I have 3 tumour samples from 3 patients from an experiment, I also downloaded 10 normal samples from TCGA. My design is like this
> mycols condition batch normal1 normal 1 normal2 normal 1 normal3 normal 1 normal4 normal 1 normal5 normal 1 normal6 normal 1 normal7 normal 1 normal8 normal 1 normal9 normal 1 normal10 normal 1 OESO_036_a_RNA cancer 2 OESO_013_a_RNA cancer 2 OESO_005_a_RNA cancer 2 >
But DESeq2 returns error when trying for differential expression analysis
> dds <- DESeqDataSetFromMatrix(countData=counts, colData=mycols, design=~batch+condition) 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') In addition: Warning message: In DESeqDataSet(se, design = design, ignoreRank) : some variables in design formula are characters, converting to factors
Then how I could extract deferentially expressed genes between tumour and normal samples while they are coming from two different batches?
Any help please?