Title: Gene Ontology as a tool for the systematic analysis of large-scale gene-expression data
1Gene Ontology as a tool for the systematic
analysis of large-scale gene-expression data
- Stefan Bentink
- Joint groupmeeting Klipp/Spang
- 11-20-2002
2Overview
- Microarrays and the Gene Ontology (GO) database
- Scoring differential gene-expression in GO groups
- Checking scores against different null
hypothesises - Sample data (two types of Breast Cancer) and
results
3Overview
- Microarrays and the Gene Ontology (GO) database
- Scoring differential gene-expression in GO groups
- Checking scores against different null
hypothesises - Sample data (two types of Breast Cancer) and
results
4Microarrays sample scheme
5Microarrays comparative analysis
sample tissue I 1,2,... tissue II 1,2,...
gene 1 mean mean gt t-value
gene 2 mean mean gt t-value
gene 3 mean mean gt t-value
...
6How to interprete the data?
- Long list of siginficant genes
- Which genes are of interest?
- Solution pooling of genes into functional
classes - provides a general overview
- Gene Ontology database provides such a functional
classification
7The Gene Ontology database
8The Gene Ontology database
- GO is a database of terms for genes
- Known genes are annotated to the terms
- Terms are connected as a directed acyclic graph
- Levels represent specifity of the terms
9The Gene Ontology database
Apoptotic protease activator
10The Gene Ontology database
- Every child-term is a member of its parent-term
- GO contains three different sub-ontologies
- Molecular function
- Biological process
- Cellular component
- Unique identfier for every term
- GO0003673(rootGene Ontology)
11Gene Ontology and microarrays
- Hypothesis Functionally related, differentially
expressed genes should accumulate in the
corresponding GO-group. - Problem Find a method, which scores accumulation
of differential gene expression in a node of the
Gene Ontology.
12Gene Ontology and microarrays
P-value for every gene by a two-sample t-test
13Overview
- Microarrays and the Gene Ontology (GO) database
- Scoring differential gene-expression in GO groups
- Checking scores against different null
hypothesises - Sample data (two types of Breast Cancer) and
results
14Scoring methods
?
- Number of significant genes in a GO-group
- Sum of negative logarithms of all p-values
- supP(n)-F(n) according to Kolmogorov-Smirnov
15The p-value
tlt0 gt p cdf tgt0 gt p 1-cdf gt p(0,
0.5 m(0, 1 m2p
t
- cdf cummulative distribution function
16Sum of log-score
- Pavalidis, Lewis, Noble 2001 Zien, Küffner,
Zimmer, Lengauer 2000 - 2p -gt 1 gt -log(2p) -gt 0
- Small p-values, high score
17Kolmogorov-Smirnov-Score
SsupP(n)-F(n) P(n) p-values for genes that
fall into a GO-group. F(n) equally
distributed values between 0 and 1.
18Overview
- Microarrays and the Gene Ontology (GO) database
- Scoring differential gene-expression in GO groups
- Checking scores against different null
hypothesises - Sample data (two types of Breast Cancer) and
results
19Null hypothesises
- The significant genes (according to Bonferoni
a0.05/n) are distributed over the GO-groups by
chance - The existing differential gene expression is
distributed over the GO-groups by chance - There is no differential gene expression in a
GO-group
20Checking H0 by permutation
Permutation of rows Mapping of p-values into
GO-groups is randomized. H0 Distribution of
differential gene expression
Permutation of columns Level of p-values is
randomized. H0 No differential gene expression
in a GO-group
21Checking H0 by permutation
- 1000 random permutations gt background
distributions - H0 Distr. of significant genes
- Randomizing GO-groups (rows)
- H0 Distr. of all p-values
- Randomizing GO-groups (rows)
- H0 Level of p-values
- Permutation of columns
22Methods (summary)
Data P-values
23Overview
- Microarrays and the Gene Ontology (GO) database
- Scoring differential gene-expression in GO groups
- Checking scores against different null
hypothesises - Sample data (two types of Breast Cancer) and
results
24Results Data (Breast Cancer)
- Two major subclasses
- Estrogen receptor postive (ER)
- Estrogen receptor negative (ER-)
- Estrogen receptor postive
- Succeptible to Tamoxifen
- Slightly better survival rate
- Great molecular differences between the two types
25Results Data (Breast Cancer)
- Data 25 ER, 24 ER-
- Array Affymetrix HuGeneFL
- 7000 Genes
- 4000 annotated to GO-terms
- Data were normalized by variance stabilization
(Heydebreck et. al 2001)
26Results Pre-conditions
- GO-group considered to be significant if less
than 5 of the random permutations exceeds the
score - Only GO-groups with more than 5 and less than
1000 genes were taken into account
27Results Number of significant genes
According to the pre-conditions 16 GO-groups were
found
28Results Permutation of rows (distribution
hypothesis)
Sum of log P Kolmogorov-Smirnov
29Results Permutation of columns (differential
gene-expression hypothesis)
Sum of log P Kolmogorov-Smirnov
30Results
- The column-permutation leads to a very low
background distribution - Many significant GO-groups
- May help to find functional groups without
differential gene-expression - Different scoring methods seem to be
complementary as indicated by the results of the
row-permutation
31Results Permutation of the rows
Sum of log 44 GO-groups were found (5 cond.,
...)
KS-score 77 GO-groups were found (5 cond., ...)
GO0000087 M-Phase of mitotic cell-cycle (37
genes)
32Results Comparing the scoring-methods (from the
row-permutation)
A counting of significant genes in GO-groups B
Kolomogorov-Smirnov C sum of logarithms
A 16 B 77 C 43 A and B 3 A and C 13 C and
B 13 A, B and C 3 C without A 30 B without
A 74
33Browsing the results
34Results Interesting GO-term (M-Phase)
- Contains a couple of interesting proliferative
genes (p-value 510-4 gt not significant) - E.g. polo-like kinase
- t-value -3.45 p-value 5.5910-4
- would not been found by a single-gene approach
- correlation with ER-Receptor could be found in
literature (Wolf et al, 2000)
35Summary/ outlook
- GO provides a general view on large-scale
gene-expression data - Less deregulated but very interesting genes could
be found - Third null hypothesis gt differential gene
expression over a wide range of genes (outlook
which GO-groups contain no differential
gene-expression) - No bias of scores by top-level genes (outlook
leaving out top-level genes for scoring) - Possible modification of scoring-methods up- and
downregulation