Understanding the Broad Institute’s GSEA and hands-on training with the software Presented by Alan E. Berger, Ph.D.

Lowe Family Genomics Core, School of Medicine, Johns Hopkins University

September 30, 2014 NIH Building 10 FAES Classroom 1

Loca

tions

of g

enes

in

1 g

ene

set

• Using gene sets, e.g., pathways, GO categories, to interpret microarray (and other) biology data• Using a measure of differential expression for all the genes, rather than a list of distinguished genes• The general approach of the Broad Institute’s GSEA software // comparison with DAVID (NIAID)• The statistics behind GSEA // The data files and formats required to use GSEA• Hands on running the GSEA software (using output data from Partek runs)• Understanding the output files produced by GSEA

The content of this set of slides is derived from several NIH-CIT tutorials on GSEA given by Aiguo Li, Ph.D., NIH-NCI and Alan Berger, Ph.D., in 2007 & 2008, and from slides prepared by Alan Berger and Maggie Cam, Ph.D., NIH-NCI for April and December 2013 NCI-BTEP classes on GSEA

my contact information:Alan E. Bergeraberger9@jhmi.edu410-550-5089

Underlying figure from http://www.broadinstitute.org/gsea/index.jsp (April 2013)

2. Provide expression data

1. P

rovi

de

Gen

e se

ts

Running GSEA

3. Choose parameters via GSEA interface

4. Decide if the GSEA output contains informative results

Main Data Files for Input

• Gene sets database files

– GeneMatrix (filename.gmt) in local machine (download from the Broad MSigDB)

• Expression Data files (expression values or differential expression levels)

– Gene Cluster Text file: filename.gct (full expression data)

– Ranked list file format: filename.rnk (condensed differential expression data)

– ExpRESsion (with P and A calls) file: filename.res (a format for Affymetrix data)

• Phenotype information files (categorical = specify the group for each sample)

– Need a .cls file if using full expression data (.gct)

– Categorical (e.g. tumor vs normal) class file format: filename.cls

– Continuous (e.g. clinical data or a gene profile) file format: filename.cls

see http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#Expression_Data_Formats

Note the user must have done any desired normalization and transformation of expression data before submitting it to GSEA; GSEA suggests no transformation.

From http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html April 2013

Information available from the Broad Institute GSEA documentation

Subset of the web page http://www.broadinstitute.org/gsea/datasets.jsp April 2013

There are additional example datasets in the R-distribution of GSEA

Tab delimited text file but NO whitespace allowed after the last gene symbol in each row

A gene sets file (.gmt) for GSEA

Schematic of a .gmt Gene Matrix Transposed Gene Sets file (Each Row is 1 Gene Set)

Gene Set Names, one per row, names must be unique

Gene Set Description, could be just “na”

Gene Identifiers, number of genes per gene set can vary, Case Sensitive (use CAP gene names in expression data files for gene sets from MSigDB)

Gene Identifiers can be probe set IDs or gene symbols but MUST BE CONSISTENT WITH column 1 of the .gct file (the chip feature IDs). If using a .chip file within Java-GSEA then col 1 of the .gct and .chip files must correspond & the gene symbols here should be those used within the Gene Symbol column of the .chip file.

(tab delimited text file)

Figure from http://www.broadinstitute.org/gsea/msigdb/index.jsp April 2013

Figure from http://www.broadinstitute.org/gsea/msigdb/collections.jsp#C2 April 2013

From http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html April 2013

Information available from the Broad Institute GSEA documentation

• Want at least 7 in each group to do sample label (phenotype) permutation

• If insufficient samples use gene set permutation

• If want a difference measure not in the GSEA menu use the GSEA Preranked option (and, perforce, gene set permutation)

• Gene set permutation requires a more stringent FDR criterion (0.05, as opposed to 0.25 for sample label permutation)

Sample label or gene set permutation

.rnk file example (tab delimited text, may have 1 header line)

signed ranking metric: e.g., fold change, t-score, Gaussian Z value corresponding to (t-test p-value) / 2. No extra columns allowed. (In this example positive is up in Treated, negative values are up in the Control group.)

Treated day 10 vs. Control culture day 10

Tscore; positive is up in Treated

Ideally, collapse duplicate gene symbols by picking the ranking metric value with the largest magnitude (e.g., {2., 3., -4.} -4.)

Gene symbols gene names must match the gene names used in the gene sets file, including case (generally use ALL CAPS), since lose information on non-matched genes; MSigDB uses human genome gene symbols

Control

Treated

Samples: Glioma stem cells (GSC) were grown from tumor derived from a GBM patient. The GSC’s were cultured in control media or differentiation media (Retinoic Acid and serum) for 3 and 10 days, RNA was extracted and processed using Affymetrix U133Plus2 arrays. While the cells in control media continued to actively proliferate, ones in the differentiation media were found to slow down and differentiate into glial and/or neural lineage. The goal of the experiment is to find regulatory mechanisms driving differentiation of tumor stem cells for drug targets.

Exercise: Microarray Analysis using Partek and GSEA

Day 3 Day 10

NBE growth medium, optimal for propagation & nondiffer. of normal neural stem cells, produces GBM cells that behave more like the parent tumor

Figure 8 above is from:

Cancer Cell v9 May 2006.

‘‘NBE’’ conditions: serum-free Neurobasal media supplemented with basic FGF and EGF

Window to load required GSEA files: .gmt ; {.rnk or .gct & .cls} (and if used -- .chip)

after hit Load Data, get this window display

Clicking here allows you to select a folder and then choose a file in that folder to load into GSEA. One by one load in the genes sets file and expression data file(s) for your run. Files loaded in recently can be selected (double click) from here

For today’s class now load in:c2.cp.v4.0.plus.c5.all.v4.0.symbols_fromidlcat.gmt & GSEA_gene_list_2_Sept18_from_Xiaowen_Wang_zip_file.txt.rnk

After load the required data files, invoke GSEA either here (Tools --> Preranked option) orhere (run using full expression dataset (requires gct and cls files)). Get this screen with the Preranked option which we will run now. Click on first field.

Since have loaded the gene sets file (.gmt), choose the local option

Lists all loaded gene sets files, choose one

For today’s class will have loaded in c2.cp.v4.0.plus.c5.all.v4.0.symbols_fromidlcat.gmtwhich combines canonical pathway gene sets plus GO gene sets from MSigDB

First set # permutations to 100 so will run fast (normally use 1000)Next choose the ranked list file GSEA_gene_list_2_Sept18_from_Xiaowen_Wang_zip_file.txt.rnk (already loaded)

Turn off (choose false) “collapse dataset …” in which case the “chip platform(s)” entry is irrelevant (leave blank).

GSEA parameters for run of Treated day 10 vs. Control day 10 with the Tscore as the ranking metric. Using canonical pathways gene sets plus GO gene sets

23161 unique gene symbols in this dataset

So will run in short amount of time

Set CPU usage

Start GSEAUse 13847 for today’s class40 is a reasonable choice

Pick a specific random number generator seed so can exactly reproduce the result

use very descriptive folder/file names so can figure out what was run later on

choose folder where output folder GSEA creates will be placed

For full run use 1000 permutations

When done, the status bar will (hopefully) have “success” in it (followed by some number) (and your computer may make a distinct noise when GSEA finishes a run)

From http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html April 2013

gct & res Expression Data File Formats (tab delimited text files, displayed here using

Excel):

*.gct file: gives feature identifiers in column 1 and gene expression data

*.res file: ExpRESsign with P, A, M calls and gene expression data (GSEA does not use the present / absent / marginal calls)

gene names must match the gene names used in the gene sets file, including case (generally use ALL CAPS)

Screen Image of P53.gct file (gene cluster text tab delimited file)

P53.gct tab delimited text file (displayed here in Excel) from the R-GSEA distribution from the Broad Institute: http://www.broad.mit.edu/gsea/index.html

required, always the same # probe sets or genes, i.e., # rows of expression data

# samples

NO Dashes (–) allowed in GSEA file names (Java)

sample identifiers, must be unique

chip feature IDs

Expr

essi

on le

vels

expression levels (or logs), for a missing value leave cell empty

can be a non- blank filler

Case Sensitive

The entries in cell A3 (NAME) and in cell B3 (DESCRIPTION) (rotated here to fit) are required

Sample categorical .cls (class) file: Specify phenotype of each sample, e.g., tumor-type1, tumor-type2, normal; treated, control; same order from left to right as in the expression file (the .gct file)

Categorical class file: 3 lines, space delimited text filethe 1 is required, does not change

line 3 can be tab delimited

Symbols corresponding to the classes of the samples in the .gct file; the 1st (D) <2nd (A)> distinct symbol in line 3 corresponds to the 1st <2nd> name in line 2 (ASP <ALA>)

1 1 1 0 0 1 0 0 1 0 1 alternate acceptable line 3 for this class file

ASP ALA

# samples in .gct file, # of phenotypes, 1

class names used by GSEA in output data

another class file example

Example of a “Numeric” Class File

A “numeric” .cls file, is of the form one would have in order to use the “Pearson correlation” gene ranking metric (for example, to rank the genes/gene sets by correlation with a clinical variable). If one had time series data, so, e.g., the samples give expression data of some system at a sequence of time points, the numeric values in line 3 (one for each sample, ordered as the samples are ordered in the .gct file) could be an expression pattern over time one was looking to have gene sets match. Or these values could be the expression levels of a gene one was looking to match, or a clinical variable, e.g., a measure of disease severity for each sample.

Identifies this as a “numeric” .cls file

arbitrary text used in some of the GSEA output file names

vector V of numbers, one for each sample; genes will be ranked by a measure of their correlation with V

Illustration of .chip Description File

see Zeeberg et al. BMC Bioinformatics 2004 for info on proper text file import into Excel

Excel display of a modified section of: HG_U133A.chip. Chip files are available from the Broad Institute

UNIQUE ID | if none enter --- or null or na | if none enter --- or null or na

column headers MUST be as displayedGSEA may optionally combine duplicate expr. values

tab delimited text file (optionally used by Java GSEA to convert feature IDs to gene symbols)

IDs & symbols are case sensitive

for more info see the GSEA documentation pageIDs should corr. to col 1 of .gct file

Use either these probe Set IDs in the gene set .gmt file or use these gene symbols

A .chip file can help matchup of gene symbols in data with those in the gene sets

Avoiding corruption of date like gene symbols when editing a tab delimited text file with Excel (define all columns containing gene symbols as text)

Excel open file

delimited tab delimited

Otherwise, may have to deal with fixing corrupted date like gene symbols (subset of a script)

Sample input choices for a test run for Desktop Java GSEA

P53.gct, C2.gmt, P53.cls were previously loaded

small # for test run

P53.gct already has gene symbols in col 1, so no .chip file needed

Click when have finished selecting parameters

Once a test run checks out, use, e.g., 1000 permutations; can check stability of NES & FDR results by varying #permutations, varying the initial random number generator seed (see next slide)

Sample input choices for Desktop GSEA: Advanced Fields

Choices for measures of differential expression

this means using NES

choose an explicit integer seed for random # generator so can easily reproduce results

Some of the GSEA Parameters and Defaults

Parameters Default Option Collapse dataset to gene symbols

True True or False (if true need to supply a .chip file)

Permutation type Phenotype Phenotype or Gene_set Enrichment statistics Weighted Classic, weighted <p=1>,

weighted_p2, weighted_p1.5 Metric for ranking genes Signal2Noise Signal2Noise, tTest, Pearson,

Ratio_of_classes, Diff_of_classes, Log2_ratio_of_classes, Euclidean, Manhatten, cosine

Gene list sorting mode Real Real or Abs Gene list ordering mode Descending Descending or Ascending Maximum size 500 User defined fields Minimum size 15 User defined fields Collapsing for probe set Max_probe Max_probe or Median of probes Number of permutations 1000 User defined fields Normalization mode meandiv Meandiv (use NES) or None (use ES) Seed for permutation timestamp We recommend putting in a user

chosen positive integer < 2^32

Measures of Differential Expression

Let the expression data consist of samples from two phenotypes A and B. For a given gene g: let A be the mean of the expression levels for g from the subset of samples having phenotype A & similarly for B; and likewise with standard deviations A and B. Then the signal2noise (GSEA default) measure of differential expression of g between A and B used as the gene ranking metric is:

A - B

A + B signal2noise(g)

A number of other options are available from the Desktop GSEA, including tTest, log2_Ratio_of_Classes, Ratio_of_classes, and several measures of correlation for continuous phenotypes; see “Metrics for Ranking Genes” in http://www.broad.mit.edu/cancer/software/gsea/doc/GSEAUserGuideFrame.html

From http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html April 2013

From http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html April 2013

Information available from the Broad Institute GSEA documentation

From http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html April 2013

Information available from the Broad Institute GSEA documentation

Information available from the Broad Institute GSEA documentation

Control

Treated

Samples: Glioma stem cells (GSC) were grown from tumor derived from a GBM patient. The GSC’s were cultured in control media or differentiation media (Retinoic Acid and serum) for 3 and 10 days, RNA was extracted and processed using Affymetrix U133Plus2 arrays. While the cells in control media continued to actively proliferate, ones in the differentiation media were found to slow down and differentiate into glial and/or neural lineage. The goal of the experiment is to find regulatory mechanisms driving differentiation of tumor stem cells for drug targets.

Exercise: Microarray Analysis using Partek and GSEA

Day 3 Day 10

• examining the GSEA output

• deciding if the output is statistically meaningful

• harder: deciding if the output is biologically informative

Hands-on sample runs of GSEA

GSEA output foldersorting files by date, most recent first, will get the files you want to look at on top

GSEA output folder from cell line A, day 10, Differentiated vs. Control Tscore as metricUsing canonical pathways gene sets plus GO gene sets

GSEA adds the analysis type and a unique timestamp to the user provided folder name

sorting files by date, most recent first, will get the files you want to look at on top

The .xls (Excel) files in the GSEA output folder are actually tab delimited text files, so will get this warning message when open one - just click on yes

GSEA output from cell line A, Treated day 10 vs. Control day 10 Tscore as metricUsing canonical pathways gene sets plus GO gene sets

Gene sets up in Control day 10 vs. differentiated (Treated) day 10

In the corresponding .html file, clicking on the gene set name, or on “details” gets additional information

GSEA output from cell line A, Treated day 10 vs. Control day 10 Tscore as metricUsing canonical pathways gene sets plus GO gene sets

Gene sets up in differentiated (Treated) day 10 vs. Control day 10

Gene set permutation (e.g., Preranked option) requires a more stringent FDR criterion (GSEA FDR q-val ≤ 0.05, as opposed to 0.25 for sample label permutation with full dataset)

Sample label or gene set permutation good FDRs

Gene set description from the GSEA GUI interface

GSEA running enrichment score plot

Information on the genes in the Leading Edge

of the DNA Replication

gene set

(header row image was pasted in)

23161 unique gene symbols in this dataset so 23117 is pretty close to the most negative end of the ranked list of genes

GSEA output from cell line A, Treated day 10 vs. Control day 10 Tscore as metricUsing canonical pathways gene sets plus GO gene sets

Gene sets up in differentiated (Treated) day 10 vs. Control day 10

Gene set description from the GSEA GUI interface

Information on the pathway

GSEA running enrichment score plot

Information on the genes in the gene set

Appendix: how to use Excel to remove rows with duplicate gene names and keep the highest magnitude metric value that occurred with each gene.

Starting with a tab delimited text file or Excel file that has NON-BLANK gene symbols in column A and a corresponding signed metric value such as fold change or T-score in column B (remember to import tab delimited text files into Excel with gene symbol columns as text so date like gene symbols are not corrupted into dates);

want to combine rows having the same gene symbol in column A and have the entry in column B for that gene symbol be the metric value having the largest absolute value among all the values that appeared with that gene symbol; for example if IFNG appeared in 3 rows with metric values 3.1, 1.2, and -3.9; want the condensed file to have 1 row for IFNG with the corresponding entry in column B to be -3.9 The intended use is for .rnk (ranking metric) files for GSEA, in which case the order of the genes in the file does not matter.

Will do the following steps:Here is a little test spreadsheet

One can check by hand that the desired result is (here the genes are in alphabetical order but that is not necessary for GSEA)

gene symbol metric valuea 3b -4c -2d 3e -5

First add column C, with its entries (below the header line) equal to the absolute value of the entries in column B

Then copy column C and via Excel’s Paste Values, paste the values (not the formulas) from column C into column D

Then delete column C (that had the ABS formulas in it) to get

Next sort on column C, from high to low (Excel sort Z to A) (sorting in Excel when there are formulas can take lots more time (with a big Excel file) and in my hands can lead to unpredictable results)

And we now have this

We will next use Excel’s “Remove Duplicates” facility which when focused on column A will remove rows containing repeats of previously occurring gene symbols. Since we have already arranged to have the first row with a given gene symbol to have the largest magnitude metric value in column B that occurred with that gene symbol, the first two columns (after having done “Remove Duplicates”) will, when saved as a tab delimited text file with the .rnk extension, be of the desired form for the Broad Institute’s Gene Set Enrichment Analysis (GSEA) software.

Highlight the data (click on the upper left cell (A1), then (holding the shift key down) click on the lower right cell of the data) and then click on data

Then click on “Remove Duplicates” and will see a box in which to make selections

Have a check mark ONLY in the box next to the “gene symbol” column and make sure the “My data has headers” box is checked

Then click OK

Click OK, and then columns A and B are as desired for a GSEA .rnk file

One can see that the result is correct (recall the order of the genes does not matter for GSEA). Delete column C and save as a tab delimited text file with the .rnk extension as required for GSEA ranking metric files, and are done

gene symbol metric valuea 3b -4c -2d 3e -5

The correct values from slide 1 (here the genes are in alphabetical order but that is not necessary for GSEA)