1. Course on Functional Analysis
::: Gene Set Enrichment Analysis - GSEA -
Madrid, Feb 16th, 2009.
Gonzalo Gómez, PhD.
Bioinformatics Unit
CNIO

2. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

3. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

4. GSEA ::: Introduction. MIT Broad Institute
Gene Set Enrichment Analysis - GSEA -
GSEA
MIT
Broad Institute
v 2.0 available since Jan 2007
v available since Feb 16th 2007
Version 2.0 includes Biocarta, Broad Institute,
GeneMAPP, KEGG annotations and more...
Platforms: Affymetrix, Agilent, CodeLink, custom...
(Subramanian et al. PNAS )

5. ::: Introduction. ::: How works GSEA?
Gene Set Enrichment Analysis - GSEA -
::: How works GSEA?
GSEA applies Kolmogorov-Smirnof test to find assymmetrical distributions for defined
blocks of genes in datasets whole distribution.
Is this particular Gene Set enriched in my experiment?
Genes selected by researcher, Biocarta pathways, GeneMAPP sets,
genes sharing cytoband, genes targeted by common miRNAs
…up to you…

6. ::: Introduction. ::: K-S test Dataset distribution
Gene Set Enrichment Analysis - GSEA -
::: K-S test
The Kolmogorov–Smirnov test is used to determine whether two underlying one-dimensional probability distributions differ, or whether
an underlying probability distribution differs from a hypothesized distribution, in either case based on finite samples.
The one-sample KS test compares the empirical distribution function with the cumulative distribution functionspecified by the null hypothesis.
The main applications are testing goodness of fit with the normal and uniform distributions.
The two-sample KS test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive to differences
in both location and shape of the empirical cumulative distribution functions of the two samples.
Dataset distribution
Gene set 2 distribution
Gene set 1 distribution
Number of genes
Gene Expression Level

7. ::: Introduction. ::: How works GSEA?
Gene Set Enrichment Analysis - GSEA -
::: How works GSEA?
ClassA ClassB
FDR<0.05
ttest cut-off
...testing genes independently...
Biological meaning?

8. - + ::: Introduction. ::: How works GSEA? ES/NES statistic
Gene Set Enrichment Analysis - GSEA -
::: How works GSEA?
Gene
Set 1
Gene
Set 2
Gene
Set 3 -
ClassA ClassB
Gene set 3
enriched in Class B
ttest cut-off
ES/NES statistic
Gene set 2
enriched in Class A +

9. ::: Introduction. ES examples :::
Gene Set Enrichment Analysis - GSEA -
ES examples :::

10. ::: Introduction. The Enrichment Score ::: NES pval FDR
Gene Set Enrichment Analysis - GSEA -
The Enrichment Score :::
NES
pval
FDR
Benjamini-Hochberg

11. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

12. ::: GSEA software. http://www.broad.mit.edu/gsea/ Download :::
Gene Set Enrichment Analysis - GSEA -
Download :::

13. ::: GSEA software. Main Window :::
Gene Set Enrichment Analysis - GSEA -
Main Window :::

14. ::: GSEA software. !!! Loading data :::
Gene Set Enrichment Analysis - GSEA -
Loading data :::
!!!

15. ::: GSEA software. Running GSEA :::
Gene Set Enrichment Analysis - GSEA -
Running GSEA :::

16. ::: GSEA software. Leading Edge Analysis :::
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::

17. ::: GSEA software. MSigDB ::: Chip to Chip Mapping :::
Gene Set Enrichment Analysis - GSEA -
MSigDB :::
Chip to Chip Mapping :::

18. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

19. ::: Data Formats.
Gene Set Enrichment Analysis - GSEA -

20. ::: Data Formats.
Gene Set Enrichment Analysis - GSEA -

21. ::: Data Formats. *.gct Expression datasets :::
Gene Set Enrichment Analysis - GSEA -
Expression datasets :::
*.gct

22. ::: Data Formats. *.res Expression datasets :::
Gene Set Enrichment Analysis - GSEA -
Expression datasets :::
*.res

23. ::: Data Formats. *.pcl Expression datasets :::
Gene Set Enrichment Analysis - GSEA -
Expression datasets :::
*.pcl

24. ::: Data Formats. *.txt Expression datasets :::
Gene Set Enrichment Analysis - GSEA -
Expression datasets :::
*.txt

25. For categorical phenotypes (e.g. Tumor vs Control)
::: Data Formats.
Gene Set Enrichment Analysis - GSEA -
Phenotype datasets :::
*.cls
For categorical phenotypes (e.g. Tumor vs Control)

26. For continuous phenotypes (e.g. Gene correlated to GeneSet)
::: Data Formats.
Gene Set Enrichment Analysis - GSEA -
Phenotype datasets :::
For continuous phenotypes (e.g. Gene correlated to GeneSet)
Time serie (each 30 minutes)
Peak profile wanted
For continuous phenotypes (e.g. Gene vs Time Series)

27. ::: Data Formats. *.gmx Gene Set Database :::
Gene Set Enrichment Analysis - GSEA -
Gene Set Database :::
*.gmx

28. ::: Data Formats. *.gmt Gene Set Database :::
Gene Set Enrichment Analysis - GSEA -
Gene Set Database :::
*.gmt

29. ::: Data Formats. *.chip *.grp Other formats:::
Gene Set Enrichment Analysis - GSEA -
Other formats:::
*.chip
*.grp

30. ::: Data Formats. *.rnk Ranked list format :::
Gene Set Enrichment Analysis - GSEA -
Ranked list format :::
*.rnk

31. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

32. ::: Using GSEA. Loading data :::
Gene Set Enrichment Analysis - GSEA -
Loading data :::
Los archivos se cargan automaticamente en la pantalla Run GSEA

33. ::: Using GSEA. Loading data ::: Seleccionar archivo ya importados
Gene Set Enrichment Analysis - GSEA -
Loading data :::
Seleccionar archivo ya importados

34. ::: Using GSEA. Running GSEA ::: Don´t forget help Option
Gene Set Enrichment Analysis - GSEA -
Running GSEA :::
Don´t forget help Option

35. ::: Using GSEA. ::: MSigDB. gsea_home
Gene Set Enrichment Analysis - GSEA -
::: MSigDB.
gsea_home

36. ::: Using GSEA. Running GSEA :::
Gene Set Enrichment Analysis - GSEA -
Running GSEA :::
1. Choose true (default) to have GSEA collapse each probe set in your expression dataset
into a single gene vector, which is identified by its HUGO gene symbol. In this case, you are
using HUGO gene symbols for the analysis. The gene sets that you use for the analysis must
use HUGO gene symbols to identify the genes in the gene sets.
2. Choose false to use your expression dataset "as is." In this case, you are using the probe
identifiers that are in your expression dataset for the analysis. The gene sets that you use for
the analysis must also use these probe identifiers to identify the genes in the gene sets.

37. ::: Using GSEA. Running GSEA ::: Phenotype Gene Sets (few samples)
Gene Set Enrichment Analysis - GSEA -
Running GSEA :::
Phenotype
Gene Sets (few samples)

38. ::: Using GSEA.
Gene Set Enrichment Analysis - GSEA -
Running GSEA :::

39. to the probe identifiers for a selected DNA chip.
::: Using GSEA.
Gene Set Enrichment Analysis - GSEA -
Chip2Chip mapping :::
Chip2Chip translates the gene identifiers in a gene sets from HUGO gene symbols
to the probe identifiers for a selected DNA chip.

40. ::: Using GSEA. Enrichment statistic :::
Gene Set Enrichment Analysis - GSEA -
Enrichment statistic :::
To calculate the enrichment score,
GSEA first walks down the ranked
list of genes increasing a running-sum
statistic when a gene is in the gene set
and decreasing it when it is not.
The enrichment score is the maximum
deviation from zero encountered during
that walk. This parameter affects the
running-sum statistic used for the
analysis.

41. ::: Using GSEA. Ranking Metric ::: Signal2Noise tTest Cosine Euclidean
Gene Set Enrichment Analysis - GSEA -
Ranking Metric :::
Signal2Noise
tTest
Cosine
Euclidean
Manhatten
Pearson (time series)
Ratio of Classes
Diff of Classes
Log2_Ratio_of_Classes
Categorical phenotypes
Continuous phenotypes

42. ::: Using GSEA. Ranking Metric :::
Gene Set Enrichment Analysis - GSEA -
Ranking Metric :::

43. ::: Using GSEA. Ranking Metric :::
Gene Set Enrichment Analysis - GSEA -
Ranking Metric :::

44. ::: Using GSEA. More parameters ::: real abs
Gene Set Enrichment Analysis - GSEA -
More parameters :::
real
8.2
8.1
8.0 …
-7.5
-7.7
-7.9
abs
7.9
7.7
7.5
parameter to determine whether to sort
the genes in descending (default) or
ascending order.

45. ::: Using GSEA. Launching Analysis :::
Gene Set Enrichment Analysis - GSEA -
Launching Analysis :::

46. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

47. By default in gsea_home
::: GSEA output.
Gene Set Enrichment Analysis - GSEA -
Results Accession :::
By default in gsea_home
C:\Documents and settings\username\gsea_home
/Users/yourhome/gsea_home

48. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

49. ::: GSEA results. Index.html ::: Gene Set Enrichment Analysis - GSEA -
Heat map of the top 50 features for each phenotype and a plot showing
the correlation between the ranked genes and the phenotypes. In a heat
map, expression values are represented as colors, where the range of colors
(red, pink, light blue, dark blue) shows the range of expression values
(high, moderate, low, lowest).

50. ::: GSEA results. Enrichment results in html :::
Gene Set Enrichment Analysis - GSEA -
Enrichment results in html :::

51. ::: GSEA results. Enrichment results in html :::
Gene Set Enrichment Analysis - GSEA -
Enrichment results in html :::

52. How can I decide about my results?
::: GSEA results.
Gene Set Enrichment Analysis - GSEA -
Enrichment results in html :::
How can I decide about my results?
FDR ≤ 0.25
NOM p-val ≤ 0.05

53. ::: Contents.
Introduction.
GSEA Software
Data Formats
Using GSEA
GSEA Output
GSEA Results
Leading Edge Analysis

54. ::: GSEA results. Leading Edge Analysis :::
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::

55. ::: GSEA results. Leading Edge Analysis ::: Set-to-Set HeatMap
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::
Set-to-Set
HeatMap
Histogram
Gene in Subsets

56. ::: GSEA results. Heat Map Leading Edge Analysis :::
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::
Heat Map
The heat map shows the (clustered) genes in the leading edge subsets. In a heat map, expression values are represented as colors,
where the range of colors (red, pink, light blue, dark blue) shows the range of expression values (high, moderate, low, lowest).

57. ::: GSEA results. Set-to-Set Leading Edge Analysis :::
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::
Set-to-Set
The graph uses color intensity to show the overlap between subsets: the darker the color, the greater the overlap between the subsets..
When you compare a leading edge subset to itself, its members completely overlap so the corresponding cell is dark green.
When you compare two subsets that have no overlapping members, the corresponding cell is white.

58. ::: GSEA results. Gene in Subsets Leading Edge Analysis :::
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::
Gene in Subsets
The graph shows each gene and the number of subsets in which it appears.

59. ::: GSEA results. Histogram Leading Edge Analysis :::
Gene Set Enrichment Analysis - GSEA -
Leading Edge Analysis :::
Histogram
The last plot is a histogram, where the Jacquard is the intersection divided by the union for a pair
of leading edge subsets. Number of Occurrences is the number of leading edge subset pairs in a
particular bin. In this example, most subset pairs have no overlap (Jacquard = 0).

60. ::: GSEA & FatiScan.
Gene Set Enrichment Analysis - GSEA -
Detects significant functions with Gene Ontology InterPro motifs, Swissprot KW
and KEGG pathways in lists of genes ordered according to differents characteristics.

61. ::: GSEA & Whichgenes. http://www.whichgenes.org
- Retrieve miRNAs targets for Gene Set Enrichment Analysis (miRBase, TargetScan)
- Always updated !
Enter if you simply want to download gene sets.
Login whether you want to download and store your gene sets

62. Try a preloaded example!!!
::: GSEA & Whichgenes.
Create Sets
1. Choose oraanism.
-Human
- Mouse
Looking for examples ?
2. Select source:
- miRBase, TScan
- Other sources
Try a preloaded example!!!
3. Copy and paste miRNAs identifiers.
Create set per items.
Retrieving targets
4. Job name.

63. ::: GSEA & Whichgenes. http://www.whichgenes.org Gene Sets Cart
1. Choose gene sets for downloading.
2. Select output format.
e.g. .CSV, .TSV, .gmt, .gmx
3. Select identifier.
e.g. Agilent, Affy, Mgi…
4. DOWNLOAD GENE SETS !!!

64. T H A N K S