Published on February 17, 2014
Biology Chemistry Partial Least Squares (O-/PLS/-DA) Informatics Partial Least Squares (O-/PLS/-DA) modeling of metabolomic sample processing methods Goal: Use PLS to identify metabolites which best discriminate (most different between) sample processing methods (Used DATA: Pumpkin data 1.csv) Topics: 1.Model Selection 2.Results visualization 3.Feature Selection 4.Validation
Biology Chemistry Partial Least Squares (O-/PLS/-DA) Informatics Partial Least Squares Modeling Discriminant Analysis (PLS-DA) Steps 1.Calculate a single Y PLS model to discriminate between extraction/treatment 2.Select optimal scaling and model latent variable (LV) number 3.Overview PLS scores and loadings plots 4.Validate model 5.Repeat steps 1-4 for an O-PLS model Visualize: 1.Sample scores annotated by extraction and treatment 2.Variable loadings plot Exercise: 1.How are scores different between 1-Y PLS, 1-Y O-PLS, 2-Y O-PLS? 2.Are there any moderate or extreme outliers? 3.What variables contribute most to the differences between treatments?
Model Performance Biology Chemistry Partial Least Squares (O-/PLS/-DA) Informatics RMSEP - root mean squared error of prediction (fit to left out set) Q2 - cross-validated fit to the training data Xvar- explained variance in X (measurements)
Model Validation Biology Chemistry Partial Least Squares (O-/PLS/-DA) Informatics single model performance estimates Model properties and validation settings Based on training/test splitting Based on training/test splitting and permuted Y Significance of difference between model performance and permuted NULL distributions
Biology Chemistry Informatics Model Scores single Y (extraction_treatment) Partial Least Squares (O-/PLS/-DA) Treatment Extraction Variance in extraction dominates model
Biology Chemistry Informatics Model Scores Comparison Partial Least Squares (O-/PLS/-DA) 1-Y PLS treatment extraction 1-Y O-PLS 2-Y O-PLS
Top Discriminants Biology Fatty acids, lipids Chemistry Partial Least Squares (O-/PLS/-DA) Informatics 2-Y O-PLS treatment extraction Sugar phosphates, polyols
Feature Selection Biology Chemistry Steps 1.Identify top 10% of discriminants for extraction based on metabolite • correlation with model scores • importance (loading, coeffcient, VIP) Visualize: 1.Feature selection diagnostics correlation Partial Least Squares (O-/PLS/-DA) Informatics Loading (LV1)
Feature Selection for Extraction Biology Chemistry Partial Least Squares (O-/PLS/-DA) Informatics 1 2 higher in 3 3 higher in 1
Summary Biology Chemistry Partial Least Squares (O-/PLS/-DA) Informatics Modeling Strategy (advanced) 1. Fit preliminary model 2. Evaluate of scores/loadings 3. Remove outliers 4. split data into test and train set (optional) 5. model selection (LV, OLV, etc) 6. internal (training set) model validation (permutation testing, training/testing) 7. Feature selection (optional) • Comparison of selected to excluded feature models 8. External model validation (test set)
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