Hi all, I am trying to create a design to compansate for different DMSO concetrations in an experiment using same cell but different inhibitors. the sampleTable looks like
sampleTable$group <- factor(sampleTable$group)
sampleTable$batch <- factor(sampleTable$batch)
> sampleTable
group batch
DR1_01_S1 DMSOFHIN1 DMSO2
DR1_02_S2 DMSOFHIN1 DMSO2
DR1_03_S3 DMSOFHIN1 DMSO2
DR1_04_S4 FHIN1 DMSO2
DR1_05_S5 FHIN1 DMSO2
DR1_06_S6 FHIN1 DMSO2
DR2_01_S1 DMSOAA5 DMSO1
DR2_02_S2 DMSOAA5 DMSO1
DR2_03_S3 DMSOAA5 DMSO1
DR2_04_S4 AA5 DMSO1
DR2_05_S5 AA5 DMSO1
DR2_06_S6 AA5 DMSO1
my commands are as follows:
# create DESeqDataSet
dds <- DESeqDataSetFromMatrix(countData = as.matrix(countTable),
colData = sampleTable,
design = ~ batch + group)
Error in checkFullRank(modelMatrix) : 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. Please read the vignette section 'Model matrix not full rank': vignette('DESeq2')
What am I missing? thank you in advance
Thank you. I though that this would be the issue, but wanted a verification. But, the issue here is that I can not compare AA5 with FHIN1 because of different DMSO concetrations that promote different transcription landscape (doing DMSO1-DMSO2 and doing GSEA outputs pathways that have some common ones with the AA5 Vs FHIN1 comparison. I read the part at DESeq2that how to make a design that can work, but could not comprehend it. Any idea how to do this?
As recommended, you can only perform ~group. You can use contrasts do perform the comparison AA5 vs FHIN1 (see DESeq2 vignette), but this result will be confounded by DMSO concentration and will biologically difficult to interpret if DMSO concentration has a huge effect
Is there a way to test properly the differences in the 2 conditions (AA5-FHIN1) by subtracting the DMSO influence?
Both ATpoint and Basti have already told you that you cannot adjust for DMSO.