<p>You have multiple questions:</p>

<p>First of all, here is the <a href="https://www.ncbi.nlm.nih.gov/geo/info/overview.html" rel="nofollow noreferrer">description</a> of <code>GSE files</code> or <code>GEO Series</code>:</p>

<blockquote>
  <p><strong>Series records are supplied by submitters</strong></p>
  
  <p>A Series record links together a group of related Samples and provides
  a focal point and description of the whole study. Series records may
  also contain tables describing extracted data, summary conclusions, or
  analyses. Each Series record is assigned a unique and stable GEO
  accession number (GSExxx).</p>
</blockquote>

<p>Apparently the authors have provided their (differential expression) analysis results (p-values, adjusted p-values, logFC, ...). And the table you are sharing in your question does not look like <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3541" rel="nofollow noreferrer">a typical GSE profile</a>.</p>

<p>On your question on the feasibility of clustering based on GSE data: that would depend on the features (columns) you are provided with, you should have an understanding of each feature that you have. In your case, you can cluster your genes based on p-value, adjusted p-value and logFC, but would that make sense?</p>

<p>Regarding your question using k-means on only one variable, logFC: <a href="https://stats.stackexchange.com/questions/13781/clustering-1d-data">This link</a> points to a related discussion. Short answer is "technically yes".</p>

<p>Your last question depends on the "research question" you have, if you are asking about the table in your question: pathway analysis, gene set enrichment analysis, checking for co-expressed modules, ...</p>

CC BY-SA 4.0
2019-11-29T09:18:22.660Z
I appreciate your response. Can you put some more light on the table I have provided ?
Dhruv Shah
CC BY-SA 4.0
2019-11-29T16:30:29.853Z
Unfortunately I cannot do so as I haven't generated this table. It looks like the result of a differential gene expression analysis of microarray data and that is the best I can say about it. There are several pathway analysis and gene set enrichment tools that accept (ranked) gene lists and you can feed your DE genes to these, depending on your research question.
haci
CC BY-SA 4.0
2021-08-14T20:18:34.670Z
@DhruvShah The table's first column is spot identifier in the chip where the intensities were measured. `adj.P.Val, P.Value, t, B, logFC` values are result of different calculations and your table doesn't contain actual measured value.
Vishal Kumar Sahu
CC BY-SA 4.0
2019-11-23T11:35:58.157Z
2019-11-23T11:35:58.157Z
haci
<p>Have you ever used <code>GEO2R</code>? You can use <code>GEO2R</code> to analyze the data dividing it in subgroups (clusters) that interest you.</p>

CC BY-SA 4.0
2019-11-22T20:20:02.970Z
2019-11-23T09:00:51.560Z
2019-11-23T09:00:51.560Z
FPS
<p>I have a GSE data file in csv file format containing fields such as: ID, adj.P.Val, P.Value, t, B, logFC, Gene.symbol, Gene.title. In which adj.P.Val, P.Value, t, B, logFC fields being numeric. What are the factors I need to consider if I want to cluster the data only on logFC using K-Means clustering algorithm ? And first of all is it feasible to perform clustering on GSE data files ? If yes, what should be the approach ? If not, what different kinds of analysis can be performed on such kind of datasets ?</p>

<p>Following is snapshot of the data: <a href="https://i.sstatic.net/GdPhj.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/GdPhj.png" alt="GSE Data snapshot"></a></p>

CC BY-SA 4.0
2021-08-14T20:15:54.000Z
Differential gene expression is one of them.
Vishal Kumar Sahu
CC BY-SA 4.0
2021-08-14T20:19:54.083Z
Since you don't have actual values, you need to proceed with P.Value and in fact most of the analysis start after these calculations.
Vishal Kumar Sahu
CC BY-SA 4.0
2023-09-15T20:28:27.877Z
Please consider the answers below, I think their good answer and if you would also consider accepting one of them. Its good for site stats.
M__
<bioconductor><python><clustering><machine-learning>
What kind of analysis is practically done on GSE data files?
# What kind of analysis is practically done on GSE data files?

(Asked by: username_0 on 2019-11-22)

I have a GSE data file in csv file format containing fields such as: ID, adj.P.Val, P.Value, t, B, logFC, Gene.symbol, Gene.title. In which adj.P.Val, P.Value, t, B, logFC fields being numeric. What are the factors I need to consider if I want to cluster the data only on logFC using K-Means clustering algorithm ? And first of all is it feasible to perform clustering on GSE data files ? If yes, what should be the approach ? If not, what different kinds of analysis can be performed on such kind of datasets ?

Following is snapshot of the data: [![GSE Data snapshot](https://i.sstatic.net/GdPhj.png)](https://i.sstatic.net/GdPhj.png)

**Tags:** bioconductor, python, clustering, machine-learning
> **Comments:**
> **username_1**: Differential gene expression is one of them.  
> **username_1**: Since you don't have actual values, you need to proceed with P.Value and in fact most of the analysis start after these calculations.  
> **username_2**: Please consider the answers below, I think their good answer and if you would also consider accepting one of them. Its good for site stats.  

---

## 2 answers

### Answer by username_3 on 2019-11-23. Score: 1

You have multiple questions:

First of all, here is the [description](https://www.ncbi.nlm.nih.gov/geo/info/overview.html) of `GSE files` or `GEO Series`:

> **Series records are supplied by submitters**
>
> A Series record links together a group of related Samples and provides
> a focal point and description of the whole study. Series records may
> also contain tables describing extracted data, summary conclusions, or
> analyses. Each Series record is assigned a unique and stable GEO
> accession number (GSExxx).

Apparently the authors have provided their (differential expression) analysis results (p-values, adjusted p-values, logFC, ...). And the table you are sharing in your question does not look like [a typical GSE profile](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3541).

On your question on the feasibility of clustering based on GSE data: that would depend on the features (columns) you are provided with, you should have an understanding of each feature that you have. In your case, you can cluster your genes based on p-value, adjusted p-value and logFC, but would that make sense?

Regarding your question using k-means on only one variable, logFC: [This link](https://stats.stackexchange.com/questions/13781/clustering-1d-data) points to a related discussion. Short answer is "technically yes".

Your last question depends on the "research question" you have, if you are asking about the table in your question: pathway analysis, gene set enrichment analysis, checking for co-expressed modules, ...

> **Comments:**
> **username_0**: I appreciate your response. Can you put some more light on the table I have provided ?  
> **username_3**: Unfortunately I cannot do so as I haven't generated this table. It looks like the result of a differential gene expression analysis of microarray data and that is the best I can say about it. There are several pathway analysis and gene set enrichment tools that accept (ranked) gene lists and you can feed your DE genes to these, depending on your research question.  
> **username_1**: @DhruvShah The table's first column is spot identifier in the chip where the intensities were measured. `adj.P.Val, P.Value, t, B, logFC` values are result of different calculations and your table doesn't contain actual measured value.  

---

### Answer by username_4 on 2019-11-22. Score: 1

Have you ever used `GEO2R`? You can use `GEO2R` to analyze the data dividing it in subgroups (clusters) that interest you.


---

