Interpreting Diverse Genomic Data Using Gene Sets
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1 Interpreting Diverse Genomic Data Using Gene Sets giovanni FHCRC February 2012
2 Gene Sets Leary 2008 PNAS Alterations in the combined FGF, EGFR, ERBB2 and PIK3 pathways. Red: Copy number alterations; Blue: Point mutations.
3 Why gene-set analysis? Improvements in interpretability of experimental results. Detection of subtle correlated changes in sets. Detection of set-level biological signals. Integration of diverse data sources.
4 The birthplaces of gene set analysis: I Tavazoie etal Nature Genetics 2000 Hypergeometric p-value.
5 The birthplaces of gene set analysis: II Mirnics et al Neuron 2000 Molecular Characterization of Schizophrenia Viewed by Microarray Analysis of Gene Expression in Prefrontal Cortex.
6 A Formalism for Two-Stage Gene Set Analysis Binary response vector Y (phenotype, class label, case-control...) one for each of N samples G N matrix X of genetic information on samples G S binary membership matrix M Stage I Testing of differences between groups for each gene. Compute for each gene g a score s g (X,Y ), capturing the relationship between the genomic measurements and a phenotype of interest. Stage II Testing of differences in scores between sets. Take the scores computed in Stage I as data, and look for association between the scores and the columns of M.
7 An Early Gene Set Analysis Chowers etal Human Molecular Genetics, 2003 Distribution of standard deviations for expression ratios of all genes of known function on the array (solid line), photoreceptor genes (dashed line), and genes involved in cell proliferation (dotted line).
8 Gene Set Enrichment Analysis Mootha et al Nature Genetics, 2003; Subramanian PNAS 2005
9 Caveats I: Biology SET QUALITY SET OVERLAP TISSUE SPECIFICITY PATHWAY TOPOLOGY
10 Caveats II: Statistics GENES ARE NOT INDEPENDENT WHAT IS THE NULL HYPOTHESIS? BIG SET BIAS
11 Outline, References and Acknowledgments MANY TECHNOLOGIES S. Tyekucheva, L. Marchionni, R. Karchin and G. Parmigiani Integrating diverse genomic data using gene sets. Genome Biol., 12: R105, ATOMS S.M. Boca, H. Corrada Bravo, B. Caffo, J.T. Leek and G. Parmigiani. A decision-theory approach to interpretable set analysis for high-dimensional data. JHU Biostat Working Paper 211, PATIENTS S.M. Boca, K.W. Kinzler, V.E. Velculescu, B. Vogelstein and G. Parmigiani. Patient oriented gene-set analysis for cancer mutation data. Genome Biol., 11: R112, 2010.
12 Multiple data types Phenotype (Pa,ents) Set C Gene s Gene s Gene s Gene s Set B Set Z Genes Set A
13 Gene-centric approaches for multiple data types Binary response vector Y (phenotype, class label, case-control...) G N matrix X of genetic information on samples G S binary membership matrix M Stage I Stage II s g (X 1,...,X D,Y ) t s (s,m s ) Integration sg(x 1 d,y )... sg D (X d,y ) t s (s 1...s D,M s ) Meta-analysis sg(x 1 d,y )... sg D (X d,y ) t s (s 1,M s )... t s (s D,M s ) Visualization
14 Clustering Sets to Compare Experiments
15 Integrative more powerful than Meta-analytic A B Expression 1 Expression 2 CNV 1 CNV 2 LR Avg. p value Min. p value True positive rate Expression 1 Expression 2 CNV 1 CNV 2 LR Avg. p value Min. p value Altered fraction False positive rate Independent Sets ROC for classification of spiked-in sets
16 Integrative more robust than Meta-analytic A B Expression 1 Expression 2 CNV 1 CNV 2 LR Avg. p value Min. p value True positive rate Expression 1 Expression 2 CNV 1 CNV 2 LR Avg. p value Min. p value Altered fraction False positive rate Chromosomal Segments ROC for classification of spiked-in sets
17 Integrative discovers novel sets (a) Synthetic sets Canonical pathways Fraction of exclusively discovered sets Expression 1 Expression 2 CN 1 CN 2 Fraction of exclusively discovered sets Expression 1 Expression 2 CN 1 CN Number of top sets Number of top sets (b) Synthetic sets Canonical pathways Fraction of exclusively discovered sets INT Avg. p value Min. p value Fraction of exclusively discovered sets INT Avg. p value Min. p value Number of top sets Number of top sets
18 GBM Color key Value Color key Value Color key 2 2 Value E2 C2 C2 C2 C1 WNT pathway C1 E2 Stress pathway C1 E2 Glycolysis pathway E1 E1 E1 PGK1 ENO1 TPI1 GPI ALDOB PFKL PKLR HK1 MAP2K4 TRADD MAP2K3 TANK MAP2K6 JUN IKBKG TNFRSF1A MAP3K14 CRADD CASP2 NFKB1 TRAF2 RIPK1 ATF1 CHUK LTA MAP4K2 NFKBIA MAPK14 IKBKB TNF MAPK8 APC WIF1 GSK3B FZD1 CSNK1A1 NLK CTNNB1 HDAC1 MAP3K7 CCND1 CSNK2A1 TLE1 CTBP1 CSNK1D PPARD FRAT1 BTRC AXIN1 MAP3K7IP1 MYC CREBBP
19 Enrichment of 3 intersecting pathways for ER+ BC Wnt pathway 28.7 % (261) 29.4% (225) 14.0% (23) 42.0% (175) Cell cycle 38.0% (235) 27.1% (7) 61.2% (6) 29.0% (31) Ubiquitin mediated proteolysis 26.1% (231) 24.5% (187) X% (Y): X% out of Y genes are estimated to have densities from the alternative distribution.
20 Decision Theoretic Angle Divide genes into atoms based on sets. Truth is the list of alternatives. We search for estimators among the unions of atoms. The estimators are based on the loss function: (1 w) # of FD +w # of MD. The posterior expected loss is: (1 w) EFD + w EMD.
21 Atomic False Discovery Rate We define the atomic false discovery rate for atom A as: AFDR(A) = FD(A)/n A. Theorem (Boca et al., 2010) Atom A is included in the Bayes estimator if and only if the atomic FDR is thresholded by w: ÂFDR(A) w. 1 ÂFDR estimates the fraction of alternatives in an atom.
22 Atomic FDR measures enrichment 1 EFDR p value enrichment fraction enrichment fraction
23 Altered Pathways in Glioblastoma Parsons 2008
24 Gene-centric vs Patient Centric Scores log10(q values) obtained by counting altered samples log10(q values) obtained by combining gene scores
25 Outline, References and Acknowledgments MANY TECHNOLOGIES S. Tyekucheva, L. Marchionni, R. Karchin and G. Parmigiani Integrating diverse genomic data using gene sets. Genome Biol., 12: R105, ATOMS S.M. Boca, H. Corrada Bravo, B. Caffo, J.T. Leek and G. Parmigiani. A decision-theory approach to interpretable set analysis for high-dimensional data. JHU Biostat Working Paper 211, PATIENTS S.M. Boca, K.W. Kinzler, V.E. Velculescu, B. Vogelstein and G. Parmigiani. Patient oriented gene-set analysis for cancer mutation data. Genome Biol., 11: R112, 2010.
Interpreting Diverse Genomic Data Using Gene Sets
Interpreting Diverse Genomic Data Using Gene Sets giovanni parmigiani@dfci.harvard.edu FHCRC February 2012 Gene Sets Leary 2008 PNAS Alterations in the combined FGF, EGFR, ERBB2 and PIK3 pathways. Red:
Introduction to Gene Sets Analysis
Introduction to Svitlana Tyekucheva Dana-Farber Cancer Institute May 15, 2012 Introduction Various measurements: gene expression, copy number variation, methylation status, mutation profile, etc. Main
Package CancerMutationAnalysis
Type Package Package CancerMutationAnalysis Title Cancer mutation analysis Version 1.2.1 Author Giovanni Parmigiani, Simina M. Boca March 25, 2013 Maintainer Simina M. Boca Imports
* Kyoto Encyclopedia of Genes and Genomes.
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CancerMutationAnalysis package
CancerMutationAnalysis package Giovanni Parmigiani Dana-Farber Cancer Institute and Harvard School of Public Health email: gp@jimmy.harvard.edu, Simina M. Boca Georgetown University Medical Center email:
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