DESeq2 paired multifactor test
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Entering edit mode
ijvechetti ▴ 10
@ijvechetti-20701
Last seen 2.0 years ago
United States

Hi, I have an experimental design where I have groups (sham and SA) and treatments (control and treated) with paired samples. It looks like this:

Samples Group Treatment Mice Mice.nested
A1 Sham Control A A
----------- --------- ------------- ------- ----------------
A2 Sham Control B B
----------- --------- ------------- ------- ----------------
A3 Sham Control C C
----------- --------- ------------- ------- ----------------
A4 Sham Control D D
----------- --------- ------------- ------- ----------------
A5 Sham Control E E
----------- --------- ------------- ------- ----------------
B1 Sham Treated A A
----------- --------- ------------- ------- ----------------
B2 Sham Treated B B
----------- --------- ------------- ------- ----------------
B3 Sham Treated C C
----------- --------- ------------- ------- ----------------
B4 Sham Treated D D
----------- --------- ------------- ------- ----------------
B5 Sham Treated E E
----------- --------- ------------- ------- ----------------
C1 SA Control F A
----------- --------- ------------- ------- ----------------
C2 SA Control G B
----------- --------- ------------- ------- ----------------
C3 SA Control H C
----------- --------- ------------- ------- ----------------
C4 SA Control I D
----------- --------- ------------- ------- ----------------
C5 SA Control J E
----------- --------- ------------- ------- ----------------
D1 SA Treated F A
----------- --------- ------------- ------- ----------------
D2 SA Treated G B
----------- --------- ------------- ------- ----------------
D3 SA Treated H C
----------- --------- ------------- ------- ----------------
D4 SA Treated I D
----------- --------- ------------- ------- ----------------
D5 SA Treated J E
----------- --------- ------------- ------- ----------------

I would like to know what are the differentially expressed genes within groups and between treatments, and I think the following would give me that:

design= ~ Group + Group:Mice.nested + Group:Treatment

Extract Sham-control vs Sham-treated

results(dds, name = "GroupSham.TreatmentTreated", test="Wald", alpha=0.05)

Extract SA-control vs SA-treated

results(dds, name = "GroupSa.TreatmentTreated", test="Wald", alpha=0.05)

Assuming everything I did at this point is correct (which could not be), how would I extract the genes that are changing between groups within treatments (Sham-vs-SA_control and Sham-vs-SA_treated) Thanks in advance

```> sessionInfo() R version 4.2.1 (2022-06-23 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 22621)

Matrix products: default

locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8

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

other attached packages: [1] DESeq2_1.36.0 SummarizedExperiment_1.26.1 Biobase_2.56.0 MatrixGenerics_1.8.1 matrixStats_0.62.0
[6] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4 IRanges_2.30.1 S4Vectors_0.34.0 BiocGenerics_0.42.0

loaded via a namespace (and not attached): [1] Rcpp_1.0.9 locfit_1.5-9.6 lattice_0.20-45 png_0.1-7 Biostrings_2.64.1 assertthat_0.2.1
[7] utf8_1.2.2 R6_2.5.1 RSQLite_2.2.18 httr_1.4.4 ggplot2_3.3.6 pillar_1.8.1
[13] zlibbioc_1.42.0 rlang_1.0.6 rstudioapi_0.14 annotate_1.74.0 blob_1.2.3 Matrix_1.4-1
[19] splines_4.2.1 BiocParallel_1.30.4 geneplotter_1.74.0 RCurl_1.98-1.9 bit_4.0.4 munsell_0.5.0
[25] DelayedArray_0.22.0 compiler_4.2.1 pkgconfig_2.0.3 tidyselect_1.2.0 KEGGREST_1.36.3 tibble_3.1.8
[31] GenomeInfoDbData_1.2.8 codetools_0.2-18 XML_3.99-0.11 fansi_1.0.3 crayon_1.5.2 dplyr_1.0.10
[37] bitops_1.0-7 grid_4.2.1 xtable_1.8-4 gtable_0.3.1 lifecycle_1.0.3 DBI_1.1.3
[43] magrittr_2.0.3 scales_1.2.1 cli_3.4.1 cachem_1.0.6 XVector_0.36.0 genefilter_1.78.0
[49] generics_0.1.3 vctrs_0.4.2 cowplot_1.1.1 RColorBrewer_1.1-3 tools_4.2.1 bit64_4.0.5
[55] glue_1.6.2 parallel_4.2.1 fastmap_1.1.0 survival_3.3-1 AnnotationDbi_1.58.0 colorspace_2.0-3
[61] memoise_2.0.1 ```

DESeq2 StatisticalMethod • 664 views
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Entering edit mode
@mikelove
Last seen 4 hours ago
United States

Sorry for the delay in reply, but I don't have sufficient time to answer statistical design questions, I have to restrict myself to software related issues on the support site. I recommend to find a local statistician or someone familiar with linear models in R to work on the statistical design.

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