Using DESeq2 for time-series analysis with two conditions
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Entering edit mode
@95479676
Last seen 5 weeks ago
Spain

I performing a Time-series experiment with several patients with 2 conditions and 2 time-points from each patient. The patients associated to the first condition are different respect to the second one. The experiment consist of 24 patients, 12 of them who respond to one treatment and the other 12 who not respond to the same treatment. the RNA raw count data was performed for two times in each patient (time=0 and time=2). I would like to compare the differential expression between responders and non-responders taking into account both time-series. However, i cannot create the matrix in the step "DESeqDataSetFromMatrix" when I'm introducing the model matrix (like this ~ phenotypeData + phenotypeData:patient.n + phenotypeData:time, coldata))with the conditions. My code is shown below:


The experiment design coldata

  phenotypeData patient time patient.n
1     responders       1   t0         1
2     responders       1   t2         1
3     responders       2   t0         2
4     responders       2   t2         2
5     responders       3   t0         3
6     responders       3   t2         3
7     responders       4   t0         4
8     responders       4   t2         4
.
.
.
43 nonresponders      22   t0        22
44 nonresponders      22   t2        22
45 nonresponders      23   t0        23
46 nonresponders      23   t2        23
47 nonresponders      24   t0        24
48 nonresponders      24   t2        24

and the CountData is like this:

               X1_SAL008_0_R X2_SAL008_2_R X5_JC022_0_R X6_JC022_2_R X9_NJ029_0_R X10_NJ029_2_R X13_IL008_0_R
ENSG00000000003          3.05         20.01        24.38         8.47         6.11          7.28          6.00
ENSG00000000005          0.00          0.00         0.00         0.00         0.00          0.00          0.00
ENSG00000000419        222.00        122.00       165.00       124.00       125.00        152.00         45.00
ENSG00000000457        160.33        342.62       314.02       295.18       228.01        244.49         86.41

And so on....


And my DESeq2 code:

ListRNAseqTimeSeries<-"raw_data_matrix_allSamples_RvsNR_newDESeqFINAL2.csv"
ListRNAseqTimeSeries

coldata <- DataFrame(phenotypeData=factor(c(rep("responders",each=24),rep("nonresponders",each=24))),
                     patient=factor(c(rep(1:24,each=2))),
                     time=factor(rep(c("t0","t2"),each=1)))
coldata$patient.n <- factor(c(rep(1:24,each=2)))
coldata$phenotypeData <- relevel(coldata$phenotypeData, "nonresponders")
RNA_rawMatrix <- data.matrix(read.csv(ListRNAseqTimeSeries, header = T, sep = "\t", dec = ".", row.names = 1))
RNA_rawMatrix2DESeq <- floor(RNA_rawMatrix) ## DESeq only allows integer values
RNAMatrix <- model.matrix(~ phenotypeData  + phenotypeData:patient.n + phenotypeData:time, coldata, tidy = F)
RNAMatrix2 <- model.matrix(~ phenotypeData  + phenotypeData:patient.n, coldata, tidy = F)
idx <- which(apply(RNAMatrix, 2, function(x) all(x==0)))
idx2 <- which(apply(RNAMatrix2, 2, function(x) all(x==0)))
RNAMatrix <- RNAMatrix[,-idx]
RNAMatrix2 <- RNAMatrix2[,-idx]
RNA_Data <- DESeqDataSetFromMatrix(countData = RNA_rawMatrix2DESeq, colData = coldata, design = RNAMatrix)
RNA_DE <- DESeq (RNA_Data, test = "LRT", reduced = RNAMatrix2)
Log2FC <- results(RNA_DE)
RNA_DE <- estimateSizeFactors(RNA_DE)
NormMatrix<-counts(RNA_DE, normalized=TRUE)
write.table (Log2FC,file = paste("RNA_DE",substr(ListRNAseqTimeSeries,27,70), sep=""),sep ="\t",row.names = TRUE,col.names = TRUE,quote=F)
write.table (NormMatrix,file = paste("RNA_NormMatrix",substr(ListRNAseqTimeSeries,27,40), sep=""),sep ="\t",row.names = TRUE,col.names = TRUE,quote=F)r

But i have the following error:

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.

And i don't know exactly how solve this error. Thanks so much in advance for further information in order to solve this problem

DESeq2 RNASeqData • 131 views
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Entering edit mode
ATpoint ★ 2.0k
@atpoint-13662
Last seen 10 hours ago
Germany

Patients 1-12 are confounded with the first condition, and 13-end with the second one. Either drop the patient column for DESeq2 or use limma with duplicateCorrelation. The search function will give plenty of posts on DESeq2 where duplicateCorrelation was recommended for these situations.

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