<p>Usually with microarrays you want to make a case/control comparison, so I am going to assume that.</p>

<p>Data from different array platforms is generally difficult to compare:  each platform is measuring potentially a different part of the expression of the gene (different exons or different regions), each platform is likely to need different normalisation approaches, and have different dynamic range.</p>

<p>If you have case/control data from several different platforms, then the most robust thing to do is to analyse each platform separately, and produce a list of up and down-regulated genes according to each platform.</p>

<p>Then, it would be reasonably safe to combine the lists of genes in a 'meta-analysis' approach.  I would expect that in a lot of cases, 'up' and 'down' regulation would be correlated across platforms, but I would not necessarily expect very strong correlation in the magnitude of changes between platforms for individual genes.</p>

CC BY-SA 4.0
2019-02-20T14:03:34.987Z
so are you saying its better not to use combat or any other algorithm for that purpose? due to multi platform issue , we have out of one control which for other comparison so meta analysis may not not be possible to different patform
kcm
CC BY-SA 4.0
2019-02-20T14:35:27.757Z
You could try combat, I don't have experience with that.  In general I would say it is difficult/not advised to mix platforms in a single analysis.
Jay Moore
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2019-02-20T14:41:54.887Z
okay .will wait for other answers if anyone has done something similar..to get their views..
kcm
CC BY-SA 4.0
2019-02-20T12:10:31.057Z
2019-02-20T12:10:31.057Z
Jay Moore
<p>I have been looking ways to use data from various micro-array platform such as agilient, rosetta/merk. Some of the established method I came across is combat, but I'm not sure if it can be done for cross platform. </p>

<p>A similar question <a href="https://support.bioconductor.org/p/77363/" rel="nofollow noreferrer">here</a> where the answer is kind of can't be or shouldn't be done.</p>

<p>Any suggestion how to take account of batch effects across multiple platforms.</p>

<r><microarray><batch-effects>
Combat for multiplatform batch correction
# Combat for multiplatform batch correction

(Asked by: username_0 on 2019-02-20)

I have been looking ways to use data from various micro-array platform such as agilient, rosetta/merk. Some of the established method I came across is combat, but I'm not sure if it can be done for cross platform.

A similar question [here](https://support.bioconductor.org/p/77363/) where the answer is kind of can't be or shouldn't be done.

Any suggestion how to take account of batch effects across multiple platforms.

**Tags:** r, microarray, batch-effects

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## 1 answers

### Answer by username_1 on 2019-02-20. Score: 3 | Accepted answer

Usually with microarrays you want to make a case/control comparison, so I am going to assume that.

Data from different array platforms is generally difficult to compare: each platform is measuring potentially a different part of the expression of the gene (different exons or different regions), each platform is likely to need different normalisation approaches, and have different dynamic range.

If you have case/control data from several different platforms, then the most robust thing to do is to analyse each platform separately, and produce a list of up and down-regulated genes according to each platform.

Then, it would be reasonably safe to combine the lists of genes in a 'meta-analysis' approach. I would expect that in a lot of cases, 'up' and 'down' regulation would be correlated across platforms, but I would not necessarily expect very strong correlation in the magnitude of changes between platforms for individual genes.

> **Comments:**
> **username_0**: so are you saying its better not to use combat or any other algorithm for that purpose? due to multi platform issue , we have out of one control which for other comparison so meta analysis may not not be possible to different patform  
> **username_1**: You could try combat, I don't have experience with that.  In general I would say it is difficult/not advised to mix platforms in a single analysis.  
> **username_0**: okay .will wait for other answers if anyone has done something similar..to get their views..  

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