Question: Can NanoString data be analyzed using DESeq2?
0
gravatar for lim6432
11 weeks ago by
lim64320
lim64320 wrote:

Hello, I am trying to analyze differential expressed genes using NanoString data. There are a number of tools that deal with RNA-seq data, but Nanostring data is not well documented. Since Nanostring data is also a gene count data, I think it is possible to analyze using RNA-seq tool.

Is it possible to analyze differential expressed genes with the DESeq2 package using NanoString data?

Many posts say that if you perform data normalization on your own, you can use DESeq to analyze differential gene analysis. Is that clear?

If you have good tools for dealing NanoString data, please let me know.

Thanks.

ADD COMMENTlink modified 11 weeks ago by Michael Love25k • written 11 weeks ago by lim64320
Answer: Can NanoString data be analyzed using DESeq2?
2
gravatar for Michael Love
11 weeks ago by
Michael Love25k
United States
Michael Love25k wrote:

We use DESeq2 on Nanostring datasets in our lab. Our approach is to estimate RUV factors using the endogenous housekeeping genes and use these in the design formula. You could also specify the endogenous housekeeping as controlGenes in DESeq2.

ADD COMMENTlink written 11 weeks ago by Michael Love25k

Thank you for your reply.

I have a question for your saying.

Is it a process for data normalization to calculate using controlGenes in DESeq2?(You could also specify the endogenous housekeeping as controlGenes in DESeq2.)

I have heard that the process of normalization of the NanoString is fixed, and I want to normalize them using other tools.(for normalization tool for NanoString nCounter data / e.g. NanoStringNorm etc.) But I can not find a function in DESeq2 that can receive data that has already been normalized.

Can I use the processed data(normalized count data) instead of the raw data(raw count data)?

ADD REPLYlink modified 11 weeks ago • written 11 weeks ago by lim64320

Their normalized values are not really the optimal input. We found in our internal testing it was much better it is to use the original counts and RUV or control genes plus various EDA and diagnostic checks, like MA plots with labeled control genes.

ADD REPLYlink written 11 weeks ago by Michael Love25k

DESeq assumes most genes are not DE. In Nanostring, as far as I understand, only genes that are expected to change are inspected (besides the housekeeping genes).

Is this DESeq assumption necessary only for the normalization step? If it is necessary for other steps in the process, it's hard to understand how can Nanostring data be analyzed with it.

ADD REPLYlink modified 10 weeks ago • written 10 weeks ago by Samuel0
1

This is why we use the endogenous housekeeping genes passed to RUV or to DESeq2 as controlGenes for normalization, followed by MA plots for quality control. The endogenous housekeeping should fall on the x-axis.

DESeq2 assumes that the median ratio captures the sequencing depth (not exactly the same as saying that most genes are not DE). But still, you do need to modify only the normalization step, to either inform DESeq2 about which are the control genes, or to calculate normalization with RUV. It’s just the normalization that needs a modification not other steps.

ADD REPLYlink modified 10 weeks ago • written 10 weeks ago by Michael Love25k
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