.98-1.2 jsonlite_1.7.2 genefilter_1.72.0 zoo_1.8-8 glue_1.4.2 gtable_0.3.0
[31] zlibbioc_1.36.0 XVector_0.30.0 webshot_0.5.2 DelayedArray_0.16.0 pkgbuild_1.2.0 abind_1.4-5
[37] mvtnorm_1.1-1 DBI_1.1.0 Rcpp_1.0.5 xtable_1.8-4 foreign_0.8-81 bit_4.0.4
[43] htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3
[49] reshape_0.8.8 XML_3.99-0.5 locfit_1.5-9.4 labeling_0.4.2 tidyselect_1.1.0 rlang_0.4.10
[55] AnnotationDbi_1.52.0 munsell_0.5.0 cellranger_1.1.0 tools_4.0.3 cli_2.2.0 generics_0.1.0
[61] RSQLite_2.2.1 ggdendro_0.1.22 evaluate_0.14 processx_3.4.5 bit64_4.0.5 fs_1.5.0
[67] zip_2.1.1 caTools_1.18.0 purrr_0.3.4 nlme_3.1-151 xml2_1.3.2 compiler_4.0.3
[73] rstudioapi_0.13 plotly_4.9.2.2 curl_4.3 testthat_3.0.1 tibble_3.0.4 statmod_1.4.35
[79] geneplotter_1.68.0 stringi_1.5.3 ps_1.5.0 desc_1.2.0 forcats_0.5.0 lattice_0.20-41
[85] nloptr_1.2.2.2 vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0 estimability_1.3 data.table_1.13.6
[91] bitops_1.0-6 R6_2.5.0 KernSmooth_2.23-18 rio_0.5.16 sessioninfo_1.1.1 codetools_0.2-18
[97] boot_1.3-25 gtools_3.8.2 assertthat_0.2.1 pkgload_1.1.0 rprojroot_2.0.2 withr_2.3.0
[103] multcomp_1.4-15 GenomeInfoDbData_1.2.4 hms_0.5.3 tidyr_1.1.2 coda_0.19-4 minqa_1.2.4
[109] rmarkdown_2.6 tinytex_0.28
