Hi,
I would like to use DESeq2 for the analysis of the differential expressed gene during the tumor development. I got the expected cell number of tumor tissue and I want to add the cell number to design as covariate, and the colData as follows:
> coldata
batch time type cell_num group
0d-Mut2 2 0d mut 1289.6 mut_0d
0d-Mut3 2 0d mut 1523.8 mut_0d
10d-Mut3_2 2 10d mut 1478.4 mut_10d
8W-Mut2_2 2 8w mut 4315.8 mut_8w
10d-Mut1 1 10d mut 1708.4 mut_10d
10d-Mut2 1 10d mut 1463 mut_10d
10d-Mut3 1 10d mut 1478.4 mut_10d
4W-Mut1 1 4w mut 3499.6 mut_4w
4W-Mut2 1 4w mut 2890.4 mut_4w
4W-Mut3 1 4w mut 3742.2 mut_4w
8W-Mut1 1 8w mut 5281.6 mut_8w
8W-Mut2 1 8w mut 4315.8 mut_8w
8W-Mut3 1 8w mut 4420.6 mut_8w
And the design formula as follow:
dds <- DESeqDataSetFromMatrix(dat_filtered, coldata, design=~batch+cell_num+group)
But I encountered an error as follow:
> dds <- DESeqDataSetFromMatrix(dat_filtered, coldata, design=~batch+cell_num+group)
converting counts to integer mode
factor levels were dropped which had no samples
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')
I want to know if smoebody can solve this error? And "cell_number" is important for me, so I need to add it to design.
Thank you in advance for your reply!
Best regards
Hi, Michael, thanks for your help very much! Best wishes to you.