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GSEA Algorithm

What is the difference between GSEA and an overlap statistic (hypergeometric) analysis tool?

An overlap statistic analysis tool typically uses a threshold to define genes as members at the top or bottom of a ranked list of genes.  In contrast GSEA uses the list rank information without using a threshold. The introduction to the Gene Set Enrichment Analysis PNAS paper discusses the limitations of the former approach and how GSEA addresses them.

Why does GSEA use the Kolmogorov-Smirnov statistic rather than the Mann-Whitney test?

The Kolmogorov-Smirnov statistic is slightly more suitable for less coherent data because it takes relatively fewer significant items to score well. The Gene Set Enrichment Analysis PNAS paper discusses the use of this statistic in detail (see the section titled Adjusting for Variation in Gene Set Size in the supplemental information).

How does GSEA rank the genes in my dataset?

By default, GSEA uses the signal-to-noise metric to rank the genes. Optionally, use the Metric for ranking genes parameter to select the ranking metric that you want GSEA to use.  For more information, see the Metric for ranking genes parameter on the Run GSEA Page in the GSEA User Guide.

Can I use GSEA to analyze my own ranked list of genes?

Yes. Use the GseaPreranked analysis to run the gene set enrichment analysis against your own ranked list of genes. For more information, see GSEAPreranked Page in the GSEA User Guide.

Can I use GSEA to compare two datasets?

Yes. Create a gene set that contains the top genes from the first dataset and use GSEA to analyze that gene set against the second dataset. Similarly, create a gene set that contains the top genes from the second dataset and use GSEA to analyze that gene set against the first dataset. For example, you might analyze the top 100 genes from each dataset.

Can I use GSEA to analyze a dataset that contains a single sample?

Yes.  However, GSEA has no way of ranking the genes in such a dataset. Therefore, you must rank the genes and then use GSEA to analyze the ranked list of genes. For more information, see the GSEA Preranked Page in the GSEA User Guide.

Can I use GSEA to analyze paired samples?

No. GSEA software does not do paired-sample analysis. If you create a ranked list of genes by running a paired-sample marker analysis outside of GSEA, you can use GSEA to analyze that ranked list of genes. For more information about analyzing your own ranked lists of genes, see the GSEA Preranked Page in the GSEA User Guide.

Can I use GSEA to find pathways that correlate to the expression of my favorite gene?

Yes. In your phenotype file, create a continuous phenotype where the expression profile is that of your favorite gene.
You can have GSEA create the necessary phenotype for you: on the Run GSEA page, click the ... button next to the Phenotype labels parameter; when GSEA prompts you to select a phenotype, click the Use a gene as the phenotype button to have GSEA create a continuous phenotype for your gene. For more information, see the Phenotype labels parameter on the Run GSEA Page in the GSEA User Guide.

Can I use GSEA with gene sets that have both up- and down-regulated genes?

The GSEA software does not yet support this, but you can use the enrichment statistic with gene sets that include both up- and down-regulated genes. For one approach, see Lamb, et. al. (2006).

How do I cite GSEA?

For information on how to cite the gene set enrichment analysis, GSEA software, and/or MSigDB, please see Gsea_Citation.

GSEA Data Files

How do I create an expression dataset file? What types of expression data can I analyze?

GSEA can be used with expression data from any source; for example, two-color ratio data, CEL files, different species, and so on. All expression data must first be converted into a supported ASCII tab-delimited file format, such as  res, gct, or pcl file. If you have CEL files, you can use the ExpressionFileCreator module in GenePattern to convert your CEL files to the gct (or res) file required by GSEA.Most other data formats can easily be converted to these by reformatting the header and other simple modifications, such as column renaming, done by using a standard text editor. For information about the data file formats, see [[Data_Formats][Data Formats]].

If you have two-color ratio data, ratios are computed in one of two ways:

• normal sample (Cy3) / treated sample (Cy5) = phenotype
  In this case, rank the genes using software other than GSEA (for example, by a t-statistic in excel), then analyze the resulting ranked list of genes in GSEA. For information about analyzing ranked lists, see PreRanked page in the GSEA User Guide.
  
• normal sample (Cy3) / common reference (Cy5) = phenotype 1 and
  treated sample (Cy3) / common reference (Cy5) = phenotype 2
  In this case, run GSEA with default values and it will create the ranked list of genes and then analyze the ranked list.

For more information, see Preparing Data Files in the GSEA User Guide.

How do I filter or pre-process my dataset for GSEA?

How you filter or pre-process your data depends on your study. Here are a few guidelines to consider:

• Probe identifiers versus gene identifiers. Typically, your dataset contains the probe identifiers native to your microarray platform DNA chip. GSEA can analyze the probe identifiers or collapse each probe set to a gene vector, where the gene is identified by gene symbol. Collapsing the probe sets prevents multiple probes per gene from inflating the enrichment scores and facilitates the biological interpretation of analysis results.
• AP call filters.  You can run GSEA on filtered or unfiltered data. Typically, the GSEA team runs the analysis on unfiltered data. One suggested approach is to run  GSEA on the unfiltered data. If the results seem dominated by gene sets will poorly expressed genes, you might gain insight into what thresholds to use for the call filters.
• Expression values. The GSEA algorithm examines the differences in expression values rather than the values themselves. For example, you might have natural scale data or logged expression levels; you might have Affymetrix data or two-color ratio data.<a name="_Toc120959112"></a> As in most data analysis methodologies, the same expression data represented in different formats may generate different analysis results. The differences are expected. GSEA cannot determine which results are "correct."<a name="_Toc120959112"></a>

For more information, see Preparing Data Files in the GSEA User Guide.

How many samples do I need for GSEA?

This depends on your specific problem and data characteristics; however, as a rule of thumb, you typically want to analyze at least ten samples.

If you have technical replicates, you generally want to remove them by averaging or some other data reduction technique. For example, assume you have five tumor samples and five control samples each run three times (three replicate columns) for a total of 30 data columns. You would average the three replicate columns for each sample and create a dataset containing 10 data columns (five tumor and five control).

How do I create a phenotype label file? What types of experiments can I analyze?

GSEA can be used to analyze experiments of any type (including time-series, three or more classes, and so on). The phenotype labels (cls) ASCII file defines the experimental phenotypes and associates each sample in your dataset with one of those phenotypes. The cls file is an ASCII tab-delimited file, which you can easily create using a text editor. For more information, see Preparing Data Files in the GSEA User Guide.

What gene sets are available? Can I create my own gene sets?

You can use the gene sets in the Molecular Signature Database (MSigDB) or create your own. For more information, see Preparing Data Files in the GSEA User Guide.

How many genes should there be in a gene set?

GSEA automatically adjust the enrichment statistics to account for different gene set sizes, as described in the   Supplemental Information for the Gene Set Enrichment Analysis PNAS paper.

Can GSEA analyze a gene set that contains duplicate genes? duplicate gene sets?

Duplicate genes in a gene set and duplicate gene sets both effect GSEA results. GSEA automatically removes duplicate genes from each gene set, but does not check for duplicate gene sets. For more information, see Gene Sets in the GSEA User Guide.

Can GSEA analyze a gene set that contains genes that are not in my expression dataset?

The gene set enrichment analysis automatically restricts the gene sets to the genes in the expression dataset. The analysis report lists the gene sets and the number of genes that were included and excluded from the analysis.

What array platforms does GSEA support? What can I do if GSEA does not support my platform?

See DNA Chip (Array) Annotations in the GSEA User Guide.

GSEA Results

Where are the GSEA statistics (ES, NES, FDR, FWER, nominal p value) described?

For brief descriptions of the statistics that appear in the GSEA analysis report, see Interpreting GSEA in the GSEA User Guide. The Gene Set Enrichment Analysis PNAS paper also describes each of these statistics: for FDR and nominal p value, see the section titled Appendix: Mathematical Description of Methods; for FWER, see the section titled FWER in the supplemental information. 

Why does GSEA use a false discovery rate (FDR) of 0.25 rather than the more classic 0.05?

An FDR of 25% indicates that the result is likely to be valid 3 out of 4 times, which is reasonable in the setting of exploratory discovery where one is interested in finding candidate hypothesis to be further validated as a results of future research. Given the lack of coherence in most expression datasets and the relatively small number of gene sets being analyzed, using a more stringent FDR cutoff may lead you to overlook potentially significant results. For more information about gene set enrichment analysis results, see Interpreting GSEA in the GSEA User Guide.

Why does GSEA give me significant results with gene set (tag) permutation, but not with phenotype permutation?

Phenotype permutation generally provides a more stringent assessment of significance and produces fewer false positives. Which permutation type you should use depends on the number of samples that you are analyzing. For more information, see the description of the Permutation type parameter on the Run GSEA Page in the GSEA User Guide.

What should I do if I have no significant gene sets or too many significant gene sets?

The number of enriched gene sets depends on the structure of the data and the problem space. In general, one would expect to see at least a few gene sets enriched for a typical morphological or tissue-specific phenotype. If no enriched gene sets or a very large number of enriched gene sets pass the FDR threshold, first check that your gene sets and expression dataset use the same array format (see Consistent Feature Identifiers Across Data Files)  and that you have used the appropriate permutation type and number of permutations (see the Run GSEA Page). If you find no issues, consider the following:

• No enriched gene sets of significance may indicate that, in fact, no gene sets are enriched. It may also be that you are analyzing too few samples, the biological signal in question is subtle, or the gene sets that you are analyzing do not represent the biology in question very well. You may still want to look at the top ranked gene sets, keeping in mind that these results provide weak evidence for potentially interesting hypotheses. You might also want to consider analyzing other gene sets or, if possible, additional samples.
• Too many enriched gene sets of significance may indicate that, in fact, many gene sets are enriched between phenotypes. Perhaps the gene sets represent the same biological signal. You can check for this by looking for overlap in the leading-edge subsets within the gene sets Running a Leading Edge Analysis). Or, you might be seeing significant differences between the phenotypes due to technical artifacts, such as samples being run in different labs, by different operators, or against different arrays. As with too few enriched gene sets, you may still want to look at the top ranked gene sets, keeping in mind that these results provide potentially biased evidence for interesting hypotheses. You might also want to consider analyzing other gene sets or, if possible, additional samples.

For more information, see Interpreting GSEA in the GSEA User Guide.

What does it mean for a  gene set to have a nominal p value of zero?

A reported p value of zero (0.0) indicates an actual p-value of less than 1/number-of-permutations. For a more accurate p value, increase the number of permutations performed by the analysis. For more information about gene set enrichment analysis results, see Interpreting GSEA in the GSEA User Guide.

What does it mean for a gene set to have a small nominal p value (p<0.025), but a high FDR value (FDR=1)?

The nominal p value estimates the significance of the observed enrichment score for a single gene set. However, when you are evaluating multiple gene sets, you must correct for multiple hypothesis testing. The FDR is the estimated probability that a gene set with a given enrichment score (normalized for gene set size) represents a false positive finding.

Generally, when your top gene sets have small nominal p values and high FDRs, it is because they are not as significant when compared with other gene sets in the empirical null distribution. This could be because you do not have enough samples, the biological signal is subtle, or the gene sets do not represent the biology in question very well. Also, the FDR is based on all gene sets; if only one of many gene sets is enriched, that gene set is likely to have a high FDR.

For more information, see Interpreting GSEA in the GSEA User Guide.

What is the difference between the weighted statistic and the classic statistic? Which should I use?

See the description of the Enrichment statistic parameter on the Run GSEA Page in the GSEA User Guide.

Why are my results different from yours when I analyze the example datasets using GSEA?

You are using a different random number generator (for sample permutation) and different seeds for that random number generator, so the resulting numbers are different.

Why do some of the example datasets contain negative expression values?

Because in older releases of Affymetrix gene chips expression values were trimmed averages over match and mismatched probes. If the mismatch probes were higher a negative number results.

MSIGDB Gene Sets

What is MSigDB?

MSigDB, the Molecular Signature Database, contains curated gene sets for use with the gene set enrichment analysis. The GSEA team has begun the critical work of populating the MSigDb with curated gene sets. Increasing the number of gene sets increases the value of this resource; therefore, the GSEA team appreciates gene set contributions and encourages users to submit their gene sets to mailto:gsea@broad.mit.edu. For information about exporting gene sets from the MSigDB, see Gene Sets in the GSEA User Guide.

What is the difference between gene sets in MSigDB and GO/BioCarta/GenMAPP?

MSigDB contains gene sets formatted for use with GSEA. MSigDB places emphasis on a genomic, unbiased approach to the definition of gene sets; therefore, an important component of MSigDB is the collection of gene sets from published expression profiles. Unlike gene sets curated from prior knowledge (such as, GO, BioCarta, and so on), experimental sets provide an unbiased readout of a biological state; experimental sets from microarray experiments reflect purely transcriptional events.

Does MSigDB include pathway diagrams?

No.

Does MSigDB include GO gene sets?

The C2 (curated) category of gene sets contains a subcategory called Ontologies, which contains the GO gene sets. For a complete description of these gene sets, see the  MSigDB page.

Why do some MSigDB gene sets have the same gene represented multiple times?

The gene sets reflect the information in the original source and no attempt to modify the definition of a gene set is done (except for eliminating obvious gene duplications).  The gene sets defined in terms of gene symbols eliminate the duplication produced by multiple probes representing the same gene.

How do I use your gene sets to analyze data from my favorite array platform?

GSEA provides a utility, Chip2Chip, which translates the gene identifiers from gene symbols to the probe identifiers of your array platform. For more information, see Chip2Chip in the GSEA User Guide.

How do I find out more information about a particular MSigDB gene set?

Each gene set is described by a gene set card on the MSigDB page.

Can I use the MSigDB gene sets without using GSEA?

Yes. You can download the gene sets from the MSigDB page.

To download the MSigDB gene sets, you will need to fill in a brief GSEA registration form. The registration simply helps us to track usage and justify software support to funding agencies.

Is MSigDB available as a web service?

No for now.

 

GSEA Software

How do I increase the amount of memory available to GSEA?

From the GSEA web site, you can launch the GSEA desktop application with 512 Mb or 1 Gb of memory. To change the amount of memory available to GSEA, you can download the .jar file and then start the application using a direct call to the jar file. On the command line, add an -Xmx flag to the command line:

java -Xmx1024m -jar gsea2.jar


Be sure to run the Java command from the folder that contains the gsea2.jar file or specify the full path to the .jar file.  For 32-bit machines, -Xmx1800m appears to be the maximum allowed by Java. It is likely  higher on 64-bit machines, but has not yet been tested.

How do I run GSEA from the command line?

See Running GSEA from the Command Line in the GSEA User Guide.

How do I add GSEA to my microarray analysis pipeline?

If you are using GenePattern pipelines (http://www.broad.mit.edu/cancer/software/GenePattern/), GSEA is available as a GenePattern analysis module.

If you are implementing your own microarray analysis pipeline, GSEA can be run from the command line. Use full file specifications and the -Dhome parameter to ensure that you are reading data from and writing data to the desired locations. For more information, see Running GSEA from the Command Line in the GSEA User Guide.

Do I have to be connected to the internet to run GSEA software?

No. If you download the .jar file, you can use most functions in GSEA without being connected to the internet; for example, you can load files, run analyses, and review analysis results. However:

• The Chip platform(s) and Gene sets database parameters (on pages such as Run GSEA) display data files available from the Broad ftp site; these data files are not available when you are working offline. Be sure to download the chip files and gene set files that you need before disconnecting from the internet.
  
• The GSEA documentation and help files are on the GSEA web site; they are not available when you are working offline.

When working offline, clear the menu item Option>Connect over the internet.  By default, this item is selected and the Chip platform(s) and Gene sets database parameters display data files available from the Broad ftp site. Clearing the menu item disables this feature and avoids any time-consuming attempt to connect to the internet.

What version of R do I need for GSEA software?

 Version 1.9 or later.

What version of java do I need for GSEA software?

GSEA 2.0 requires Java 1.5 or later. If you do not have the correct version of Java installed when you start GSEA, an error message appears referring to an "unsupported class version."

Important note for Mac OS X users: You need Tiger (MacOS X 10.4) to run Java 1.5. See the "Requirements" section on <a href="http://www.apple.com/support/downloads/java2se50developerdocumentation.html">this page</a>.

How do I create the input files for GSEA in R?

The GSEA R code uses the gct, cls and gmt file formats for input. For more information, see Preparing Data Files in the GSEA User Guide.

Does GSEA have a programmatic API? What languages are supported?

The Broad Institute provides R and Java APIs on the Software page.

John Aach (Department of Genetics, Harvard Medical School) implemented a simplified version of the GSEA algorithm in MATLAB
as part of the paper Global gene expression of Prochlorococcus ecotypes in response to changes in nitrogen availability. Aach notes that the MATLAB code supports only that portion of the GSEA algorithm required for the paper: it processes a particular file format, its significance calculations are performed by shuffling genes only and not phenotypes, and it does not include multiple hypothesis corrections for different gene sets.

For more information, please visit  John Aach'sGSEA webapge