

From: Karen Chait [mailto:kchait@tx.technion.ac.il]
Sent: Monday, March 17, 2014 10:57 AM
To: Olga Karinsky
Subject: RE: using DESeq2 with multi factor data

Hello all,
I am trying to use the DESeq2 package to perform RNA-Seq analysis on a data containing several factors.
I have been closely following the emails between Ming Yi and Michael Love, because I think that my problem is very similar to what they have discussed.  But even though I received a lot of useful information from their discussion, I still have several questions regarding my specific data.
Just as an overall information regarding my data, I have 96 samples and the two factors I am interested in exploring are "time" and "metastasis".
In order to build my data set I used the following commands:

>  countData = read.table("merged_counts.txt", header=TRUE, row.names=1)

>  metasVector=c("met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_no","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes","met_yes"(

>  timePointsVector=c("6","4","6","6","3","6","3","5","6","6","1","5","3","4","3","6","6","6","2","6","1","2","4","6","5","5","5","3","6","5","6","2","6","6","1","5","5","6","6","6","6","6","6","4","2","6","3","1","2","5","6","1","1","3","6","3","6","4","4","5","6","6","3","5","4","6","1","4","3","1","1","1","4","2","1","1","3","6","1","1","2","1","6","3","3","2","5","3","2","3","1","4","1","1","6","1")

>  colData=data.frame(row.names=colnames(countData),metas=metasVector,gender=gendarVector)

>  colData$metas=factor(colData$metas, levels=c("met_no","met_yes"))

>  colData$time = factor(colData$time, levels = c("1", "2", "3", "4", "5", "6"))

>  dds=DESeqDataSetFromMatrix(countData=tmpcountData, colData=colData, design=~time + metas + metas:time)

>  dds=DESeq(dds)
I have several questions:

-          first of all I have tried running those commands on DESeq2 version 1.2.10 (R version 3.0.2) and DESeq2 version 1.3.47 (R version 3.0.2) and what I have received from the resultsNames() function I both cases is very different. Using the 1.2.10 version I have received:

> resultsNames(dds)

[1] "Intercept"               "time_2_vs_1"             "time_3_vs_1"             "time_4_vs_1"             "time_5_vs_1"             "time_6_vs_1"             "metas_met_yes_vs_met_no" "time2.metasmet_yes"

 [9] "time3.metasmet_yes"      "time4.metasmet_yes"      "time5.metasmet_yes"      "time6.metasmet_yes"

> sessionInfo()

R version 3.0.2 (2013-09-25)

Platform: x86_64-w64-mingw32/x64 (64-bit)



locale:

[1] LC_COLLATE=Hebrew_Israel.1255  LC_CTYPE=Hebrew_Israel.1255    LC_MONETARY=Hebrew_Israel.1255 LC_NUMERIC=C                   LC_TIME=Hebrew_Israel.1255



attached base packages:

[1] parallel  stats     graphics  grDevices utils     datasets  methods   base



other attached packages:

[1] DESeq2_1.2.10             RcppArmadillo_0.4.100.2.1 Rcpp_0.11.0               GenomicRanges_1.14.4      XVector_0.2.0             IRanges_1.20.7            BiocGenerics_0.8.0



loaded via a namespace (and not attached):

[1] annotate_1.40.1      AnnotationDbi_1.24.0 Biobase_2.22.0       DBI_0.2-7            genefilter_1.44.0    grid_3.0.2           lattice_0.20-27      locfit_1.5-9.1       RColorBrewer_1.0-5   RSQLite_0.11.4

[11] splines_3.0.2        stats4_3.0.2         survival_2.37-7      tools_3.0.2          XML_3.98-1.1         xtable_1.7-3



Using the 1.3.47 version I have received:

> resultsNames(dds)

[1] "Intercept"               "timetime_1"              "timetime_2"              "timetime_3"              "timetime_4"              "timetime_5"

 [7] "timetime_6"              "metasmet_no"             "metasmet_yes"            "timetime_1.metasmet_no"  "timetime_2.metasmet_no"  "timetime_3.metasmet_no"

[13] "timetime_4.metasmet_no"  "timetime_5.metasmet_no"  "timetime_6.metasmet_no"  "timetime_1.metasmet_yes" "timetime_2.metasmet_yes" "timetime_3.metasmet_yes"

[19] "timetime_4.metasmet_yes" "timetime_5.metasmet_yes" "timetime_6.metasmet_yes"

> sessionInfo()

R version 3.0.2 (2013-09-25)

Platform: x86_64-w64-mingw32/x64 (64-bit)



locale:

[1] LC_COLLATE=Hebrew_Israel.1255  LC_CTYPE=Hebrew_Israel.1255    LC_MONETARY=Hebrew_Israel.1255 LC_NUMERIC=C                   LC_TIME=Hebrew_Israel.1255



attached base packages:

[1] parallel  stats     graphics  grDevices utils     datasets  methods   base



other attached packages:

[1] DESeq2_1.3.47           RcppArmadillo_0.4.100.0 Rcpp_0.11.0             GenomicRanges_1.14.4    XVector_0.2.0           IRanges_1.20.7

[7] BiocGenerics_0.8.0



loaded via a namespace (and not attached):

[1] annotate_1.40.1      AnnotationDbi_1.24.0 Biobase_2.22.0       DBI_0.2-7            genefilter_1.44.0    geneplotter_1.40.0   grid_3.0.2

 [8] lattice_0.20-27      locfit_1.5-9.1       RColorBrewer_1.0-5   RSQLite_0.11.4       splines_3.0.2        stats4_3.0.2         survival_2.37-7

[15] XML_3.98-1.1         xtable_1.7-3

(I have ran the 1.3.47 version the same way besides a difference in the names of the time levels, but I do not believe that this is the reason for the differences)
                I don't fully understand the results I receive using the 1.3.47 version and even more the difference between the versions.

-          From my understanding, the results I received using the 1.2.10 version are the more reasonable and they fit my settings of base levels in the data. Now after receiving these results I would love to understand how do I receive different contrast testing? For each time period metas_yes vs. metas_no (for example timetime_2.metasmet_yes vs. timetime_2.metasmet_no)



Thank you in advance,

Olga and Karen



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