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

  • $\begingroup$ Differential gene expression is one of them. $\endgroup$ Aug 14, 2021 at 20:15
  • $\begingroup$ 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. $\endgroup$ Aug 14, 2021 at 20:19
  • $\begingroup$ 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. $\endgroup$
    – M__
    Sep 15, 2023 at 20:28

2 Answers 2


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


You have multiple questions:

First of all, here is the description 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.

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 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, ...

  • $\begingroup$ I appreciate your response. Can you put some more light on the table I have provided ? $\endgroup$
    – Dhruv Shah
    Nov 29, 2019 at 9:18
  • $\begingroup$ 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. $\endgroup$
    – haci
    Nov 29, 2019 at 16:30
  • $\begingroup$ @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. $\endgroup$ Aug 14, 2021 at 20:18

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