Supervised and unsupervised methods for large scale genomic data integration Curtis Huttenhower Harvard School of Public Health Department of Biostatistics

Greatest Biological Discoveries? 2

Are We There Yet? 3 How much biology is out there? How much have we found? How fast are we finding it? Human Proteins with Annotated Biological Roles Age-Adjusted Citation Rates for Major Sequencing Projects Species Diversity of Environmental Samples Fierer 2008 # Distinct Roles Matt Hibbs

# Distinct Roles Matt Hibbs Are We There Yet? 4 How much biology is out there? How much have we found? How fast are we finding it? Human Proteins with Annotated Biological Roles Age-Adjusted Cost per Citation for Major Sequencing Projects Species Diversity of Environmental Samples Fierer 2008 Lots! Not nearly all Not fast enough Our job is to create computational microscopes: To ask and answer specific biomedical questions using millions of experimental results Our job is to create computational microscopes: To ask and answer specific biomedical questions using millions of experimental results

Outline 5 1. Big picture: Algorithms for mining genome-scale datasets 2. Details: Recovering mechanistic detail from high-throughput data 3. Applications: Microbial communities and functional metagenomics

A framework for functional genomics 6 High Similarity Low Similarity High Correlation Low Correlation G1 G2 + G4 G9 + … G3 G6 - G7 G8 - … G2 G5 ? …0.10.2…0.8 +-…--…+ 0.5… …0.6 High Correlation Low Correlation Frequency Coloc.Not coloc. Frequency SimilarDissim. Frequency P(G2-G5|Data) = Ms gene pairs → ← 1Ks datasets + =

Functional network prediction and analysis 7 Global interaction network Metabolism networkSignaling networkGut community network Currently includes data from 30,000 human experimental results, 15,000 expression conditions + 15,000 diverse others, analyzed for 200 biological functions and 150 diseases HEFalMp

HEFalMp: Predicting human gene function 8 HEFalMp

HEFalMp: Predicting human genetic interactions 9 HEFalMp

HEFalMp: Analyzing human genomic data 10 HEFalMp

HEFalMp: Understanding human disease 11 HEFalMp

Meta-analysis for unsupervised functional data integration 12 Evangelou 2007 Huttenhower 2006 Hibbs 2007 Simple regression: All datasets are equally accurate Random effects: Variation within and among datasets and interactions

Meta-analysis for unsupervised functional data integration 13 Following up with semi- supervised approach Evangelou 2007 Huttenhower 2006 Hibbs =

Functional mapping: mining integrated networks 14 Predicted relationships between genes High Confidence Low Confidence The strength of these relationships indicates how cohesive a process is. Chemotaxis

Functional mapping: mining integrated networks 15 Predicted relationships between genes High Confidence Low Confidence Chemotaxis

Functional mapping: mining integrated networks 16 Flagellar assembly The strength of these relationships indicates how associated two processes are. Predicted relationships between genes High Confidence Low Confidence Chemotaxis

Functional Mapping: Scoring Functional Associations 17 How can we formalize these relationships? Any sets of genes G 1 and G 2 in a network can be compared using four measures: Edges between their genes Edges within each set The background edges incident to each set The baseline of all edges in the network Stronger connections between the sets increase association. Stronger within self-connections or nonspecific background connections decrease association.

Functional Mapping: Bootstrap p-values Scoring functional associations is great… …how do you interpret an association score? –For gene sets of arbitrary sizes? –In arbitrary graphs? –Each with its own bizarre distribution of edges? 18 Empirically! # Genes Histograms of FAs for random sets For any graph, compute FA scores for many randomly chosen gene sets of different sizes. Null distribution is approximately normal with mean 1. Standard deviation is asymptotic in the sizes of both gene sets. Maps FA scores to p-values for any gene sets and underlying graph. Null distribution σ s for one graph

Functional Mapping: Functional Associations Between Processes 19 Edges Associations between processes Very Strong Moderately Strong Nodes Cohesiveness of processes Below Baseline (genomic background) Very Cohesive Borders Data coverage of processes Well Covered Sparsely Covered Hydrogen Transport Electron Transport Cellular Respiration Protein Processing Peptide Metabolism Cell Redox Homeostasis Aldehyde Metabolism Energy Reserve Metabolism Vacuolar Protein Catabolism Negative Regulation of Protein Metabolism Organelle Fusion Protein Depolymerization Organelle Inheritance

Functional Mapping: Functional Associations Between Processes 20 Edges Associations between processes Very Strong Moderately Strong Nodes Cohesiveness of processes Below Baseline (genomic background) Very Cohesive Borders Data coverage of processes Well Covered Sparsely Covered

Functional Maps: Focused Data Summarization 21 ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA Data integration summarizes an impossibly huge amount of experimental data into an impossibly huge number of predictions; what next?

Functional Maps: Focused Data Summarization 22 ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA How can a biologist take advantage of all this data to study his/her favorite gene/pathway/disease without losing information? Functional mapping Very large collections of genomic data Specific predicted molecular interactions Pathway, process, or disease associations Underlying experimental results and functional activities in data

Outline Big picture: Algorithms for mining genome-scale datasets 2. Details: Recovering mechanistic detail from high-throughput data 3. Applications: Microbial communities and functional metagenomics

Gene expression Physical PPIs Genetic interactions Colocalization Sequence Protein domains Regulatory binding sites … ? How do functional interactions become pathways? 24 + =

Functional genomic data 25 With Chris Park, Olga Troyanskaya Simultaneous inference of physical, genetic, regulatory, and functional networks Functional interactions Regulatory interactions Post-transcriptional regulation Metabolic interactions Phosphorylation Protein complexes

Learning a compendium of interaction networks 26 Train one SVM per interaction type Resolve consistency using hierarchical Bayes net

Learning a compendium of interaction networks 27 AUC Both presence/absence and directionality of interactions are accurately inferred

Using network compendia to predict complete pathways 28 Additional 20 novel synthetic lethality predictions tested, 14 confirmed (>100x better than random) Confirmed Unconfirmed With David Hess

Interactive aligned network viewer – coming soon! 29 Graphle

Outline Big picture: Algorithms for mining genome-scale datasets 2. Details: Recovering mechanistic detail from high-throughput data 3. Applications: Microbial communities and functional metagenomics

Microbial Communities and Functional Metagenomics Metagenomics: data analysis from environmental samples –Microflora: environment includes us! Pathogen collections of “single” organisms form similar communities Another data integration problem –Must include datasets from multiple organisms What questions can we answer? –What pathways/processes are present/over/under- enriched in a newly sequences microbe/community? –What’s shared within community X? What’s different? What’s unique? –How do human microflora interact with diabetes, obesity, oral health, antibiotics, aging, … –Current functional methods annotate ~50% of synthetic data, <5% of environmental data 31 With Jacques Izard, Wendy Garrett

Data Integration for Microbial Communities 32 ~300 available expression datasets ~30 species Weskamp et al 2004 Flannick et al 2006 Kanehisa et al 2008 Tatusov et al 1997 Data integration works just as well in microbes as it does in yeast and humans We know an awful lot about some microorganisms and almost nothing about others Sequence-based and network-based tools for function transfer both work in isolation We can use data integration to leverage both and mine out additional biology

Functional network prediction from diverse microbial data bacterial expression experiments 876 raw datasets 310 postprocessed datasets 304 normalized coexpression networks in 27 species Integrated functional interaction networks in 15 species 307 bacterial interaction experiments raw interactions postprocessed interactions E. Coli Integration ← Precision ↑, Recall ↓

Functional maps for cross-species knowledge transfer 34 ← Precision ↑, Recall ↓ Following up with unsupervised and partially anchored network alignment

Sleipnir C++ library for computational functional genomics Data types for biological entities Microarray data, interaction data, genes and gene sets, functional catalogs, etc. etc. Network communication, parallelization Efficient machine learning algorithms Generative (Bayesian) and discriminative (SVM) And it’s fully documented! Efficient Computation For Biological Discovery Massive datasets and genomes require efficient algorithms and implementations. 35 It’s also speedy: microbial data integration computation takes <3hrs.

Outline Big picture: Algorithms for mining genome-scale datasets 2. Details: Recovering mechanistic detail from high-throughput data 3. Applications: Microbial communities and functional metagenomics Bayesian and unsupervised methods for data integration HEFalMp system for human data analysis and integration Functional mapping to statistically summarize large data collections Simultaneous inference of an interaction network compendium Accurate prediction of interaction types and directionality Validated pathways and specific individual interactions in yeast Integration for microbial communities and metagenomics Sleipnir software for efficient large scale data mining

Thanks! Olga Troyanskaya Chris Park David Hess Matt Hibbs Chad Myers Ana Pop Aaron Wong Hilary Coller Erin Haley Jacques Izard Wendy Garrett Sarah Fortune Tracy Rosebrock

Validating Human Predictions 39 Autophagy Luciferase (Negative control) ATG5 (Positive control) LAMP2RAB11A Not Starved (Autophagic) Predicted novel autophagy proteins 5½ of 7 predictions currently confirmed With Erin Haley, Hilary Coller

Functional maps for cross-species knowledge transfer 40 G17 G16 G15 G10 G6 G9 G8 G5 G11 G7 G12 G13 G14 G2 G1 G4 G3 O8 O4 O5 O7 O9 O6 O2 O3 O1 O1: G1, G2, G3 O2: G4 O3: G6 … ECG1, ECG2 BSG1 ECG3, BSG2 …

Functional maps for functional metagenomics 41 GOS Hypersaline Lagoon, Ecuador KEGG Pathways Organisms Pathogens Env. Mapping genes into pathways Mapping pathways into organisms + Integrated functional interaction networks in 27 species Mapping organisms into phyla =

Functional maps for functional metagenomics 42 Nodes Process cohesiveness in obesity Very Downregulated Baseline (no change) Very Upregulated Edges Process association in obesity More Coregulated Less Coregulated Baseline (no change)

Current Work: Molecular Mechanisms in a Colorectal Cancer Cohort 43 With Shuji Ogino, Charlie Fuchs ~3,100 gastrointestinal subjects ~3,800 tissue samples ~1,450 colon cancer samples ~1,150 CpG island methylation ~1,200 LINE-1 methylation ~700 TMA immuno- histochemistry ~2,100 cancer mutation tests Health Professionals Follow-Up Study Nurse’s Health Study LINE-1 Methylation Repetitive element making up ~20% of mammalian genomes Very easy to assay methylation level (%) Good proxy for whole-genome methylation level DASL Gene Expression Gene expression analysis from paraffin blocks Thanks to Todd Golub, Yujin Hoshida ~775 gene expression

Molecular Subtypes of Colorectal Cancer: Stem Cell Programs and Proliferation 44 Chr. 19 rearrangement, membrane receptors/channels HSC signature Neural/ESC signature Angiogenesis, proliferation BRCA interactors, chrom. stability factors Cell cycle regulation C1 C2C3C4 Nonnegative matrix factorization Tumors → ← Genes

Molecular Subtypes of Colorectal Cancer: Stem Cell Programs and Proliferation 45 Subramanian et al, Neural Stem Cell Signature Hematopoeitic Stem Cell Signature Embryonic Stem Cell Signature Chr. 19q BAX CD133 + Bcl-X(L) CD44 + CD166 Hypotheses? Two main pathways to proliferation: HSC program + BAX ESC/NSC program Two main pathways to deregulation: Angiogenesis + chrom. instability Cell cycle disruption (MSI?) Note that these regulatory programs do not appear to correspond with demographics or common pathologic markers… Testing now for correlation with outcome.

Epigenetics of Colorectal Cancer: LINE-1 methylation levels 46 ρ = 0.718, p < 0.01 Ogino et al, 2008 Lower LINE-1 methylation associates with poor colon cancer prognosis. LINE-1 methylation varies remarkably between individuals… …but it is highly correlated within individuals. What does it all mean?? What is the biological mechanism linking LINE-1 methylation to colon cancer?

Epigenetics of Colorectal Cancer: LINE-1 methylation levels 47 ρ = 0.718, p < 0.01 Ogino et al, 2008 Lower LINE-1 methylation associates with poor colon cancer prognosis. LINE-1 methylation varies remarkably between individuals… …but it is highly correlated within individuals. This suggests a genetic effect. This suggests a copy number variation. This suggests linkage to a cancer-related pathway. Is anything different about these outliers? What is the biological mechanism linking LINE-1 methylation to colon cancer?

Epigenetics of Colorectal Cancer: LINE-1 methylation levels 48 What is the biological mechanism linking LINE-1 methylation to colon cancer? Preliminary Data 10 genes differentially expressed even using simple methods 1/3 are from the same family with known GI tumor prognostic value 1/3 are X-chromosome testis/cancer-specific antigens 1/2 fall in same cytogenic band, which is also a known CNV hotspot HEFalMp links to a cascade of antigens/membrane receptors/TFs Cell adhesion p-value ≈ 0, moderate correlation in many cancer arrays GSEA pulls out a wide range of proliferation up (E2F), immune response down; need to regress out prognosis correlates Check back in a couple of months!