DESeq2 model design: dose and time effect
i have three samples each with 3 biological replicates i.e. one control and other at dose 12ug/ml and 25ug/ml at 24hr
> library('DESeq2')
> directory<-"./data"
> sampleFiles <- grep("Htseq",list.files(directory),value=TRUE)
> sampleCondition<-c("treated","treated","treated","treated","treated","treated","untreated","untreated","untreated")
> sampleGroup <-c ("A","A","A","B","B","B","C","C","C")
> sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles, condition=sampleCondition, group=sampleGroup)
> sampleTable
sampleName fileName condition group
1 24h_12ug_1.count 24h_12ug_1.count treated A
2 24h_12ug_2.count 24h_12ug_2.count treated A
3 24h_12ug_3.count 24h_12ug_3.count treated A
4 24h_25ug_1.count 24h_25ug_1.count treated B
5 24h_25ug_2.count 24h_25ug_2.count treated B
6 24h_25ug_3.count 24h_25ug_3.count treated B
7 24h_ctrl_1.count 24h_ctrl_1.count untreated C
8 24h_ctrl_2.count 24h_ctrl_2.count untreated C
9 24h_ctrl_3.count 24h_ctrl_3.count untreated C
what should be good design if i want to see the differential expressed genes between treated vs untreated as well between different groups ?
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~group)
OR
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)
what will be the difference between the two designs?
I tried the following design but it gave me error
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~group + condition + condition:group)
Error in DESeqDataSet(se, design = design, ignoreRank) :
the model matrix is not full rank, so the model cannot be fit as specified.
one or more variables or interaction terms in the design formula
are linear combinations of the others and must be removed
how will the design will change if i add three samples each with 3 biological replicates i.e. one control and other at dose 12ug/ml and 25ug/ml at 6hr
> library('DESeq2')
> directory<-"./data"
> sampleFiles <- grep("Htseq",list.files(directory),value=TRUE)
> sampleCondition<-c("treated","treated","treated","treated","treated","treated","untreated","untreated","untreated")
> sampleGroup <-c ("A","A","A","B","B","B","C","C","C")
> sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles, condition=sampleCondition, group=sampleGroup)
> sampleTable
sampleName fileName condition group
1 24h_12ug_1.count 24h_12ug_1.count treated A
2 24h_12ug_2.count 24h_12ug_2.count treated A
3 24h_12ug_3.count 24h_12ug_3.count treated A
4 24h_25ug_1.count 24h_25ug_1.count treated B
5 24h_25ug_2.count 24h_25ug_2.count treated B
6 24h_25ug_3.count 24h_25ug_3.count treated B
7 24h_ctrl_1.count 24h_ctrl_1.count untreated C
8 24h_ctrl_2.count 24h_ctrl_2.count untreated C
9 24h_ctrl_3.count 24h_ctrl_3.count untreated C
what should be good design if i want to see the differential expressed genes between treated vs untreated as well between different groups ?
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~group)
OR
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)
what will be the difference between the two designs?
I tried the following design but it gave me error
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~group + condition + condition:group)
Error in DESeqDataSet(se, design = design, ignoreRank) :
the model matrix is not full rank, so the model cannot be fit as specified.
one or more variables or interaction terms in the design formula
are linear combinations of the others and must be removed
how will the design will change if i add three samples each with 3 biological replicates i.e. one control and other at dose 12ug/ml and 25ug/ml at 6hr
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