Building a Global Map of (Human) Gene Expression Misha Kapushesky European Bioinformatics Institute, EMBL St. Petersburg, Russia May, 2010
Building a Global Map of (Human) Gene Expression
Misha KapusheskyEuropean Bioinformatics Institute, EMBL
St. Petersburg, RussiaMay, 2010
From one genome to many biological states
• While there is only one genome sequence, different genes are expressed in many different cell types and tissues, different developmental or disease states
• The size and structure of this “expression space” is still largely unknown
• Most individual experiments are looking at small regions• We would like to build a map of the global human gene
expression space
Mapping the human transcriptome
Traditional researchA microarray experiment
Everest Lhasa
Kathmandu
The map we want to build
How to build such a global map
• This space is huge - There are thousands of potentially different states – cell types, tissue types, developmental stages, disease states, systems under various treatments (drugs, radiation, stress, …) –
• It is not feasible to study them all in a single laboratory experiment (costs, rare samples, …)
• However thousands of gene expression experiments are performed every year (microarrays, new generation sequencing)
• Can we use the published data to build the global expression map?
ArrayExpress
• www.ebi.ac.uk/arrayexpress• Data from over 280,000 assays and over 10,000
independent studies (microarrays, sequencing, …)• Gene expression and other functional genomics assays• Over 200 species• Data collection and exchange from GEO
Can we integrate these data to answer questions that go beyond what was done in the individual studies?
• On a quantitative level - data on only the same microarray platform can be integrated
A global map of human gene expression
• Angela Gonzales (EBI)• Misha Kapushesky (EBI)• Janne Nikkila (Helsinki
University of Technology) • Helen Parkinson (EBI), • Wolfgang Huber (EMBL)• Esko Ukkonen (University of
Helsinki)
Margus Lukk et al, Nature Biotechnology, 28, p322-324 (April, 2010)
• We collected over 9000 raw data files from Affymetrix U133A from GEO and ArrayExpress
• Applying strict quality controls, removing the duplicates • Data on 5372 samples remained
from 206 different studies generated in 163 different laboratoriesgrouped in 369 different biological ‘conditions’ (tissue types,
diseases, various cell lines, etc)• The 369 conditions grouped in different larger
‘metagroups’
The most popular gene expression microarray platform: Affymetrix U133A
First 3 (5) principal components
1. Hematopoietic axis – blood, ‘solid tissues’, ‘incompletely differentiated cells and connective tissues’
2. Malignancy axis - Cell lines – cancer – normals and other diseases
3. Neurological axis – nervous system / the rest 4. RNA degradation5. Samples seem to ‘cluster’ by the tissues of origin
Human gene expression map02/03/201626
Hierarchical clustering of 97 groups with at least 10 replicates each
Comparison of the 97 larger sample groups to the rest
Incompletely differentiated cell type and connective tissue group
Conclusions so far• We have identified 6 major transcription profile classes
in these data: 1. cell lines2. incompletely differentiated cells and connective tissues3. neoplasms 4. blood 5. brain 6. muscle
• Cell lines cluster together!
Gene expression across the 5372 samples
• The expression of most genes is relatively constant• There are only 1034 probesets (mapping to less than
900) genes where normalised signal variability has standard deviation > 2
Clustering of 97 sample groups and 1000 most variable probesets (about 900 genes)
1. Immune repsonse2. Nervous system development3. Lipid raft4. Mitosis5. Neurotransmitter uptake6. Cytoskeletal protein binding7. Extracellular matrix8. Extracellular regions9. Extracellular matirx10. Extracellular region11. Mitosis
12. Defence response13. Nervous system development14. Actin cytoskeleton organisation and biogenesis15. Protein carrier activity16. No significant resout17. Antigen presentation, exogenous antigen18. Trans – 1,2-dyhydrobenzene, 1,2-dyhydrogenase
activity19. S100 alpha binding
1 2 3 4 5 6 7 8 9 10 11 12 13 141516 17 18 19
Clustering based on subset of these genes produce similar results
• Clustering based on 350 most variable probesets gives almost the same result
• Even clustering based on 30 most variable probesets is very close
24 most variable genesCALD1 Actin- and myosin-binding protein implicated in the regulation of actomyosin interactions in smooth muscle and nonmuscle cells CDH1 calcium dependent cell-cell adhesion glycoprotein COL1A1 Type I collagen - fibrillar forming collagen (alpha 1 chain) COL1A2 Type I collagen - fibrillar forming collagen (alpha 2 chain) COL3A1 Collagen type III occurs in most soft connective tissues along with type I collagenCOL6A3 Collagen VI acts as a cell-binding proteinCXCR4 Receptor for the C-X-C chemokine CXCL12/SDF-1, participates in a signal transductionDCN May affect the rate of fibrils formationDKK3 Inhibitor of Wnt signaling pathway (Potential)FN1 Involved in cell adhesion, cell motility, opsonization, wound healing, and maintenance of cell shapeHBA1 Involved in oxygen transport from the lung to the various peripheral tissuesHLA-DRA One of the HLA class II alpha chain paralogues, plays a central role in the immune system HLA-DRA1HLA-DRB3 Plays a central role in the immune system by presenting peptides derived from extracellular proteinsJGA1 Cluster of closely packed pairs of transmembrane channels, the connexonsKRT15 Encodes a member of the keratin gene familyKRT18 Type I intermediate filament chain keratin LUM A member of the small leucine-rich proteoglycan (SLRP) family LYZ Encodes human lysozymePLS3 Actin-bundling protein found in intestinal microvilli, hair cell stereocilia, and fibroblast filopodiaS100AB S-100 is a group of low molecular weight (10–12 kD) calcium-binding proteins highly conserved among vertebratesSPARC Appears to regulate cell growth through interactions with the extracellular matrix and cytokinesSPARCL1 Seems to be little knownTACSTD2 Tumor-associated calcium signal transducer 2
Human gene expression map02/03/201639
Hierarchical clustering of all 369 sample groups
Some finer groups:
Cancer:•Sarcomas•Carcinomas•Neuroblastomas
Normal:•Liver and gut
Identifying condition specific genes by supervised analysis
• Using linear models to find condition specific genes, multiple testing correction, differential expression cut-offs
• Example - 174 leukemia specific genes include most well known markers (e.g, BCR, ETV6, FLT3,
HOXA9, MUST3, PRDM2, RUNX1, and TAL1) Many confirmed as associated with leukemia
• Beyond the major 6 classes the ‘signal’ becomes weak
• The problem may be lab effectsThe large biological effects are stronger than the lab
effectsHowever, when we zoom into particular subclasses, the
lab effects may be taking precedence
Mapping the human transcriptome
Traditional researchA microarray experiment
Everest Lhasa
Kathmandu
The map we want to build
Our current view on global transcriptome
97 groups – colours recycled
Frontal cortex
Muscular dystrophySkeletal muscle
Brain
Heart and heart parts
CerebellumCaudate nucleus
Hippocampaltissue
Nervous system tumors
Brain and system Mono-
nuclearcells
AML
Gene Expression Atlas
• Ele Holloway• Ibrahim Emam• Pavel Kurnosov • Helen Parkinson• Anrey Zorin• Tony Burdett • Gabriella Rustici• Eleanor William• Andrew Tikhonov
Global Differential Expression Analysis
• Selected ~10% of the data from ArrayExpress (including GEO imports), manually curated for quality and mapped to a custom-built ontology of experimental factors, EFO: http://www.ebi.ac.uk/efo
• Data on differential expression of genes in 1000+ studies, comprising ~30000 assays, in over 5000 conditions
• For each experiment, differentially expressed genes have been identified computationally via moderated t-tests and statistical meta-analysis
Meta-Analysis Approaches
• Vote counting: number of independent studies supporting an observation for a particular gene
• Effect size integration: compute effect size statistics in each study, assess relevant statistical model and compute combined z-score, for each gene/condition/study combination (extension of Choi et al, 2003)
Analysing each contributing dataset separately:
AML CML normal
genes
AML CML normalgene 1 0 1 0gene 2 1 1 0gene 3 0 0 0
gene n 0 0 1
one-way ANOVA
Combining the datasets
…
Experiments 1, 2, 3, …, m AML e1 AML e2 AML e3 CML e1 CML e2 CML e3 CML e4 normalgene 1 0 0 0 1 1 0 1 0gene 2 1 1 1 1 0 0 0 0gene 3 0 0 0 0 0 0 0 0
gene n 0 1 1 0 0 0 0 1
Effect size-based meta-analysis
• We have for each gene in each experiment/condition:p-value for significancesimulaneous t-statistics & confidence intervalsd.e. label (“up” or “down”)
• However, we would like to:Measure of strength of d.e. effect sizeAbility to combine d.e. findings statistically
• Effect SizeStandardized mean difference or similar (e.g., correlation coef.)
Meta-analysis Procedure
• For each gene-experiment-condition combinationCompute effect size from simultaneous d.e. t-statistics
• Combine effect sizes across multiple studiesUsing fixed-effects or random-
effects modelsObtain for each gene-condition
combination:• Mean effect size estimate• Combined z-score• Overall p-value
Annotating data with ontologies
• Diverse nature of annotations on data• Need to support complex queries which contain semantic
informationE.g. which genes are under-expressed in brain samples in
human or mouse• If we annotate with do we get this data?
cancer
adenocarcinoma
James Malone
We can use the ontology structure
We can perform effect size meta-analysis on a hierarchy,if we follow several rules:
Query for genes
Query for conditions
species
The ‘advanced query’ option allows building more complex queries
http://www.ebi.ac.uk/gxa
www.ebi.ac.uk/gxa
Query results for gene ASPM
ArrayExpress61
ASPM is downregulated in ‘normlal’ condition in comparison to a disease in 9 studies out of 10Upregulated in ‘Glioblastoma’ in 3 indepnendent studies
Zoom into one of the ‘Glioblastoma’ studies. Each bar represents an expression level in a particular sample
Integrating both approaches
• First approach gives the global view, but obsucres the detail
• The second approach gives detail, but doesn’t allow easily to integrate everything in one map
• Can we combine both approaches?
Other data
• RNAseq data• Proteomics data – Human Proteome Atlas from KTH in
Stockholm (collaboration with Mathias Uhlen)
• Time series – what states a cell goes through to become from an ESC to a mature cell?
Two ways of integrating the data
• On a quantitative level – normalise all data together Advantages – results easier to interpretDisadvantages – lab effects
• On a statistics level – analyse each dataset separately firstAdvantages – less lab effects Disadvantages – combined data difficult to interpret (in each
experiment each conditions is compared to something else)• How to combine the two approaches?
Acknowledgements• Margus Lukk• Misha Kapushesky• Angela Gonzales• Helen Parkinson• Gabriela Rustici• Ugis Sarkans• Ele Holloway • Roby Mani • Mohammadreza Shojatalab • Nikolay Kolesnikov • Niran Abeygunawardena • Anjan Sharma • Miroslaw Dylag• Ekaterina Pilicheva • Ibrahim Emam• Pavel Kurnosov• Andrew Tikhonov• Andrey Zorin
• CollaboratorsAudrey Kaufman (EBI)Wolfgang Huber (EBI)Sami Kaski (Helsinki)Morris Swertz (Groningen)…
• FundingEuropean Commision
• FELICS• MolPAGE• ENGAGE• MuGEN• SLING• DIAMONDS• EMERALD
NIH (NHGRI)EMBL
• Anna Farne• Eleanor Williams • Tony Burdett• James Malone• Holly Zheng• Tomasz Adamusiak• Susanna-Assunta Sansone• Philippe Rocca-Serra • Natalija Sklyar• Marco Brandizi• Chris Taylor• Eamonn Maguire• Maria Krestyaninova• Mikhail Gostev• Johan Rung• Natalja Kurbatova• Katherine Lawler• Nils Gehlenborg • Lynn French