Data Mining For Genomics
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1 Data Mining For Genomics Alfred O. Hero III The University of Michigan, Ann Arbor, MI ISTeC Seminar, CSU Feb. 22, Biotechnology Overview 2. Gene Microarray Technology 3. Mining the genomic database 4. The post- genomic era
2 I. Biotechnology Overview! Genome: All the DNA contained in an organism. The operating system/program for gene structure/function of an organism.! Genomics: investigation of structure and function of very large numbers of genes undertaken in a simultaneous fashion.! Bioinformatics: Computational extraction of information from biological data.! Data Mining: Algorithms for extracting information from huge datasets using user- specified criteria.
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4 Central Dogma: From Gene to Protein Source: NHGRI
5 Towards a unified theory. Map Databases Gene Expression? PDB Regulatory Pathways? Clinical Data? Biodiversity? DNA RNA Proteins Circuits Phenotypes Populations GenBank EMBL DDBJ Development? SwissPROT PIR Metabolism? Neuroanatomy? Molecular Epidemiology? Comparative Genomics? Source:
6 Hierarchy of biological questions! Gene sequencing: what is the sequence of base pairs in a DNA segment, gene, or genome?! Gene Mapping: what are positions (loci) of genes on a chromosome?! Gene expression profiling: what is pattern gene activation/inactivation over time, tissue, therapy, etc?! Genetic circuits: how do genes regulate (stimulate/inhibit) each other s expression levels over time?! Genetic pathways: what sequence of gene interactions lead to a specific metabolic/structural (dys)function?
7 Sequencing Milestones Organism #of genes % genes with sequencing inferred function complete E. Coli 4, Yeast 6, C. Elegans 19, Drosophila 12,-14, Arabidopsis 25, 4 2 Mouse 26,-4, Human 26,383-39, Source:
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9 II. Gene Microarray Technologies! High throughput method to probe DNA in a sample! Two principal microarray technologies: 1) Affymetrix GeneChip 2) cdna spotted arrays! Main idea behind cdna technology: 1) Specific complementary DNA sequences arrayed on slide 2) Dye-labeled RNA from sample is distributed over slide 3) RNA binds to probes (hybridization) 4) Presence of bound RNA-DNA pairs is read out by detecting spot fluorescence via laser excitation (scanning)! Result:,- 5, genes can be probed at once
10 Specialized cdna Array: Eye-Gene wt RNA ko RNA I-gene slides Gene Expression Source: J. Yu, UM BioMedEng Thesis Proposal (22)
11 I-Gene Array: Probe Generation E15.5, Pn2, MAR Farjo, R & Yu, J. Vision Research 42 (22)
12 I-Gene Array: Printing and Processing Farjo, R & Yu, J. Vision Research 42 (22)
13 Treated sample labeled red (Cy5) Control data labeled green (Cy3)
14 Single-Chip Raw Data Analysis Source: J. Yu, UM BioMedEng Thesis Proposal (22)
15 Problem: Experimental Variability! Population too wide genetic diversity! Cell lines - poor sample preparation! Slide Manufacture slide surface quality, dust deposition! Hybridization sample concentration, wash conditions! Cross hybridization similar but different genes bind to same probe! Image Formation scanner saturation, lens aberrations, gain settings! Imaging and Extraction misaligned spot grid, segmentation Microarray data is intrinsically Statistical!
16 III. Mining Statistical Genomic Data. Questions:! How to estimate true Cy5 and Cy3 from raw data?! How to compensate for experimental variability?! How to extract expression profile ratios from a set of up to 5, probe responses?! How to specify gene profile selection criteria for mining in this data?! How to discover complex genetic pathways to disease, aging, etc?
17 Mining Statistical Genomic Data. Answers:! Spot Extraction: Estimate Cy3 and Cy5 concentrations " Image processing, image segmentation, anova models! Comparing between microarray experiments " Statistical invariance, equalizing transformations, normalization! Gene filtering and screening " Simultaneous statistical inference, T- tests, FDR! Discovery of genetic pathways " Clustering, dependency graphs, HMM s
18 Spot Extraction Issues! Technical noise and variability! Laser gain and calibration! Cy3/cy5 channel bleedthrough! Image formation gain! Spot- gridding algorithm! Spot segmentation algorithm
19 Technical Noise and Variability Weak Signal Streaks Good Signal Source: Irregular Spots Comet Tails
20 Gain Effects Weak Normal Saturated Optimal gain can be studied by information theory
21 Rate Distortion Lower Bound MSE Gain Hero Springer-3
22 Standard Spot Segmentation Method! Addressing Locate center of description for each spot! Spot Segmentation Classification of pixels either as signal or background.! Spot Quantification Estimation of hybridization level/ratio of spot Grid misalignment Laser Misalignment Source: C. Ball, Stanford Microarray Database
23 Segmentation via Morphological Operators Original Image Alternate-Sequential Filtered Watershed Transformed Final Segmented Image Siddiqui, Hero and Siddiqui, Asilomar-2
24 Spot EigenAnalysis! Gray level covariance matrix over each spot boundary is calculated! Eigen analysis of each covariance matrix is performed! Trends in direction of eigenvectors indicate systematic bias in spot printing Siddiqui, Hero and Siddiqui, Asilomar-2
25 Add Dimension: Expression Profiles Cy5/Cy3 hybridization profiles
26 Problem: Intrinsic Profile Variability Across- gene variability Within- gene variability
27 Solution: Experimental Replication Exp 1 Exp 2 Exp M. M replicates Issues: Control by experimental replication is expensive Surplus real estate allows replication in layout Batch and spatial correlations may be a problem
28 Comparing Across Microarray Experiments Experiment A Experiment B Question: How to combine or compare experiments A and B?
29 Un-Normalized Data Sets Within- experiment intensity variations mask A- B differences: Experiment A (Wildtype) Experiment B (Knockout) Hero&Fleury, ISSP-3
30 Two Approaches! If quantitative gene profile comparisons are required: " must find normalization function to align all data sets within an experiment to a common reference.! If only ranking of gene profile differences is required: " No need to normalize: can apply rank order transformation to measured hybridization intensities
31 A vs B Microarray Normalization Method Exp A Inverse Housekeeping Gene Selector Unif Tran Mean Normalized A Normalized B Unif Tran Mean Exp B Inverse
32 Un-Normalized Data Set (Wildtype) Hero&Fleury, ISSP-3
33 Normalized Data Set (Wildtype) Hero&Fleury, ISSP-3
34 Rank Order Statistical Transformation! Rank order algorithm: at each time point replace each gene intensity with its relative rank among all genes " The relative ranking is preserved by (invariant to) arbitrary monotonic intensity transformations. x * 2 * 4 *3 * 1 x 1 y * 2 * 4 * 3 * 1 y 1
35 Mining Gene Expression Data! Issues " Feature space " Feature selection criteria " Statistical robustification " Cross-validation " Experimental Validation
36 Y/O Human Retina Study (Yosida&etal:22) 16 individuals in 2 groups of 8 subjects Selection criteria:
37 Fred Wright s Human Fibroblast Data Lemon&etal:21 18 individuals in 3 groups of 6 subjects Selection criteria:
38 Mouse Retinal Aging Data Yosida&etal:23 24 mice in 6 groups of 4 subjects Selection criteria:
39 NRL Knockout vs Wildtype Retina Study 12 knockout/wildtype mice in 3 groups of 4 subjects Swaroop&etal:22 Knockout Wildtype Selection criteria:
40 Data Mining with a Single Criterion! Paired t-test with False Discovery Rate:! For Y/O Human study:
41 Multicriterion scattergram: Paired t-test 8226 Y/O retina genes plotted in multicriteria plane Fleury&etal ICASSP-2
42 Multicriterion Selection Criteria! Seek to find Pareto-optimal genes which strike a compromise between two criteria A,B,D are Pareto optimal Pareto Fronts
43 Multicriterion scattergram: Pareto Fronts Pareto fronts first second third Buried gene Fleury&etal ICASSP-2
44 Cross-Validation Approach: Resampling # replicates=m=4 # time points=t=6 # profiles=4^6=496
45 Bayesian approach: Posterior Analysis
46 Pareto Front Likelihood table Hero&Fleury:VLSI3
47 Robustification and Validation Issues! Cross-validation recomputes Pareto fronts over resampled virtual profiles (Fleury&etal:22).! Bayesian Pareto front also robustifies to prior uncertainty in data (Hero&Fleury:22).! Computational issues: " Cross-validated fronts: completely data-driven but computation is exponential in # replicates (m) and # time points (t). " Bayesian Pareto fronts: requires joint density of criteria and marginalization. Computation is linear.
48 The Post-Genomic Era! Whole genomes of species will be mapped! Genetic pathways to structure, metabolism, disease, will remain as open questions! Pathway analysis: what are the important gene interactions? " Requires performing many more experiments than zero-interaction analysis " Computational load is exponentially increasing in number of genes in pathway " New algorithms and models are needed
49 Draft Pathways for Photoreceptor Function Wnt/Ca calmodulin pathway Bmp pathway Retinoid acid pathway Source: J. Yu, UM BioMedEng Thesis Proposal (22)
50 Each Link: Gene Co-regulation Study Rxra Rxrb Rxrg Rara Bmp2 Bmp4 Brk-1 Tob1 Smad1 Smad Bmp2 Bmp4 Brk-1 Tob1 Smad1 Smad Bmp2 Bmp4 Brk-1 Tob1 Smad1 Smad Bmp2 Bmp4 Brk-1 Tob1 Smad1 Smad Bmp2 Bmp4 Brk-1 Tob1 Smad1 Smad Bmp2 Bmp4 Brk-1 Tob1 Smad1 Smad Rxra Rxrb Rxrg Rara Bmp Bmp genes genes RA RA genes genes Calmodulin Calmodulin family family
51 Conclusions! Signal processing, math, computer science, statistics: ever-increasing role in genomics! New frontiers: " Protein arrays " Mass Spect " Molecular Imaging! Bottleneck will remain: computational and statistical inadequacies!
52 Dawning of Post-Genomic Era
53 Post-Post-Genomic Era?
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