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Prote-OMIC Data Analysis and Visualization
The document discusses advanced methodologies for multivariate analysis and visualization of proteomic data in a state-of-the-art facility, managing significant biological data outputs. It covers various statistical techniques, data quality assessments, experimental designs, and provides insights into analytical variability and significance testing for effective data interpretation. Additionally, it introduces software tools for dynamic data analysis and visualization, aiding researchers in extracting meaningful biological insights from complex datasets.
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Prote-OMIC Data Analysis and Visualization
- 1. Dmitry Grapov, PhD MultivariateAnalysis and Visualization of ProteOmic Data
- 2. State of theart facility producing massive amounts of biological data… >20-30K samples/yr >200 studies
- 3. Analysis at theProteOmic Scale and Beyond Genomic Proteomic Metabolomic Multi-OmicOmic integration
- 4. Sample Variable Data Analysis andVisualization Quality Assessment • use replicated mesurements and/or internal standards to estimate analytical variance Statistical and Multivariate • use the experimental design to test hypotheses and/or identify trends in analytes Functional • use statistical and multivariate results to identify impacted biochemical domains Network • integrate statistical and multivariate results with the experimental design and analyte metadata experimental design - organism, sex, age etc. analyte description and metadata - biochemical class, mass spectra, etc. VariableSample
- 5. Sample Variable Data Analysis andVisualization Quality Assessment • use replicated mesurements and/or internal standards to estimate analytical variance Statistical and Multivariate • use the experimental design to test hypotheses and/or identify trends in analytes Functional • use statistical and multivariate results to identify impacted biochemical domains Network • integrate statistical and multivariate results with the experimental design and analyte metadata Network Mapping experimental design - organism, sex, age etc. analyte description and metadata - biochemical class, mass spectra, etc. VariableSample
- 6. Data Quality Assessment Qualitymetrics •Precision (replicated measurements) •Accuracy (reference samples) Common tasks •normalization •outlier detection •missing values imputation
- 7. Principal Component Analysis (PCA)of all analytes, showing QC sample scores Batch Effects Drift in >400 replicated measurements across >100 analytical batches for a single analyte Acquisition batch Abundance QCs embedded among >5,5000 samples (1:10) collected over 1.5 yrs If the biological effect size is less than the analytical variance then the experiment will incorrectly yield insignificant results
- 8. Analyte specific dataquality overview Sample specific normalization can be used to estimate and remove analytical variance Raw Data Normalized Data Normalizations need to be numerically and visually validated log mean low precision %RSD high precision Samples QCs Batch Effects
- 9. Outlier Detection • 1variable (univariate) • 2 variables (bivariate) • >2 variables (multivariate)
- 10. bivariate vs. multivariate mixed upsamples outliers? (scatter plot) (PCA scores plot) Outlier Detection
- 11. Network Mapping Ranked statistically significantdifferences within a a biochemical context Statistics Multivariate Context + + = Statistical and Multivariate Analyses Group 1 Group 2 What analytes are different between the two groups of samples? Statistical significant differences lacking rank and context t-Test Multivariate ranked differences lacking significance and context O-PLS-DA
- 12. Network Mapping Statistics Multivariate Context + + = Statistical andMultivariate Analyses Group 1 Group 2 What analytes are different between the two groups of samples? Statistical t-Test Multivariate O-PLS-DA To see the big picture it is necessary too view the data from multiple different angles
- 13. Statistical Analysis: achieving‘significance’ significance level (α) and power (1-β ) effect size (standardized difference in means) sample size (n) Power analyses can be used to optimize future experiments given preliminary data Example: use experimentally derived (or literature estimated) effect sizes, desired p-value (alpha) and power (beta) to calculate the optimal number of samples per group
- 14. Statistical Tests • Shouldbe chosen based on the distribution (shape, type) of the (e.g. normal, negative binomial, Poisson) • Can be optimized based on data pre- treatment (e.g. NSAF, Power Law Global Error Model, PLGEM) Poisson normal
- 15. False Discovery Rate(FDR) Type I Error: False Positives (α) •Type II Error: False Negatives (β) •Type I risk = •1-(1-p.value)m m = number of variables tested
- 16. False Discovery RateAdjustment FDRadjustedp-value p-value Benjamini & Hochberg (1995) (“BH”) •Accepted standard Bonferroni •Very conservative •adjusted p-value = p-value x # of tests (e.g. 0.005 x 148 = 0.74 )
- 17. Functional Analysis Nucl. AcidsRes. (2008) 36 (suppl 2): W423-W426.doi: 10.1093/nar/gkn282 Identify changes or enrichment in biochemical domains • decrease • increase
- 18. Functional Analysis: Enrichment BiochemicalPathway Biochemical Ontology
- 19.
- 20. Artist: Chuck Close ClusterAnalysis Useful for •pattern recognition •complexity reduction Common Methods •Hierarchical •Model based •Other (k-means, k-NN, PAM, fuzzy) Linkage k-means Distribution Density
- 21. Hierarchical Clustering Similarity x x x x Dendrogram How doesmy metadata match my data structure?
- 22. Projection Methods The algorithmdefines the position of the light source Principal Components Analysis (PCA) • unsupervised • maximize variance (X) Partial Least Squares Projection to Latent Structures (PLS) • supervised • maximize covariance (Y ~ X) James X. Li, 2009, VisuMap Tech. single analyte all analytes
- 23. Interpreting scores andloadings variables with the highest loadings have the greatest contribution to sample scores loadings represent how variables contribute to sample scores loadings Scores represent dis/similarities in samples based on all variables scores
- 24.
- 25. Enrichment Network Mapping ofparents through children
- 26.
- 27. Empirical Networks • Correlationbased networks (CN) (simple, tendency to hairball) • GGM or partial correlation based networks (advanced, preference of direct over indirect relationships • *Increase in robustness with sample size 10.1007/978-1-4614-1689-0_17
- 28. Proteomic Case Study:Diabetes Markers • Small sample size (control =12, GDM =6); covariates (time of sample collection) • >600 measured colostrum proteins; ~ 300 NSAF normalized proteins retained • Multivariate classification with O-PLS-DA used to identify variables to test using PLGEM with correction for FDR • Partial-correlation protein-protein interaction network analysis
- 29. DeviumWeb https://github.com/dgrapov/DeviumWeb • visualization • statistics •clustering • PCA • O-PLS
- 30. DeviumWeb • visualization • statistics •clustering • PCA • O-PLS https://github.com/dgrapov/DeviumWeb
- 31. Software and Resources •DeviumWeb-Dynamic multivariate data analysis and visualization platform url: https://github.com/dgrapov/DeviumWeb •imDEV- Microsoft Excel add-in for multivariate analysis url: http://sourceforge.net/projects/imdev/ •MetaMapR- Network analysis tools for metabolomics url: https://github.com/dgrapov/MetaMapR •TeachingDemos- Tutorials and demonstrations •url: http://sourceforge.net/projects/teachingdemos/?source=directory •url: https://github.com/dgrapov/TeachingDemos •Data analysis case studies and Examples url: http://imdevsoftware.wordpress.com/
- 32. Questions? dgrapov@ucdavis.edu This research wassupported in part by NIH 1 U24 DK097154