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Multiple Testing Under Dependency, with Applications to Genomic Data Analysis

Multiple Testing Under Dependency, with Applications to Genomic Data Analysis. Zhi Wei Department of Computer Science New Jersey Institute of Technology Joint work with Wenguang Sun. X. X. X. Expression. X. X. X. Phenotype 2. Phenotype 1. Intro Microarray.

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Multiple Testing Under Dependency, with Applications to Genomic Data Analysis

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  1. Multiple Testing Under Dependency, with Applications to Genomic Data Analysis Zhi Wei Department of Computer Science New Jersey Institute of Technology Joint work with Wenguang Sun

  2. X X X Expression X X X Phenotype 2 Phenotype 1 Intro Microarray • Measure the activity of thousands of genes simultaneously • DE v.s. EE genes • Notation: • Xi∈ {DE, EE} • Yi = (yi1, …, yim; yi(m+1), …, yi(m+n)), m, n replicates for 2 cond, resp. Differential Expression (DE) Analysis Data for a single gene

  3. Microarray Time Course Data: A Motivating Example • Microarray Time Course (MTC) data commonly collected for studying biological processes • Tian, Nowak, and Brasier (2005) “A TNF-Induced Gene Expression Program Under Oscillatory NF-Kappab Control,” • study the biological process of how the cytokine tumor necrosis factor (TNF) initiates tissue inflammation • Dataset: time-course microarray experiment to profile gene activities at 0hr, 1hr, 3hr, 6hr after inhibition of the NF-kB transcription factor • To find: • (1) “Early response genes” that were differentially expressed (DE) less than 1hr after the NF-kB inhibition • (2) “Middle response genes” that were DE at 3hr but no response prior to that • (3) “Late response genes” that were DE at 6hr but no response prior to that • (4) “Biphasic genes” that were DE at both 1hr and 6hr hours, but not at 3hr.

  4. [Yi1,Yi2,…,YiT] Yi1 Yi2 … YiT Xi Xi1 Xi2 … XiT Existing solutions • Solution 1: Find TDE genes • Moderated likelihood ratio statistic and Hotelling T2 (Tai and Speed 06), • functional data analysis : Luan and Li (04), Storey et al. (05), Hong and Li (06) • Solution 2: Find DE gene at each time point • T-test at each time point • HMM, Yuan and Kendziorski (06). ? Too general Too detailed

  5. Set-wise multiple testing • Conceptualize the gene selection problem as a set-wise multiple testing problem • Test DE vs EE at each time point for each gene • Combine the tests across all time points as a set for DE patterns of interest, namely, one gene corresponding to a set of hypotheses • Several issues in set-wise testing • (i) multiplicity: how to control testing errors (such as the false discovery rate) at the set level when thousands of gene are considered simultaneously; • (ii) optimality: how to optimally combine the testing results within a set so that sensitivity is maximized while multiplicity is controlled. • (iii) dependency: how to account for and exploit the dependency of the tests within a set, e.g. the high temporal correlation in MTC data.

  6. Related work • Multiplicity issue for simultaneous set-wise tests • Formally addressed by (Benjamini & Heller, Biometrics, 2008) • Test DE vs EE at each time point individually • Combine the p-values at each time point into a pooled p-value by Simes • BH procedure is applied to the Simes-combined P values • Partial conjunction tests • Are all T hypotheses in the set true? (Conjunction null) • Are at least u out of T hypotheses in the set false? (Partial Conjunction alternative) • Limitations • Incapable of capturing sophisticated DE patterns, e.g., early response genes • Not optimal because dependency is ignored, although FDR at set level (FSR) is controlled. • Testing hypotheses under dependency (Sun & Cai, JRSSB, 2009) • One HMM to account for dependency of hypotheses • Local Index of Significance (LIS) statistics • Valid and optimal

  7. Characterizing Gene Temporal Expr. Patterns in a Set-wise Multiple Testing Framework • Setup for MTC data: • Each gene has two possible states at each time point: equally expressed (EE) and differentially expressed (DE), denoted by 0 and 1, respectively. • Consider P sets of hypotheses: {(Hi1, . . . ,HiT) : i = 1, . . . ,P}for testing EE versus DE, where P is #genes and T, #time points. • Pattern representation • 2T atomic patterns η∈ {0,1} T , e.g. 00101, 11100, etc. for T=5 • Partition 2T ηsinto null Θ0 and non-null Θ1, the patterns of interest • 2^(2T) -1 partitions  powerful temporal pattern characterization • Conjunction: Θ0 ={(η1, . . . , ηT) ∈ {0, 1}T|∑ ηt =0} • Partial Conjunction: Θ0 ={(η1, . . . , ηT) ∈ {0, 1}T|∑ ηt < u} • “Late response genes”, DE after time t, Θ1 ={(η1, . . . , ηT) ∈ {0, 1}T| η1=…=ηt=0&ηt+1+…+ηT>0}

  8. Yi1 Yi2 … YiT Xi1 Xi2 … XiT HMM • The state sequence of gene i over time is a binary vector Xi = (xi1, . . . , xiT), where xit = 1 indicates that gene i at time t is DE and xit = 0 otherwise. • Assume Xi ∈{0,1}T is distributed as a Markov chain • Transition probability can be either homogeneous or inhomogeneous • Assume Xi ┴ Xj for i <>j • Emission probability f(Yit|Xit) ~ Gamma-Gamma Model (Newton et al. 2001, Kendziorski et al 2003)

  9. Yi1 Yi2 … YiT Xi1 Xi2 … XiT HMM-based set-wise testing • Use EM algorithm for estimating all HMM parameters: initial prob., transition prob., emission prob. (Gamma-Gamma model) • Define a binary vector ϑ = (ϑ1, . . . , ϑp) ∈ {0, 1}p, where ϑi =1 if Xi ∈ Patterns of Interest andϑi =0 otherwise. • Define the generalized local index of significance (GLIS):

  10. HMM-based set-wise testing (cont’d) • GLIS testing procedure • Denote by GLIS(1), ..., GLIS(p) the ordered GLIS values and H(1), ..., H(p) the corresponding hypotheses • Let • Then reject all H(i), for i=1, 2, ..., l • Valid and asymptoticallyoptimal for set-wise multiple testing when the HMM parameter estimate is consistent (Sun and Wei, JASA, 2011) • The GLIS statistic can be interpreted as the probability of a gene being null (i.e., not exhibiting a pattern of interest) given the observed expression data

  11. Simulation • Setup • #subjects under both conditions are n1 = n2 = 10 • #genes P = 2000 • #time points =6 • #replications =200. • Nominal FSR level is 0.1. • HMM:π = (0.95, 0.05) , At = (a00, 1−a00; 1−a11, a11), Gt = (αt,α0t, νt). • Dependency: fix Gt = (10, 1, 0.5) and a00 =0.95 , change a11 from 0.2 to .98 • Gene expr. Signal: fix At = (0.95, 0.05; 0.2, 0.8) and Gt = (αt,1, 0.5), changeαt • Conjunction and Partial Conjunction • Comparison • BH-Simes • Viterbi-based decision rule: the most probable state sequence η∈ Θ1then select it. • Evaluation: FSR (False Set Rate), MSR (Missed Set Rate) = 1 – sensitivity

  12. Conjunction Test Results

  13. Partial Conjunction Test Results

  14. Ranking efficiency • Ak = (0.95, 0.05; 0.2, 0.8) and Gk = (αk,32, 4) Solid: Oracle GLIS Dashed: data-driven GLIS Dotted: BH-Simes

  15. Application Calvano et al (Nature 2005):A network-based analysis of systemic inflammation in humans. Dataset: 4 healthy persons’ gene expression in whole blood leukocytes immediately before and at 2,4,6,9 and 24 h after the intravenous administration of bacterial endotoxin; 4 healthy persons w/o treatment as controls. Goal: Identify genes in response to endotoxin

  16. Pattern Design • Genes perturbed in response to treatments • Sequentially perturbed genes • Early and late response genes OR

  17. HMM Fitting

  18. Early Response Genes (DE from 2nd time point)

  19. Late Response Genes (DE after 2nd time point)

  20. Published Genome-Wide Associations through 3/2009, 398 published GWA at p < 5 x 10-8 NHGRI GWA Catalog www.genome.gov/GWAStudies

  21. HMM-dependent (set-wise) hypothesis test in Genome-wide Association Studies (GWAS) • Individual Tests (Wei, Sun et al Bioinformatics, 2009) • N chromosomes  N HMMs, emission prob: Normal mixtures • How to pool different HMMs together for having a global multiplicity control • Improvement: More reproducible , Higher sensitivity • Set-wise Tests (Wang, Wei, and Sun, Statistics and Its Interface, 2010) • Testing a genomic region

  22. From HMM to Markov Random Field • An undirected graph, where each node represents a random variable Xi and each edge represents a dependency • Joint Distribution Function • Markov Blanket and Conditional independence • Inference • Exact inference is NP-complete • Approximation techniques: MCMC, loopy belief propagation, ICM

  23. Microarray DE analysis • Discrete Markov Random Field Model for Latent Differential Expression States (Wei and Li, Bioinformatics, 2007) Phenotype 2 Phenotype 1 Gene expression Yi=(Yi1,…,Yim; Yi(m+1),…,Yi(m+n)) Networks built from KEGG: 1668 nodes (genes), 8011 edges p genes on a network Framework: f(X|Y) ~ f(Y|X)*Pr(X) Pr(X) ~ Markov random field (MRF) Model Emission Prob. f(Y|X) ~ Gamma-Gamma Model

  24. MRF in Genome-wide association studies (GWAS) • GWAS data with network structure constructed based on LD dependency. (Li, Wei, and Maris, Biostatistics, 2010) Framework: f(X|Y) ~ f(Y|X)*Pr(X) X, disease assc. state: Pr(X) ~ Markov random field (MRF) Model Y, genotype counts, Emission Prob. f(Y|X) ~ Dirichlet-Trinomial Model

  25. Gene g+1 g g -1 Time t -1 t t +1 MTC Data • Hidden Spatial-Temporal MRF Model (Wei and Li, Annals of Applied Statistics 2008) • Model both regulatory (gene network) and temporal dependency (time). Spatial Neighbors Temporal Neighbors Ygt Gene Expression Xgt = 0 or 1, Gene State Temporal Dependency Regulatory Dependency f(Ygt|Xgt) ~ Emission Prob.

  26. Challenges in MRF • MRF-structured hypothesis test is valid and optimal? • MRF-structured set-wise hypothesis test is valid optimal? • Unlike HMM, the model fitting and parameter estimating for MRF is much more challenging • The EM algorithm for MRF is NP-hard • (Generalized) belief propagation algorithm

  27. References • Wei Z and Li H (2007): A Markov random field model for network-based analysis of genomic data. Bioinformatics, 23: 1537-1544. • Wei Z and Li H (2008): A spatial-temporal MRF model for network-based analysis of microarray time course gene expression data. Annals of Applied Statistics, 2: 408-429 . • Sun, W., and Cai, T. (2009). “Large-scale multiple testing under dependence”. Journal of the Royal Statistical Society, Series B, 71, 393-424. • Wei Z, Sun W, Wang K and Hakonarson H (2009), Multiple Testing in Genome-Wide Association Studies via Hidden Markov Models, Bioinformatics, 25:2802-2808 • Li H, Wei Z and Maris J (2010), A Hidden Markov Random Field Model for Genome-wide Association Studies, Biostatistics, 11:139-150. • Wang W, Wei Z, and Sun W (2010), Simultaneous Set-Wise Testing Under Dependence, with Applications to Genome-Wide Association Studies, Statistics and Its Interface, 3(4):501-512 • Sun W and Wei Z (2011), Multiple Testing for Pattern Identification, with Applications to Microarray Time Course Experiments, Journal of the American Statistical Association, 106(493):73–88

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