<p>It doesn't make sense to compare gene expression data from different studies unless you have a negative spiked-in control such as <a href="https://www.sequin.xyz/" rel="nofollow noreferrer">sequins</a>.</p>

<p>The most obvious thing you will need to do is normlizate the data for various bias such as batch effects, sequencing platforms, sequencing depth, bioinformatic workflows and more ... Your task will be quite <strong>complicated</strong> as you must justify how your normalization would work for data from different studies.</p>

<p>You should check how the raw data was generated (methods section), convince yourself, and then apply gene quantification yourself from the raw files.</p>

CC BY-SA 4.0
2018-08-16T13:05:34.830Z
Please check my recent comment in response to your answer
Natasha
CC BY-SA 4.0
2018-08-15T14:07:59.300Z
2018-08-15T14:07:59.300Z
SmallChess
<p>There's going to be a batch effect between studies, so unless you have some sort of constant group that you can use for between-study normalization you're going to completely muck up your results from using data from multiple studies.</p>

<p>More specifically, this is what packages like <code>sva</code> and the <code>combat()</code> command in it are used to correct. Note that you should go back to the raw .CEL or .fastq files if you want to process multiple studies, in order to at least ensure that everything is processed in the same way.</p>

CC BY-SA 4.0
2018-08-16T13:05:17.477Z
Please check my recent comment in response to your answer
Natasha
CC BY-SA 4.0
2018-08-15T12:04:00.343Z
2018-08-15T12:04:00.343Z
Devon Ryan
<p>I'm trying to compare the gene expression data reported in the studies documented in GEO for a specific gene expressed in a tissue of my interest in Homo sapiens.
I compared the values reported in 4 studies, in two studies the expression value of gene A lies between 15-30, in the other two studies the range lies between 1-4.</p>

<p>I would like to ask for advice on whether it is meaningful to compare the data from different studies. What are the factors that one should keep in mind while making such comparisons? </p>

<p>Any help would be highly appreciated</p>

CC BY-SA 4.0
2018-08-16T13:04:32.300Z
From the suggestions below, as a first check, I have looked for the platform used for the microarray analysis. The platform varies in each study. I'm trying to compare the probe densities. Unfortunately, I am not able to find this information.Any suggestions?
Natasha
CC BY-SA 4.0
2018-08-16T13:39:32.713Z
You won't be able to make comparison in this case.
SmallChess
CC BY-SA 4.0
2018-08-16T16:23:17.913Z
Could you please explain why?
Natasha
CC BY-SA 4.0
2018-08-16T20:54:23.290Z
The two answers here.
SmallChess
<gene><gene-expression><geoquery>
Comparing the gene expression data
# Comparing the gene expression data

(Asked by: username_0 on 2018-08-15)

I'm trying to compare the gene expression data reported in the studies documented in GEO for a specific gene expressed in a tissue of my interest in Homo sapiens.
I compared the values reported in 4 studies, in two studies the expression value of gene A lies between 15-30, in the other two studies the range lies between 1-4.

I would like to ask for advice on whether it is meaningful to compare the data from different studies. What are the factors that one should keep in mind while making such comparisons?

Any help would be highly appreciated

**Tags:** gene, gene-expression, geoquery
> **Comments:**
> **username_0**: From the suggestions below, as a first check, I have looked for the platform used for the microarray analysis. The platform varies in each study. I'm trying to compare the probe densities. Unfortunately, I am not able to find this information.Any suggestions?  
> **username_1**: You won't be able to make comparison in this case.  
> **username_0**: Could you please explain why?  
> **username_1**: The two answers here.  

---

## 2 answers

### Answer by username_1 on 2018-08-15. Score: 1

It doesn't make sense to compare gene expression data from different studies unless you have a negative spiked-in control such as [sequins](https://www.sequin.xyz/).

The most obvious thing you will need to do is normlizate the data for various bias such as batch effects, sequencing platforms, sequencing depth, bioinformatic workflows and more ... Your task will be quite **complicated** as you must justify how your normalization would work for data from different studies.

You should check how the raw data was generated (methods section), convince yourself, and then apply gene quantification yourself from the raw files.

> **Comments:**
> **username_0**: Please check my recent comment in response to your answer  

---

### Answer by username_2 on 2018-08-15. Score: 1

There's going to be a batch effect between studies, so unless you have some sort of constant group that you can use for between-study normalization you're going to completely muck up your results from using data from multiple studies.

More specifically, this is what packages like `sva` and the `combat()` command in it are used to correct. Note that you should go back to the raw .CEL or .fastq files if you want to process multiple studies, in order to at least ensure that everything is processed in the same way.

> **Comments:**
> **username_0**: Please check my recent comment in response to your answer  

---

