<p>I have done some gene expression work using two Nanostring panels (glial profiling and neuroinflammation) on the same 18 tissue samples. There are some overlapping genes between the panels, about 150 of 1400. Does anyone have any suggestions, possibly using a R package but open to other software, for how to normalize the gene expression data (raw count data) and deal with the overlapping genes between the two panels from the same samples. I have found some normalization packages (RUVSeq) for R but not yet something to handle the overlapping genes.</p>
<p>Thank you,<br />
Carolyn</p>

CC BY-SA 4.0
2023-02-22T18:57:19.890Z
What is the point to normalize them together? There is a batch effect between both due to different panels which cannot meaningfully be corrected due to the small overlap of genes. Why not analyzing separately using the control genes the panels have as reference, for example with DEseq2.
ATpoint
CC BY-SA 4.0
2023-02-23T21:41:21.347Z
Nanostring already provides their own normalisation software for adjusting gene counts based on panel and general controls (which is pretty good). Is there a particular reason why you're not using that?
gringer
<r><gene-expression><normalization>
Normalization and Merging of Gene Expression Panels with Overlapping Genes from Same Cohort
# Normalization and Merging of Gene Expression Panels with Overlapping Genes from Same Cohort

(Asked by: username_0 on 2023-02-22)

I have done some gene expression work using two Nanostring panels (glial profiling and neuroinflammation) on the same 18 tissue samples. There are some overlapping genes between the panels, about 150 of 1400. Does anyone have any suggestions, possibly using a R package but open to other software, for how to normalize the gene expression data (raw count data) and deal with the overlapping genes between the two panels from the same samples. I have found some normalization packages (RUVSeq) for R but not yet something to handle the overlapping genes.

Thank you,  
Carolyn

**Tags:** r, gene-expression, normalization
> **Comments:**
> **username_1**: What is the point to normalize them together? There is a batch effect between both due to different panels which cannot meaningfully be corrected due to the small overlap of genes. Why not analyzing separately using the control genes the panels have as reference, for example with DEseq2.  
> **username_2**: Nanostring already provides their own normalisation software for adjusting gene counts based on panel and general controls (which is pretty good). Is there a particular reason why you're not using that?  

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