![Page 1: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/1.jpg)
Jianguo (Jeff) Xia
Dr. David Wishart Lab
University of Alberta, Canada
The Fifth International Conference of Metabolomic Society
![Page 2: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/2.jpg)
OutlineI. Overview of procedures for metabolomic
studies
II. Introduction to different data processing & statistical methods
III. MetaboAnalyst – a web service for metabolomic data processing, analysis and annotation
IV. Conclusions & future directions
The Fifth International Conference of Metabolomic Society 2
![Page 3: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/3.jpg)
The Fifth International Conference of Metabolomic Society 3
![Page 4: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/4.jpg)
Data collection
The Fifth International Conference of Metabolomic Society 4
Biological Samples → Spectra
![Page 5: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/5.jpg)
Data processing
The Fifth International Conference of Metabolomic Society 5
Raw Spectra → Data Matrix
![Page 6: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/6.jpg)
Data analysis
The Fifth International Conference of Metabolomic Society 6
Extract important features/patterns
![Page 7: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/7.jpg)
Data interpretation
Mainly a manual processRequire domain expert knowledgeTools are coming:
Comprehensive metabolite databases Network visualization Pathway analysis
The Fifth International Conference of Metabolomic Society 7
Features/patterns → biological knowledge
![Page 8: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/8.jpg)
The Fifth International Conference of Metabolomic Society 8
![Page 9: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/9.jpg)
Data processing (I)
The Fifth International Conference of Metabolomic Society 9
Purposes:To convert different metabolomic data into data
matrices suitable for varieties of statistical analysisQuality control
To check for inconsistencies To deal with missing values To remove noises
![Page 10: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/10.jpg)
Data processing (II)
The Fifth International Conference of Metabolomic Society 10
A data matrix with rows represent samples and columns represents features (concentrations/intensities/areas)
![Page 11: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/11.jpg)
Data normalization
The Fifth International Conference of Metabolomic Society 11
Purposes:To remove systematic variation between experimental
conditions unrelated to the biological differences (i.e. dilutions, mass) Sample normalization (row-wise)
To bring variances of all features close to equal Feature normalization (column-wise)
![Page 12: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/12.jpg)
Sample normalization
The Fifth International Conference of Metabolomic Society 12
By sum or total peak areaBy a reference compound (i.e. creatinine, internal
standard)By a reference sample
a.k.a “probabilistic quotient normalization” (Dieterle F, et al. Anal. Chem. 2006)
By dry mass, volume, etc
![Page 13: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/13.jpg)
Feature normalizationLog transformationScaling
The Fifth International Conference of Metabolomic Society 13
-- van den Berg RA, et al. BMC Genomics (2006) 7:142
![Page 14: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/14.jpg)
The Fifth International Conference of Metabolomic Society 14
![Page 15: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/15.jpg)
Data Analysis
The Fifth International Conference of Metabolomic Society 15
![Page 16: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/16.jpg)
Volcano-plotArrange features along dimensions of statistical (p-
values from t-tests) and biological (fold changes) changes;
The assumption is that features with both statistical and biological significance are more likely to be true positive.
Widely used in microarray and proteomics data analysis
The Fifth International Conference of Metabolomic Society 16
![Page 17: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/17.jpg)
PLS-DADe facto standard for
chemometric analysisA supervised method that
uses multiple linear regression technique to find the direction of maximum covariance between a data set (X) and the class membership (Y)
Extracted features are in the form of latent variables (LV)
The Fifth International Conference of Metabolomic Society 17
![Page 18: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/18.jpg)
PLS-DA for feature selectionVariable importance in projection or VIP score
A weighted sum of squares of the PLS loadings. The weights are based on the amount of explained Y-variance in each dimension.
Based on the weighted sum of PLS-regression
coefficients. The weights are a function of the reduction of the sums of
squares across the number of PLS components.
The Fifth International Conference of Metabolomic Society 18
![Page 19: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/19.jpg)
Over fitting problemPLS-DA tend to over fit data
It will try to separate classes even there is no real difference between them! Westerhuis, C.A., et al. (2007) Assessment of PLSDA cross
validation. Metabolomics, 4, 81-89.
Require more rigorous validationFor example, to use permutations to test the
significance of class separations
The Fifth International Conference of Metabolomic Society 19
![Page 20: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/20.jpg)
1) Use the same data set with its class labels reassigned randomly.
2) Build a new model and measure its performance (B/W)
3) Repeat many times to estimate the distribution of the performance measure (not necessarily follows a normal distribution).
4) Compare the performance using the original label and the performance based on the randomly labeled data
Permutation tests
The Fifth International Conference of Metabolomic Society 20
![Page 21: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/21.jpg)
Multi-testing problemP-value appropriate to a single test situation is
inappropriate to presenting evidence for a set of changed features.Adjusting p-values
Bonferroni correction Holm step-down procedure
Using false discovery rate (FDR) A percentage indicating the expected false positives among
all features predicted to be significant More powerful, suitable for multiple testing
The Fifth International Conference of Metabolomic Society 21
![Page 22: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/22.jpg)
A well-established method widely used for identification of differentially expressed genes in microarray experiments
Use moderated t-tests to computes a statistic dj for each gene j, which measures the strength of the relationship between gene expression (X) and a response variable (Y).
Uses non-parametric statistics by repeated permutations of the data to determine if the expression of any gene is significant related to the response.
Significance Analysis of Microarray (and Metabolomics)
The Fifth International Conference of Metabolomic Society 22
![Page 23: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/23.jpg)
ClusteringUnsupervised learningGood for data overviewUse some sort of distance measures to group
samplesPCAHeatmap & dendrogramSOM & K-means
The Fifth International Conference of Metabolomic Society 23
![Page 24: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/24.jpg)
ClassificationSupervised learningMany traditional multivariate statistical methods are
not suitable for high-dimensional data, particularly small sample size with large feature numbers
New or improved methods, developed in the past decades for microarray data analysisSupport vector machine (SVM)Random Forests
The Fifth International Conference of Metabolomic Society 24
![Page 25: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/25.jpg)
The Fifth International Conference of Metabolomic Society 25
![Page 26: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/26.jpg)
Microarray data analysis pipeline
The Fifth International Conference of Metabolomic Society 26
![Page 27: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/27.jpg)
Bijlsma S et al. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal. Chem. (2006) 78:567–574
A proposed pipeline for metabolomics studies
The Fifth International Conference of Metabolomic Society 27
![Page 28: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/28.jpg)
-- A web service for high-throughput metabolomic data processing, analysis and annotation
-- Implementation of all the methods mentioned in the form of user-friendly web interfaces
-- www.metaboanalyst.ca
The Fifth International Conference of Metabolomic Society 28
![Page 29: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/29.jpg)
The Fifth International Conference of Metabolomic Society 29
Metaboliteconcentrations
Data annotation• Peak searching (3)• Pathway mapping
MS / NMRpeak lists
GC/LC-MSraw spectra
• Peak detection• Retention time correction
Peak alignment
Data analysis• Univariate analysis (3)• Dimension reduction (2)• Feature selection (2)• Cluster analysis (4)• Classification (2)
• Data integrity check• Missing value imputation
Data normalization• Row-wise normalization (4)• Column-wise normalization (4)
MS / NMR spectra bins
Baseline filtering
Download• Processed data• PDF report• Images
![Page 30: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/30.jpg)
Implementation features
The Fifth International Conference of Metabolomic Society 30
![Page 31: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/31.jpg)
The Fifth International Conference of Metabolomic Society 31
![Page 32: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/32.jpg)
Some usage statistics
Over 1,200 visits since publication (~15 / day)
The Fifth International Conference of Metabolomic Society 32
![Page 33: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/33.jpg)
Current status
The Fifth International Conference of Metabolomic Society 33
![Page 34: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/34.jpg)
Challenges & future directionsUnbiased and comprehensive survey of
metabolomeNMR only able to detect more abundant compound
species (> 1 µmol)MS are usually optimized to detect compounds of
certain classesSystematic classification of compounds (ontology) More efficient pathway analysis & visualization
The Fifth International Conference of Metabolomic Society 34
![Page 35: Metabolomic Data Processing & Statistical Analysis](https://reader036.vdocuments.us/reader036/viewer/2022062408/56813aba550346895da2c2d3/html5/thumbnails/35.jpg)
Acknowledgement
• Dr. David Wishart
• Dr. Nick Psychogios
• Nelson Young
The Fifth International Conference of Metabolomic Society 35
Alberta Ingenuity Fund (AIF)
The Human Metabolome Project (HMP)
University of Alberta