Functional genomics and gene expression data analysis
Joaquín Dopazo
Bioinformatics Unit, Centro Nacional de Investigaciones Oncológicas (CNIO), Spain.
http://bioinfo.cnio.es
The use of high throughput methodologies allows us to query our systems in a new way but, at the same time, generates new challenges for data analysis and requires from us a change in our data management habits
National Institute of Bioinformatics, Functional Genomics node
From genotype to phenotype. (only the genetic component)
>protein kinase
acctgttgatggcgacagggactgtatgctgatctatgctgatgcatgcatgctgactactgatgtgggggctattgacttgatgtctatc....
…are expressed and constitute the
transcriptome...
… which accounts for the function providing they are expressed in the proper moment and place...
…in cooperation with other proteins (interactome) …
…conforming complex interaction networks
(metabolome)...
Genes in the DNA... …whose final
effect can be different because of the variability.
Now: 23531 (NCBI 34 assembly 02/04) Recent estimations: 20.000 to 100.000.
50% mRNAs do not code for proteins (mouse)50% display alternative splicing
Each protein has an average of 8 interactions
A typical tissue is expressing among 5000 and 10000
genes
More than 4 millon SNPs have been
mapped
25%-60% unknown
...and code for proteins (proteome)
that...
>protein kunase
acctgttgatggcgacagggactgtatgctgatctatgctgatgcatgcatgctgactactgatgtgggggctattgacttgatgtctatc....
Pre-genomics scenario in the lab
Sequence
Molecular databases
Search results
Phylogenetic tree
alignment
Conserved region
MotifMotif
databases
Information
Secondary and tertiary protein structure
Bioinformatics tools for pre-genomicsequence data analysis
The aim:
Extracting as much information as possible for one single data
Genome sequencing
2-hybrid systemsMass spectrometry for protein complexes
Post-genomic vision
ExpressionArrays
Literature, databases
Who?
Where, when and how much?
What do we know?
In what way?
SNPs
And who else?
http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html
genes
interactions
Post-genomic vision
Gene expression
Information
polimorphisms
InformationDatabases
The new tools:Clustering
Feature selectionData integration
Information mining
Gene expression profiling.The rationale, what we would like and related problems
Differences at phenotype level are the visible cause of differences at molecular level which, in many cases, can be detected by measuring the levels of gene expression. The same holds for different experiments, treatments, etc.
• Classification of phenotypes / experiments (Can I distinguish among classes, values of variables, etc. using molecular gene expression data?)
• Selection of differentially expressed genes among the phenotypes / experiments (did I select the relevant genes, all the relevant genes and nothing but the relevant genes?)
• Biological roles the genes are playing in the cell (what general biological roles are really represented in the set of relevant genes?)
A note of caution:
Question Experiment test
Is gene A involved in process B?
Experiment (sometimes) test Question
Is there any gene (or set of genes) involved in any process?
Genome-wide technologies allows us to produce vast amounts of data. But... data is not knowledgeMisunderstanding of this has lead to “new” (not necessarily good) ways of asking (scientific) questions
Cy5 Cy3
cDNA arrays Oligonucleotide arrays
Gene expression analysis using DNA microarrays
There are two dominant technologies: spotted arrays and oligo arrays although new players are arriving to the arena
Transforming images into data
Test sample labeled red (Cy5)Reference sample labeled green (Cy3)
Red : gene overexpressed in test sampleGreen : gene underexpressed in test sampleYellow - equally expressed
red/green - ratio of expression
Normalisation
Before (left) and after (right) normalization. A) BoxPlots, B) BoxPlots of subarrays and C) MA plots (ratio versus intensity)
(a) After normalization by average (b) after print-tip lowess normalization (c) after normalization taking into account spatial effects
There are many sources of error that can affect and seriously biass the interpretation of the results. Differences in the efficience of labeling, the hibridisation, local effects, etc.
Normalisation is a necessary step before proceeding with the analysis
A
B
C
The data
Characteristics of the data:
• Many more variables (genes) than measurements (experiments / arrays)
• Low signal to noise ratio
• High redundancy and intra-gene correlations
• Most of the genes are not informative with respect to the trait we are studying (account forunrelated physiological conditions, etc.)
• Many genes have no annotation!!
Genes(thousands)
Experimental conditions (from tens up to no more than a few houndreds)
A B C
Expression profile of a gene across the experimental conditions
Expression profile of all the genes for a experimental condition (array)
Different classes of experimental conditions, e.g. Cancer types, tissues, drug treatments, time survival, etc.
...
Co-expressing genes... What do they have in common?
Different phenotypes...
What genes are responsible for?
Genes interacting in a network (A,B,C..)...
How is the network?
A
B C
DE
Molecular classification of samples
Multiple array experiments.Can we find groups of experiments with similar gene expression profiles?
Unsupervised
Supervised
Reverse engineering
Unsupervised clustering methods:Useful for class discovery (we do not have any
a priori knowledge on classes)
Non hierarchical hierarchical
K-means, PCA UPGMA
SOM SOTA
Different levels of information
quick and robust
An unsupervised problem: clustering of genes.
•Gene clusters are unknown beforehand
•Distance function
•Cluster gene expression patterns based uniquely on their similarities.
•Results are subjected to further interpretation (if possible)
Perou et al., PNAS 96 (1999)
Clustering of experiments:The rationale
Distinctive gene expression patterns in human mammary epithelial cells and breast cancers
Overview of the combined in vitro and breast tissue specimen cluster diagram. A scaled-down
representation of the 1,247-gene cluster diagram The black bars show the positions of the clusters
discussed in the text: (A) proliferation-associated, (B) IFNregulated, (C) B lymphocytes, and (D) stromal
cells.
If enough genes have their expression levels altered in the different experiments, we might be able of finding these classes by comparing gene expression profiles.
Clustering of experiments:The problems
Any gene (regardeless its relevance for the classification) has the same weight in the comparison. If relevant genes are not in overwhelming majority it produces:
Noise
and/or
irrelevant trends
Supervised analysis.If we already have information on the classes, our question
to the data should use it.Class prediction based on gene expression profiles:
Problems:
How can classes A, B, C... be distiguished based on the corresponding profiles of gene expression?
How a continuous phenotypic trait (resistence to drugs, survival, etc.) can be predicted?
And
Which genes among the thousands analysed are relevant for the classification?
Genes(thousands)
Experimental conditions (from tens up to no more than a few houndreds)
A B C
Predictor
Gene selection
Gene selection.We are interested in selecting those genes showing differential expression among the classes studied.
• Contingency table (Fisher's test)
For discrete data (presence/absence, etc).
• T-test
We could compare gene expression data between two types of patients.
• ANOVA
Analysis of variance. We compare between two or more groups the value of an interval data. The pomelo tool
Gene selection and class discrimination
Genes differentially expressed among classes (t-test or ANOVA), with p-value < 0.05
10 10cases controls
Sorry... the data was a collection of random numbers labelled for two classes
This is a multiple-testing statistic contrast.
Adjusted p-values must be used!
NE EEC
NEEEC
Gene selection
between normal endometrium (ne) and endometrioid
endometrial carcinomas (eec)
G Symbol A Number
Hierarchical Clustering of 86 genes with different expression patterns between Normal Endometrium and Endometrioid
Endometrial Carcinoma (p<0.05) selected among the ~7000 genes in the CNIO
oncochip
Moreno et al., BREAST AND
GYNAECOLOGICAL CANCER LABORATORY, Molecular Pathology Programme, CNIO
And, genes are not only related to discrete classes...
Pomelo: a tool for finding differentially expressed genes
• Among classes
• Survival
• Related to a continuous parameter
Of predictors and molecular signaturesA B
Model, or classificator
A/B?
1 Training
(with internal and/or external CV)
A
2. Classification / predictionA/B?
CV
Unknown sample
Predictor of clinical outcome in breast cancer
van’t Veer et al., Nature, 2002
Genes are arranged to their correlation eith the pronostic groups
Pronostic classifier with optimal accuracy
Information mining How are structured?
Clustering
What is this gen?
Links
My data...
?
What are these groups?
Information mining
Cell cycle...
DBs Information
Information mining applications.
1) use of biological information as a validation criteria
Information mining of DNA array data. Allows quick assignation of function, biological role and
subcellular location to groups of genes.
Used to understand why genes differ in their expression between two different conditions
Sources of information: • Free text• Curated terms (ontologies, etc.)
Gene OntologyCONSORTIUM
http://www.geneontology.org • The objective of GO is to provide controlled vocabularies
for the description of the molecular function, biological process and cellular component of gene products.
• These terms are to be used as attributes of gene products by collaborating databases, facilitating uniform queries across them.
• The controlled vocabularies of terms are structured to allow both attribution and querying to be at different levels of granularity.
FatiGO: GO-driven data analysisThe aim: to develop a statistical framework able to deal with multiple-testing questions
The Gene Ontology Consortium. 2000. Gene Ontology: tool for the unification of biology. Nature Genetics 25: 25-29
GO: source of information. A reduced number of curated terms
How does FatiGO work? Compares two sets of genes (query and reference) Has Ontology information [Process, Function and Component] on
different organisms Select level [2-5]. Important: annotations are upgraded to the level
chosen. This increases the power of the test: there are less terms to be tested and more genes by term.
Cluster GenesQuery
ClusterGenes
Reference
Remove genes
repeated
in Cluster Query
Remove genes repeated
between Clusters
Remove genes
repeated
in Cluster Reference
CleanCluster Query
CleanCluster
Reference
GO – DB
Search GO term at level and ontology
selected
DistributionOf GO Terms
In QueryCluster
DistributionOf GO TermsIn Reference
Cluster
p-valuemultiple test
Important: since we are performing as many tests as GO terms, multiple-testing adjustment must be used
Number Genes with GO Term at level and ontology selected for each Cluster
Unadjusted p-valueStep-down min p adjusted p-value
FDR (indep.) adjusted p-valueFDR (arbitrary depend.) adjusted p-value
Tables GO Term – Genes
Genes of old versions (Unigene)Genes without result
Repeated Genes
GO Tree with diferent levels of information
FatiGO ResultsThe application extracts biological relevant terms (showing a significant differential distribution) for a set of genes
PTL LBC
Martinez et al., Human Genetics Laboratory. Molecular Pathology Programme, CNIO
Limphomas from mature lymphocytes (LB) and precursor T-lymphocyte (PTL).
Genes differentially expressed, selected among the ~7000 genes in the CNIO oncochip
Genes differentially expressed among both groups were mainly related to immune response (activated in mature lymphocytes)
Understanding why genes differ in their expression between two
different phenotypes
Martinez et al., Human Genetics Laboratory. Molecular Pathology Programme, CNIO
Biological processes shown by the genes differentially expressed among PTL-LB
Looking for significant differences.Statistical approaches
Don’t worry, be happy
2-fold increase/decrease
Individual test
Hundred of differentially expressed genes
Panic
Bonferroni
FWER
Hardly a few differentially expressed genes (or even none)
Looking for more heuristic and/or realistic ways of finding differentially expressed genes
Use of external information1. Use of biological information as a validation
criteria
2. Use of biological information as part of the algorithm
False Discovery Rate (FDR), controls the expected number of false rejections among the rejected hypotheses (differentially expressed genes), instead of the more conservative FWER, that controls the probability that one of more of the rejected hypotheses is true.
Necessity of a tool and the appropriate statistical framework for the management of the information
Applications2) Use of biological information as a
threshold criteria The problem:
We might be interested in understanding, e.g., which genes differ between tissues, diseases, etc.
Typically:
We examine each gene selecting only those that show significant differences using an appropriate statistical model, and correcting for multiple testing.
The threshold, thus, is based on expression values in absence of any other information. Conventional levels (e.g., Type I error rate of 0.05) attending exclusively to statistical criteria are used.
A B
B
A
Metabolism
Transport
...Reproduction
Use biological information as a validation criteria
Use of biological information as a threshold criteria
Information-driven approach
We examine the GO terms associated to each gene and see, correcting for multiple testing, if some of them are overrepresented
The threshold is based on levels (e.g., Type I error rate of 0.05) of distribution of GO terms
A B
B
A
GO terms
Metabolism
Transport
...Reproduction
Present
Absent
The rationale: genes are differentially expressed because some biological reason
The procedure becomes more sensitive
Other approaches that include
information in the algorithm: GSEA
Figure 1: Schematic overview of GSEA.The goal of GSEA is to determine whether any a priori defined gene sets (step 1) are enriched at the top of a list of genes ordered on the basis of expression difference between two classes (for example, highly expressed in individuals with NGT versus those with DM2). Genes R1,...RN are ordered
on the basis of expression difference (step 2) using an appropriate difference measure (for example, SNR). To determine whether the members of a gene set S are enriched at the top of this list (step 3), a Kolmogorov-Smirnov (K-S) running sum statistic is computed: beginning with the top-ranking gene, the running sum increases when a gene annotated to be a member of gene set S is encountered and decreases otherwise. The ES for a single gene set is defined as the greatest positive deviation of the running sum across all N genes. When many members of S appear at the top of the list, ES is high. The ES is computed for every gene set using actual data, and the MES achieved is recorded (step 4). To determine whether one or more of the gene sets are enriched in one diagnostic class relative to the other (step 5), the entire procedure (steps 2–4) is repeated 1,000 times, using permuted diagnostic assignments and building a histogram of the maximum ES achieved by any pathway in a given permutation. The MES achieved using the actual data is then compared to this histogram (step 6, red arrow), providing us with a global P value for assessing whether any gene set is associated with the diagnostic categorization.
Mootha et al., Nat Genet. 2003 Jul;34(3):267-73
ISW applied to a dataset for which no genes differentially expressed
could be found
IGT + Diabetic Normal tolerance to glucose
Mootha et al., Nat Genet. 2003
17 NTG vs.
8 IGT 18 DM2
No differentially expressed genes between both conditions were found.
Gene Set Enrichment Analysis detects Oxidative phosphorylation
ISW detects 5 pathwaysarrangement
Pathways over- and underrepresented
Algorithms are used if they are available in programs.GEPAS, a package for DNA array data analysisArray
Scanning,
Image processing
Preprocessor+ hub
Supervised clustering
SVM
Unsupervised clustering
HierarchicalSOMSOTA
SomTree
Datamining
FatiGO
FatiWise
Viewers
SOTATreeTreeViewSOMplot
External tools
EP, HAPI
Two-conditions comparisonGene selection
Two-classes
Multiple classes
Continuous variable
Categorical variable
survival
NormalizationDNMAD
Predictor
tnasas
In silico CGH
Bioinformatics Group, CNIO
http://bioinfo.cnio.es http://gepas.bioinfo.cnio.es
http://fatigo.bioinfo.cnio.es
From left to right: Lucía Conde, Joaquín Dopazo, Alvaro Mateos, Fátima Al-Shahrour, Víctor Calzado, Hernán Dopazo, Javier Herrero, Javier Santoyo, Ramón Díaz, Michal Karzinstky & Juanma Vaquerizas