Hi all,

I know this was discussed already, but still after reading a lot here and in the vignette, I'm still not sure, I am doing it correctly.

I have multiple time points (colData below) with two or three replica. I am interessted in finding genes which are differentially regulated between two specific time points (TP) and also such, which are DE over all TP.

This is what I have done so far:

#reading in the phenotypic data from a file
phenotype <- read.delim2("phenotypeData.txt", colClasses = c(rep("factor", 3)))
# create the DESeq object from a matrix.
dds<-DESeqDataSetFromMatrix(countData=countTable, colData=phenotype, design= ~ replica + time )
dds = DESeq(dds, test="LRT", reduced=~replica)
resultsNames(dds)

res<-results(dds)

I think my design make sense if I am trying to search for such genes, which change over all TP, but it arises a few questions about the follow-up analysis

I was wondering how sensitive is DESeq2 for changes in only one or two time points. If I have genes with a very high expression in one or two (consecutive or not) TP, will DESeq2 be able to identify them with such a model?

I don't know how sensitive DESeq2 is to dynamics in the time course. What happens with genes, that goes up in one TP, down in the next and up again, etc. Can DESeq2 identify them as well?

I have a second question about the results I get from this analysis. When I am looking at the number of genes differentially expressed, there are a lot of them

> attr(res, "filterThreshold")
11.08629%
0.5207798

and when I am plotting the data I get IMHO a very unusual plot (attached here below). This is also one reason why I am not so sure, if the design matrix I'm using is the right one.

I am sorry my question is so long and hope you've reached to the end.

Thanks for the help in advance

Assa

phenotypeData.txt
sampleName    time    replica
IFM_Myoblast_1    0h    1
IFM_Myoblast_2    0h    2
IFM_Myoblast_3    0h    3
IFM16h_1    16h    1
IFM16h_2    16h    2
IFM16h_3    16h    3
IFM24h_1    24h    1
IFM24h_2    24h    2
IFM24h_3    24h    3
IFM30h_1    30h    1
IFM30h_2    30h    2
IFM48h_1    48h    1
IFM48h_2    48h    2
IFM48h_3    48h    3
IFM72h_1    72h    1
IFM72h_2    72h    2
IFM90h_1    90h    1
IFM90h_2    90h    2
IFM100h_1    100h    1
IFM100h_2    100h    2

> sessionInfo()
R version 3.2.1 (2015-06-18)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

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] ggplot2_1.0.1             data.table_1.9.6          hwriter_1.3.2            
 [4] GOstats_2.34.0            graph_1.46.0              Category_2.34.2          
 [7] GO.db_3.1.2               AnnotationDbi_1.30.1      Matrix_1.2-2             
[10] Biobase_2.28.0            gplots_2.17.0             biomaRt_2.24.0           
[13] ReportingTools_2.8.0      RSQLite_1.0.0             DBI_0.3.1                
[16] knitr_1.11                RColorBrewer_1.1-2        genefilter_1.50.0        
[19] stringr_1.0.0             DESeq2_1.8.1              RcppArmadillo_0.5.600.2.0
[22] Rcpp_0.12.1               GenomicRanges_1.20.6      GenomeInfoDb_1.4.2       
[25] IRanges_2.2.7             S4Vectors_0.6.5           BiocGenerics_0.14.0      

loaded via a namespace (and not attached):
 [1] bitops_1.0-6              tools_3.2.1               rpart_4.1-10             
 [4] KernSmooth_2.23-15        Hmisc_3.16-0              colorspace_1.2-6         
 [7] nnet_7.3-11               gridExtra_2.0.0           GGally_0.5.0             
[10] chron_2.3-47              formatR_1.2.1             rtracklayer_1.28.10      
[13] ggbio_1.16.1              caTools_1.17.1            scales_0.3.0             
[16] RBGL_1.44.0               digest_0.6.8              Rsamtools_1.20.4         
[19] foreign_0.8-66            R.utils_2.1.0             AnnotationForge_1.10.1   
[22] XVector_0.8.0             dichromat_2.0-0           highr_0.5.1              
[25] limma_3.24.15             BSgenome_1.36.3           PFAM.db_3.1.2            
[28] BiocParallel_1.2.21       gtools_3.5.0              acepack_1.3-3.3          
[31] R.oo_1.19.0               VariantAnnotation_1.14.13 RCurl_1.95-4.7           
[34] magrittr_1.5              Formula_1.2-1             futile.logger_1.4.1      
[37] munsell_0.4.2             proto_0.3-10              R.methodsS3_1.7.0        
[40] stringi_0.5-5             edgeR_3.10.2              MASS_7.3-44              
[43] zlibbioc_1.14.0           plyr_1.8.3                grid_3.2.1               
[46] gdata_2.17.0              lattice_0.20-33           Biostrings_2.36.4        
[49] splines_3.2.1             GenomicFeatures_1.20.4    annotate_1.46.1          
[52] locfit_1.5-9.1            geneplotter_1.46.0        reshape2_1.4.1           
[55] futile.options_1.0.0      XML_3.98-1.3              evaluate_0.8             
[58] biovizBase_1.16.0         latticeExtra_0.6-26       lambda.r_1.1.7           
[61] gtable_0.1.2              reshape_0.8.5             xtable_1.7-4             
[64] survival_2.38-3           OrganismDbi_1.10.0        GenomicAlignments_1.4.1  
[67] cluster_2.0.3             GSEABase_1.30.2 

I'll continue on the other thread

DE genes over time with DESeq2