Hello
1) I have two imput files: countData (counts of reads per gene, RNAseq type of data) and colData as .csv.
> colData<-read.csv("Pine_ColData.csv", sep=";", header=TRUE, check.names=FALSE,row.names=1) > countData<-read.csv("Pine_RNAseq_no outliers.csv", sep=";", header=TRUE, check.names=FALSE, row.names=1)>
2) I created the dds<-DESeqDataSetFromMatrix
> dds<-DESeqDataSetFromMatrix(countData=countData,colData=colData,design=~TREAT+LAT) > head(dds) class: DESeqDataSet dim: 6 16 metadata(0): assays(1): counts rownames(6): 1A_I11_NT_comp41909_c0_seq1|m.4335.path1.0 1A_I11_NT_comp41909_c0_seq1|m.4335.path2.0 ... 1A_I11_NT_comp45669_c0_seq3|m.4374.path2.0 1A_I11_NT_comp46081_c0_seq2|m.4378.path1.0 rowRanges metadata column names(0): colnames(16): A-P56-1 A-P56-3 ... B-P67-4 D-P56-4 colData names(2): TREAT LAT
3) when I try to run DEseq(dds) I got this error;
estimating size factors estimating dispersions gene-wise dispersion estimates Error in model.matrix.formula(design(object), data = colData(object)) : data must be a data.frame
4) I checked with is.data.frame(colData) and it returns [TRUE].
is.data.frame(colData) [1] TRUE > is.data.frame(countData) [1] TRUE
5) In that case, what is the problem?
I appreciate your help. (I could send you the original csv files if you want)
Maria
I mean, can you copy in the information that is printed when you type:
strange. I can't see what would be causing this. Can you email me the dds object:
save(dds, file="dds.rda")
my email is:
maintainer("DESeq2")