Design Matrix Singularity Error
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mike • 0
@9a9db074
Last seen 7 days ago
United States

Hi

I had a question about computationally singular matrices in DESeq and surrogate variables. After including all the SVs into my formula where I am analyzing paired samples (before/after treatment within a sample), I use the DESeq function and am returned with the error that my matrix is computationally singular. When I reduce the number of surrogate variables to 3, based on the n.sv() function using the "be" method, I no longer receive this error and am able to run the analysis. I want to know why the reduction of the number of SVs included in my design formula allows the dataset to run?

I am trying to create a design matrix that can be run in DESeq2 and have 8 variables that I am controlling for. The design matrix is as below:

dds <- DESeqDataSetFromMatrix(countData = countdata, colData = phenotype, design = ~ V3 + V4 + V5 + V6 + V7 + V8 + V9  +  V2)

My design matrix is this:

Design Matrix

Column headers are V1, V2.....V9. First column is ID, second column is treatment and the rest are what I am controlling for in respect to treatment. However, when I create the design matrix and run DESeq2, i get that error that my matrix is computationally singular. I have looked at similar questions posted here and checked if my design matrix has variables correlating highly with each other and that is not the case. I wanted to ask, first, that how many variables can I control for in DESEq2? Second, looking at my design matrix, what could be causing this error, Appreciate any help. Code should be placed in three backticks as shown below

DES DESIGN • 79 views
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@mikelove
Last seen 4 days ago
United States

Can you post the correlation matrix of the variables in your design?

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