This video updates a 2012 video called, GEO2R: Analyze GEO Data.
Both videos demonstrate how to use GEO2R to analyze gene expression data present in GEO.
Now, more than 99 thousand studies are available for analysis with GEO2R.
Plus, we've added two new features: the ability to analyze RNA-seq data, and interactive visualization
tools to help you explore the data.
In this demonstration, I'm going to analyze the GEO Series GSE41586, which investigates
gene expression in a human colon cancer cell line, HT29, when the cells are treated with
3 concentrations of the chemotherapy drug, 5-aza-deoxy-cytidine.
I'll begin the analysis by scrolling down and clicking the button, Analyze with GEO2R.
Before I run GEO2R, note the 'Full instructions' link near the top of the page.
This documentation includes edit options and features that are not part of this video,
and provides references for the software that forms the basis of the tool.
And, be sure to read the section detailing GEO2R's 'Limitations and caveats'.
Going back to GEO2R, we see a list of the samples in the study, along with metadata
extracted from the original records.
The samples are grouped by 3 treatment levels; zero, 5 micromolar, and 10 micromolar 5-aza.
The first thing we need to do is to define the groups of samples that we want to compare.
Click 'Define groups' and type a group name into the box; you can name the groups anything
you want.
I'll type control, hit Enter; then low dose, Enter, and high dose, Enter.
Next, we need to assign samples to each group.
I'll click on a row and drag the cursor over the relevant samples.
Then click the group name that they belong to, and repeat for each group.
Note that GEO2R supports up to 10 groups.
I can now run the analysis.
Down the page, I'll click on the Analyze button.
This performs a comparison with default settings, and after a brief wait, I see several visualization
tools, and below those, a table of the top 250 differentially expressed genes, sorted
by P-values.
In the table, I can view the gene expression profile for a single gene by clicking in the
row.
For example, I'll click on the top row, for gene ERVFC1, and I see a chart where the red
bars represent the expression level of the gene in each sample.
In this case we see that ERVFC1 is more highly expressed in the low dose group compared to
the other 2 groups.
You could also use the Profile graph tab to explore a specific gene.
Here you enter either the gene symbol or gene ID number, which you can get from the table,
or by downloading an annotation file with this link, Download gene annotation.
For example, if I want to see the graph for Nitric Oxide Synthase 3, I enter either NOS3
or the ID, 4846.
Clicking on Set displays the graph.
I'll now go back to the table of the top differentially expressed genes.
I can edit this table by using the 'Select columns' feature.
This opens a dialog box that lists all the available statistics and annotation columns.
To save the full set of results, use the ‘Download Full Table’ link to export the results as
a tab-delimited file.
Now let's take a look at the new visualization tools.
We see seven different plots for visualizing the data.
Let's look first at the UMAP plot.
Click on the plot to open a larger pop-up for better viewing.
In general, samples clustered together are more closely related than those found farther
apart.
See the Full instructions that we saw earlier for more explanation.
For this series we see that the samples cluster within each group, but the groups are not
closely related to each other.
If I want to look at the relationship between samples before running an analysis, click
“Analyze” before assigning samples to groups and the UMAP plot and boxplot will
be displayed.
The plots outlined in green have interactive features.
I'll click on the first plot on the left, the volcano plot, then on the “Explore and
Download” button.
Red dots represent genes with increased expression and blue with decreased expression.
Data below the threshold is colored gray.
Scrolling over points on the graph reveals gene information, the log2 fold-change value,
and Pvalue.
Radio buttons at the bottom of the window allow you to toggle between comparisons, called
contrasts here, and you can download a tab separated file containing data for the selected
contrast.
You may want to change test parameters and run the analysis again.
Use the Options tab to make changes to the P-value adjustment method, or significance
level cut-off, or log 2 fold-change threshold.
I'll lower the significance level cut-off to 0.001 and the log2 fold change threshold
to 2, then I'll click “Reanalyze”.
Some of the plots are now revised, including the Volcano plot.
Finally, the R script tab displays the script used to perform your last analysis.
You may want to save this as a reference.
That's all for this demonstration.
If you have any questions or feedback, please email the GEO team: geo@ncbi.nlm.nih.gov
or use the Email GEO link at the top of the page.