I am working with gene expression data from a RNASeq dataset using DESEq2.

I have realised my housekeeping gene (gene which is expected to be maintained across samples independent of condition) is significantly different between two of my condition groups. Therefore, I would like to normalise all my data to the expression of this gene.

Can I approach it like this?

dds <- DESeq(dds)

dds<- estimateSizeFactors(dds, controlGenes=OG_90)

dds <- nbinomWaldTest(dds)

I get the following error when running the estimateFactor:

Error in estimateSizeFactorsForMatrix(counts(object), locfunc = locfunc, : object 'OG_90' not found

How can I refer to the housekeeping gene in that function?

From the manual:

controlGenes: optional, numeric or logical index vector specifying those genes to use for size factor estimation (e.g. housekeeping or spike-in genes)

That means it must be a logical (TRUE/FALSE) or numeric vector that tells the function which row of your dds object the control genes are in. I would check though whether the normalization itself (using the defaults) make sense, for example using MA-plots. The bulk of data points should be somewhat centered along a logFC of zero. Just because a gene is expected not to change does not mean that in reality this assumption holds true. I would explore data before relying on normalization to a single gene.