1、Analyzing Metabolomic Datasets,Jack Liu Statistical Science, RTP, GSK 7-14-2005,Overview,Features of Metabolomic datasets Pre-learning procedures Experimental design Data preprocess and sample validation Metabolite selection Unsupervised learning Profile clustering SVD/RSVD Supervised learning Softw
2、are,Why metabolomics?,Discover new disease biomarkers for screening and therapy progression A small subsets of metabolites can indicate an early disease stage or predict a therapy efficiency Associate metobolites (functions) with transcripts (genes) Metobolites are downstream results of gene express
3、ion,Metabolomics datasets,Advantages Metabolomics are not organism specific = make cross-platform analysis possible Changes are usually large Closer to phenotype Metabolites are well known (900-1000) Disadvantages Lots of missing data and mismatches (like Proteomics) Expensive (about 2-10 more expen
4、sive than Affymetrix),Experimental design,Traditional experimental design still apply Blocking Randomization Enough replicates Design the experiment based on the expectation A two-group design will not lead to a complete profiling (if samples in groups are homogenous) A multiple-group design may hav
5、e difficulty for supervised learning (if group number is large and data is noisy),Data preprocessing,Perform transformation Log-2 transformation is a common choice Normalization: use simple ones Summarization is needed for technical replicates Filter variables by missing patterns What to do with the
6、 missing data?,“Curse of missing data”,Missing can be due to multiple causes Informative missing Inconsistency / mismatch Unknown missing (we recently identified a suppression effect in Proteomics) What to do? Replace with the detection limit (nave) Leave as it is and let the algorithm to deal with
7、it (we may ignore important missing patterns) Single imputation (KNN, SVD. Not easy for a data with 20% missing) Multiple imputation (How to impute? Not easy to apply) Whats needed? Theory support for univariate modeling incorporating missing values/censored values,NCI dataset,58 cells and 300 metab
8、olites, no replicates These cells are the majorities of the famous NCI-60 cancer cell lines 27% missing data. Can not replace missing values with a low value. Why?,Missing value replacement: does it always work?,Before replacement Correlation = 0.88,After replacement Correlation = 0.68,Cell 1 and 2
9、are both breast cancer cell types,Note: use pair-wise deletion to compute correlation; replace with value 13.,Sample validation,Objective After we do the experiment, how do we decide if a sample has passed QC and is not an outlier? Solutions Technical QC measures PCA: visual approach. Accepting or n
10、ot is arbitrary Correlation-based method: formal and quantitative approach; based on all the data; has been taken by GSK as the formal procedure Sample validation is a cost-saving procedure,Metabolite selection,Objective Filter metabolites and assign significance Outcome Least square means Fold chan
11、ge estimates and p-values High dimensional linear modeling All the variables share the same X matrix and the same decomposition Implemented in PowerArray 100 faster than SAS Multivariate approach Cross-metabolite error model: not recommended unless n is very small (df 10) PCA/PLS method: useful if n
12、o replicates,Metabolite selection: example,ANOVA ModelingTwo-way ANOVAConsider block effectsSpecify interesting contrasts,ANOVA modeling resultsSignificant metabolitesMeans for each conditionsFold changes,Unsupervised learning,Clustering Hierarchical clustering K-means/K-medians (partitioning) Profi
13、le clustering SVD/RSVD Ordination/segmentation for heatmaps Plots based on scores/loadings Gene shaving (iterative SVD),Profile clustering,Clustering based on profiles Different from K-means or hierarchical clustering No need to specify K Does not cluster all the observations only extract those with
14、 close neighbors Guarantee the quality of each cluster Works on a graph instead of a matrix,Profile clustering - NCI,Use correlation cutoff 0.90 Revealed 9 tight clusters. Most of the clusters include cell lines with the same cancer type.,Unexpected clusters?,MALME-3M (melanoma) are strongly correla
15、ted with other three renal cancersHS-578T (breast cancer), SF-268 (CNS cancer), HOP-92 (non small cell lung cancer) are totally different cell lines but they share similar metabolic profiles,Singular value decomposition,SVD in statisticsPrinciple component analysisPartial least squareCorrespondence
16、analysisBi-plot,SVD in -omics analysisPCA for clusteringSVD-based matrix imputationSVD for ordinationAffymetrix signal extraction,Robust singular value decomposition,Advantages: Robust to outliers Automatically deals with missing entries Different versions of approaches L2-ALS: Gabriel and Zamir (19
17、79) L1-ALS: Hawkins, Li Liu and Young (2002) LTS-ALS: Jack Liu and Young (2004),Alternating least trimmed squares,Least trimmed squares: Solves byEstimation General: genetic algorithm Single-variate has much better solutions We used Brents search,Supervised learning: GSK use,Regression PLS Stepwise
18、regression LARS/LASSO Classification PLS-DA / SIMCA SVM,Supervised learning: whats useful for drug discovery?,A model will not be particularly useful if it involves thousands of variables A model will not be useful it is not interpretable Therefore, a model is useful if is Easy to interpret Easy to
19、apply prediction Better than empirical guess Variable selection for regression or classification has attracted a lot of interest,Volcano plots,Scatter plots,Visualizing LSMeans,Heatmaps,Simca,Analyses PCA PLS PLS-DA / SIMCA Advantages Takes cares of missing data Good job on model validation,PowerArr
20、ay,Analyses High dimensional linear modeling RSVD/RPCA Profile clustering + pattern analysis (available soon) Advantages Public version is free SpotFire-like visualizations Extremely easy to use Available from http:/www.niss.org/PowerArray. Complete documentation available in Sep. Email or youngniss.org for questions,
