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Data analysis workflows part 1 2015

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Dmitry Grapov
Dmitry Grapov

part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md

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Omic Data Analysis Workflow Dmitry Grapov, PhD CDS- Creative Data Solutions
data analysis and visualization Grapov et. al., Circ. Cardiovasc. Genet. 2014 Network Analysis Multivariate Modeling Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260 J. Proteome Res., 2015, 14 (1), pp 557–566 DOI: 10.1021/pr500782g Biomarker validation •Metabolomics can offer real-time insight into treatment efficacy and drive personalized medicine decisions
Metabolomics: study of small molecules
Metabolome: a proxy for phenotype
PMID:24204828 2009 ~10% variance explained Many diseases, including aging, have dominant metabolic components (e.g. metabolic syndrome) Genotype + metabolome >40% variance explained Type 2 Diabetes Need for Integromics
1. exploratory analysis 2. statistical analysis 3. predictive modeling 4. functional analysis Follow along: report/report.md View Code: report/report.Rmd Example: Proteomic and metabolomic data analysis https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Wor kflow/
Projection of Data The algorithm defines 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. PC1 PC2
Interpreting PCA Results Variance explained (eigenvalues) Row (sample) scores and column (variable) loadings
How are scores and loadings related? 1/3:

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Data analysis workflows part 1 2015

  • 1. Omic Data Analysis Workflow Dmitry Grapov, PhD CDS- Creative Data Solutions
  • 2. data analysis and visualization Grapov et. al., Circ. Cardiovasc. Genet. 2014 Network Analysis Multivariate Modeling Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260 J. Proteome Res., 2015, 14 (1), pp 557–566 DOI: 10.1021/pr500782g Biomarker validation •Metabolomics can offer real-time insight into treatment efficacy and drive personalized medicine decisions
  • 3. Metabolomics: study of small molecules
  • 4. Metabolome: a proxy for phenotype
  • 5. PMID:24204828 2009 ~10% variance explained Many diseases, including aging, have dominant metabolic components (e.g. metabolic syndrome) Genotype + metabolome >40% variance explained Type 2 Diabetes Need for Integromics
  • 6. 1. exploratory analysis 2. statistical analysis 3. predictive modeling 4. functional analysis Follow along: report/report.md View Code: report/report.Rmd Example: Proteomic and metabolomic data analysis https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Wor kflow/
  • 7. Projection of Data The algorithm defines 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. PC1 PC2
  • 8. Interpreting PCA Results Variance explained (eigenvalues) Row (sample) scores and column (variable) loadings
  • 9. How are scores and loadings related? 1/3: