Hi,
I've done a model design and I hope someone can help out with my understanding of it!
I have an experimental setup that looks something like this:
3 Time points (0hrs, 6hrs, 12hrs)
3 Different Conditions (Treatments A, B and C)
So that makes 9 different combinations of time points and treatments, each is in triplicate. There are therefore27 Samples.
My design formula is : ~ Treatment + TimePoint + Treatment:TimePoint
My current understanding is this will give me small pValues of Treatment-specific effects over time?
I wanted to further refine this and look at a specific treatment and how it differs between two time points. So I used the following line:
foo <- list(c(“TreatmentA.TimePoint12hrs"), c(“TreatmentA.TimePoint0hrs"))
resMFType <- results(dds, contrast=foo)
EDIT::
as.data.frame(colData(dds))
TimePoint Treatment
X1 0hr A
X2 0hr B
X3 0hr C
X4 0hr A
X5 0hr B
X6 0hr C
X7 0hr A
X8 0hr B
X9 0hr C
X10 6hr A
X11 6hr B
X12 6hr C
X13 6hr A
X14 6hr B
X15 6hr C
X16 6hr A
X17 6hr B
X18 6hr C
X19 12hr A
X20 12hr B
X21 12hr C
X22 12hr A
X23 12hr B
X24 12hr C
X25 12hr A
X26 12hr B
X27 12hr C
sessionInfo()
R version 3.1.2 (2014-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] DESeq2_1.6.2 RcppArmadillo_0.4.500.0 Rcpp_0.11.3 GenomicRanges_1.18.3
[5] GenomeInfoDb_1.2.3 IRanges_2.0.0 S4Vectors_0.4.0 BiocGenerics_0.12.1
loaded via a namespace (and not attached):
[1] acepack_1.3-3.3 annotate_1.44.0 AnnotationDbi_1.28.1 base64enc_0.1-2
[5] BatchJobs_1.5 BBmisc_1.8 Biobase_2.26.0 BiocParallel_1.0.0
[9] brew_1.0-6 checkmate_1.5.0 cluster_1.15.3 codetools_0.2-9
[13] colorspace_1.2-4 DBI_0.3.1 digest_0.6.4 fail_1.2
[17] foreach_1.4.2 foreign_0.8-61 Formula_1.1-2 genefilter_1.48.1
[21] geneplotter_1.44.0 ggplot2_1.0.0 grid_3.1.2 gtable_0.1.2
[25] Hmisc_3.14-5 iterators_1.0.7 lattice_0.20-29 latticeExtra_0.6-26
[29] locfit_1.5-9.1 MASS_7.3-35 munsell_0.4.2 nnet_7.3-8
[33] plyr_1.8.1 proto_0.3-10 RColorBrewer_1.0-5 reshape2_1.4
[37] rpart_4.1-8 RSQLite_1.0.0 scales_0.2.4 sendmailR_1.2-1
[41] splines_3.1.2 stringr_0.6.2 survival_2.37-7 tools_3.1.2
[45] XML_3.98-1.1 xtable_1.7-4 XVector_0.6.0
Is this correct?
Thanks,
hi Andrew,
Just to make sure we give the correct response, can you paste the output of
as.data.frame(colData(dds))
and also paste the output of
sessionInfo()
Hi Michael, See my edit above, Thanks,