Dear all,
I am working with count data from a sequencing experiment and need some help with the design.
I have 4 treatment groups which can be accounted by a factorial design. Defining 2 variables, A and B, the control group would be 1,1; the A group would be 2,1; the B groups would be 1,2; and the AB group would be 2,2 in the design matrix.
For all the groups I have a measurement before (T0) and after the treatment (T1). Every group had 10 individuals, so I have in total 80 measurement, 10 for each Treatment*Time.
I have used de design ~A*B*Time, but can not retrieve the contrast I look for.
I want to contrast for different times within a treatment, and for a given time (T0 or T1) the contrast between treatments.
For example, contrast="A1.B1.TimeT0" vs "A1.B1.TimeT1"
or contrast="A2.B1.TimeT1" vs "A1.B1.TimeT1"
but when I try to ask R for that it tells me that I can only contrast the parameters shown in resultsName where the combination of interaction are not to be found.
resultsNames(diagdds0)
[1] "Intercept" "A_2_vs_1" "B_2_vs_1" "Time_T1_vs_T0"
[5] "A2.B2" "A2.TimeT1" "B2.TimeT1" "O2.P2.TimeT1"
I think I could get the contrast that I want if I the variables them selves were parameters, i.e "A" and "B" "Time" and I could use contrast=list(c(), c()), and concatenate different levels of the variables. But there not there! were they absorbed by the intercept? what does that mean? or is it that I can only access to the information of the the second level because the first is considered as the reference?
When I try to use the design ~A+B+Time+A:B+A:B:Time it gives the error, some of the variables are lineal combination of others. (This would also happen if I do the model ~A:B. I don´t get why they would be a lineal combination if there are different combination of 1 and 2 for each treatment and have different individuals having those different combination).
I have created a new variable where every treatment is a factor and used the design ~Treatment*Time, and it gave me the combinations I want to contrast, but I think that the other design should account better for the variance and wish to use it instead. Hope some one can give me a hand.
Thank you very much in advance, cheers
Isabel.
Design matrix SampleID A B Time 13.1 2 2 T0 25.2 2 2 T1 34.1 1 2 T0 38.1 1 2 T0 2.2 2 1 T1 1.1 1 2 T0 10.2 1 1 T1 8.1 2 1 T0 26.2 1 1 T1 2.1 2 1 T0 15.1 1 2 T0 9.1 2 2 T0 39.1 1 2 T0 29.2 2 2 T1 1.2 1 2 T1 19.1 2 1 T0 34.2 1 2 T1 14.2 2 1 T1 18.2 1 1 T1 35.2 2 2 T1 14.1 2 1 T0 11.1 2 1 T0 12.2 2 2 T1 18.1 1 1 T0 28.1 1 2 T0 13.2 2 2 T1 20.1 1 1 T0 9.2 2 2 T1 6.2 1 2 T1 15.2 1 2 T1 27.1 1 2 T0 36.1 1 1 T0 8.2 2 1 T1 10.1 1 1 T0 36.2 1 1 T1 37.2 2 1 T1 39.2 1 2 T1 31.1 2 1 T0 30.1 2 1 T0 17.2 1 2 T1 35.1 2 2 T0 31.2 2 1 T1 32.2 2 1 T1 6.1 1 2 T0 27.2 1 2 T1 32.1 2 1 T0 33.1 1 1 T0 37.1 2 1 T0 19.2 2 1 T1 17.1 1 2 T0 3.2 1 1 T1 11.2 2 1 T1 26.1 1 1 T0 29.1 2 2 T0 30.2 2 1 T1 12.1 2 2 T0 20.2 1 1 T1 3.1 1 1 T0 23.1 1 1 T0 7.1 1 2 T0 16.2 2 2 T1 41.1 2 2 T0 16.1 2 2 T0 7.2 1 2 T1 4.2 1 1 T1 41.2 2 2 T1 38.2 1 2 T1 21.1 2 2 T0 22.1 1 2 T0 5.2 2 2 T1 25.1 2 2 T0 22.2 1 2 T1 4.1 1 1 T0 40.2 1 1 T1 40.1 1 1 T0 21.2 2 2 T1 23.2 1 1 T1 5.1 2 2 T0 24.2 2 1 T1 24.1 2 1 T0 28.2 1 2 T1 > sessionInfo() R version 3.1.2 (2014-10-31) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq2_1.6.3 RcppArmadillo_0.4.600.4.0 Rcpp_0.11.4 GenomicRanges_1.18.4 [5] GenomeInfoDb_1.2.4 IRanges_2.0.1 S4Vectors_0.4.0 BiocGenerics_0.12.1 [9] plyr_1.8.1 phyloseq_1.10.0 loaded via a namespace (and not attached): [1] acepack_1.3-3.3 ade4_1.6-2 annotate_1.44.0 AnnotationDbi_1.28.1 ape_3.2 [6] base64enc_0.1-2 BatchJobs_1.5 BBmisc_1.9 Biobase_2.26.0 BiocParallel_1.0.3 [11] biom_0.3.12 Biostrings_2.34.1 bitops_1.0-6 brew_1.0-6 checkmate_1.5.1 [16] cluster_2.0.1 codetools_0.2-10 colorspace_1.2-4 data.table_1.9.2 DBI_0.3.1 [21] digest_0.6.8 evaluate_0.5.5 fail_1.2 foreach_1.4.2 foreign_0.8-62 [26] formatR_1.0 Formula_1.2-0 genefilter_1.48.1 geneplotter_1.44.0 ggplot2_1.0.0 [31] grid_3.1.2 gtable_0.1.2 Hmisc_3.15-0 igraph_0.7.1 iterators_1.0.7 [36] knitr_1.9 lattice_0.20-29 latticeExtra_0.6-26 locfit_1.5-9.1 MASS_7.3-39 [41] Matrix_1.1-5 mgcv_1.8-4 multtest_2.22.0 munsell_0.4.2 nlme_3.1-119 [46] nnet_7.3-9 permute_0.8-3 plotly_0.5.24 proto_0.3-10 RColorBrewer_1.1-2 [51] RCurl_1.95-4.5 reshape2_1.4.1 RJSONIO_1.3-0 rpart_4.1-9 RSQLite_1.0.0 [56] scales_0.2.4 sendmailR_1.2-1 splines_3.1.2 stringr_0.6.2 survival_2.37-7 [61] tools_3.1.2 vegan_2.2-1 XML_3.98-1.1 xtable_1.7-4 XVector_0.6.0 [66] zlibbioc_1.12.0