Hello,

I am working with NanoString transcriptomics targeted data panel (containing 800 gene panel), the raw counts data was normalized in the nSolver Data Analysis software. I have the normalized data, and would like to use `limma`

for further analysis like filtering and statistical modelling. I would like to perform filter by expression on the normalized data matrix, it seems like in `limma`

this type of filtering could be performed only on the raw data (counts). Is there a functionality that I can use this normalized matrix in `limma`

to perform filtering by gene expression.

For instance, the below functionality I use in RNA-Seq analysis:

```
dge <- DGEList(counts=counts)
The next step is to remove rows that consistently have zero or very low counts. One can for example use
keep <- filterByExpr(dge, design)
dge <- dge[keep,,keep.lib.sizes=FALSE]
dge <- calcNormFactors(dge)
```

Thank you in advance.

Toufiq

The answer is probably yes, but it depends on what "normalized data" you have from nSolver. Do you have log-counts-per-million? Or something else? What exactly has the data been normalized for?

Gordon Smyth thank you for the response.

The data matrix is a normalized matrix (without log transformation). My collaborator has shared the steps how the normalization was performed:

Hi Toufiq,

I am trying to do the same thing as you with some NanoString data: I have a normalised count matrix from nSolver, normalised using geNorm, and want to perform DEG analysis with limma.

I have looked at the attached resources but I am still a bit confused with how to do this. Did you work out how to use limma/voom with normalised data instead of raw?

Thanks,

Emily

Emily

Hi, I used normalized data to log(norm.data, 2) > used in the

`limma`

package. Basically,