1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Metabolomic Data Analysis Using MetaboAnalyst
Module 5
Metabolomic Data Analysis Using MetaboAnalyst
David Wishart
Informatics and Statistics for Metabolomics
May 26-27, 2016

4. Learning Objectives
To become familiar with the standard metabolomics data analysis workflow
To become aware of key elements such as: data integrity checking, outlier detection, quality control, normalization, scaling, etc.
To learn how to use MetaboAnalyst to facilitate data analysis

5. A Typical Metabolomics Experiment

6. 2 Routes to Metabolomics1 2 3 4 5 6 7
ppm
Quantitative (Targeted)
Methods
Chemometric (Profiling)
Methods
-25
-20
-15
-10 -5 5 10 15 20 25
-30
PC1
PC2
PAP
ANIT
Control 1 2 3 4 5 6 7
ppm
hippurate
urea
allantoin
creatinine
2-oxoglutarate
citrate
TMAO
succinate
fumarate
water
taurine

7. Metabolomics Data Workflow
Chemometric Methods Targeted Methods
Data Integrity Check
Spectral alignment or binning
Data normalization
Data QC/outlier removal
Data reduction & analysis
Compound ID
Data Integrity Check
Compound ID and quantification
Data normalization
Data QC/outlier removal
Data reduction & analysis

8. Data Integrity/Quality
LC-MS and GC-MS have high number of false positive peaks
Problems with adducts (LC), extra derivatization products (GC), isotopes, breakdown products (ionization issues), etc.
Not usually a problem with NMR
Check using replicates and adduct calculators
MZedDB
HMDB

9. Data/Spectral Alignment
Important for LC-MS and GC-MS studies
Not so important for NMR (pH variation)
Many programs available (XCMS, ChromA, Mzmine)
Most based on time warping algorithms

10. Binning (3000 pts to 14 bins) xi,yi x = 232.1 (AOC) y = 10 (bin #)
bin1 bin2 bin3 bin4 bin5 bin6 bin7 bin8...

11. Data Normalization/Scaling
Can scale to sample or scale to feature
Scaling to whole sample controls for dilution
Normalize to integrated area, probabilistic quotient method, internal standard, sample specific (weight or volume of sample)
Choice depends on sample & circumstances
Same or different?

12. Data Normalization/Scaling
Can scale to sample or scale to feature
Scaling to feature(s) helps manage outliers
Several feature scaling options available: log transformation, auto-scaling, Pareto scaling, and range scaling
MetaboAnalyst
Dieterle F et al. Anal Chem Jul 1;78(13):

13. Data QC, Outlier Removal & Data Reduction
Data filtering (remove solvent peaks, noise filtering, false positives, outlier removal -- needs justification)
Dimensional reduction or feature selection to reduce number of features or factors to consider (PCA or PLS-DA)
Clustering to find similarity

14. MetaboAnalyst
A comprehensive web server designed to process & analyze LC-MS, GC-MS or NMR-based metabolomic data

15. MetaboAnalyst History
2009 v1.0 - Supports both univariate and multivariate data processing, including t-tests, ANOVA, PCA, PLS-DA, colorful plots, with detailed explanations & summaries
2012 v2.0 - Identifies significantly altered functions & pathways
2015 v3.0 – Better performance, better graphical interactivity, biomarker analysis, power analysis, integration with gene expression data …

16. MetaboAnalyst Overview
Raw data processing
Data reduction & statistical analysis
Functional enrichment analysis
Metabolic pathway analysis
Power analysis and sample size estimation
Biomarker analysis
Integrative analysis

17. MetaboAnalyst Modules
Data pre-processing
Data normalization
Data analysis
Data interpretation

18. MetaboAnalyst Modules

19. Example Datasets
Click the “Data Formats” link

20. Example Datasets
Right click the “download” link of the first example to save to your local computer

21. Metabolomic Data Processing

22. Common Tasks
Purpose: to convert various raw data forms into data matrices suitable for statistical analysis
Supported data formats
Concentration tables (Targeted Analysis)
Peak lists (Untargeted)
Spectral bins (Untargeted)
Raw spectra (Untargeted)

23. Select a Module (Statistical Analysis)

24. Data Upload
Go back to the home page and Click “click here to start” to upload the file

25. Alternatively …

26. Data Set Selected
Here we have selected a data set from dairy cattle fed different proportions of cereal grains (0%, 15%, 30%, 45%)
The rumen was analyzed using NMR spectroscopy using quantitative metabolomic techniques
High grain diets are thought to be stressful on cows

27. Data Integrity Check

28. Data Normalization
Samples = rows
Compounds = columns

29. Data Normalization
At this point, the data has been transformed to a matrix with the samples in rows and the variables (compounds/peaks/bins) in columns
MetaboAnalyst offers three types of normalization, sample normalization, data transformation, and data scaling
Sample normalization aims to make each sample (row) comparable to each other (i.e. urine samples with different dilution effects)

30. Data Normalization
Data transformation & data scaling aims to make each variable (column) comparable in scale to each other, thereby generating a “normal” distribution
This procedure is useful when variables are of very different orders of magnitude
Transformation operates on each data point itself
Log and cube root transformation
Scaling operates on each variable column
Autoscaling, Pareto scaling and range scaling

31. Normalization Result

32. Data Normalization
You cannot know a priori what the best normalization protocol will be
MetaboAnalyst allows you to interactively explore different normalization protocols and to visually inspect the degree of “normality” or Gaussian behavior
This example is nicely normalized

33. Next Steps
After normalization has been completed it is a good idea to look at your data a little further to identify outliers or noise that could/should be removed

34. Quality Control Dealing with outliers Noise reduction
Detected mainly by visual inspection
May be corrected by normalization
May be excluded
Noise reduction
More of a concern for spectral bins/ peak lists
Usually improves downstream results

35. Visual Inspection What does an outlier look like?
Finding outliers via PCA
Finding outliers via Heatmap

36. Outlier Removal (Data Editor)

37. Noise Reduction (Data Filtering)

38. Noise Reduction (cont.)
Characteristics of noise & uninformative features
Low variances (default)
Low intensities

39. Data Reduction and Statistical Analysis

40. Common Tasks To identify important features
To detect interesting patterns
To assess difference between the phenotypes
To facilitate classification or prediction
We will look at ANOVA, Multivariate Analysis (PCA, PLS-DA) and Clustering

42. ANOVA Looking at 4 different dairy cow populations
0% grain in diet
15% grain in diet
30% grain in diet
45% grain in diet
Try to identify those metabolites that are different between all groups or just between 0% and everything else

43. ANOVA Click this to view the table Click this spot and the 3-PP
graph pops up

44. View Individual Compounds
Click this to see
the uracil graphs

45. What’s Next?
Click and compare different compounds to see which ones are most different or most similar between the 4 groups
Click on the Correlation link (under the ANOVA link) to generate a heat map that displays the pairwise compound correlations and compound clusters

46. Overall Correlation Pattern
Click this to save
a high res. image

47. High Resolution Image

48. What’s Next?
When looking at >2 groups it is often useful to look for patterns or trends within particular metabolites
Use Pattern Hunter to find these trends

49. Pattern Matching
Looking for compounds showing interesting patterns of change
Essentially a method to look for linear trends or periodic trends in the data
Best for data that has 3 or more groups

50. Pattern Matching (cont.)
Strong linear
+ correlation
to grain %
Strong linear
- correlation
to grain %

52. Multivariate Analysis
Use PCA option to view the separation (if any) in the 4 groups
Look at the 2D PCA Score Plot
2 most significant principal components
Look at the 2D PCA Loading Plot
Look at the PCA Plot in 3D
3 most significant principal components
Options for viewing are located in the top tabs

53. PCA Scores Plot

54. PCA Loading Plot Compounds most responsible for separation
Click on a point
to view

55. 3D Score Plot
Drag to rotate
Mouse over to see
sample names

57. Multivariate Analysis
Use PLS-DA option to view the separation of the 4 (labeled) groups
PLS-DA “rotates” the PCA axes to maximize separation
Look at the 2D PLS Scores Plot
Look at the Q2 and R2 Values (Cross Validation)
Use the VIP plot to ID important metabolites (VIP > 1.2)

58. PLS-DA Score Plot

59. Evaluation of PLS-DA Model
PLS-DA Model evaluated by cross validation of Q2 and R2
Using too many components can over-fit
3 component model seems to be a good compromise here
Good R2/Q2 (>0.7)

60. Important Compounds

61. Model Validation
Note, permutation is computationally intensive. It is not performed by default. Users need to set the permutation number and press the submit button

63. Hierarchical Clustering (Heat Maps)
An alternative way of viewing or clustering multivariate data
Allows one to look at the behavior of individual metabolites
Can ask questions such as: which compounds have a low concentration in group 0, 15 but increase in the group 35 and 45? or which compound is the only one significantly increased in group 45?

64. Heatmap Visualization
Note that the Heatmap is not being clustered on Rows. It is ordered by the class labels

65. Heatmap Visualization (cont.)

66. What’s Next? Most of the multivariate analysis is now done
MetaboAnalyst has been keeping track of the plots or graphs you have generated
Now its time to generate a printed report that summarizes what you’ve done and what you’ve found

67. Download Results

68. Analysis Report

69. Select a Module (Enrichment Analysis)

70. Metabolite Set Enrichment Analysis (MSEA)
Designed to handle lists of metabolites (with or without concentration data)
Modeled after Gene Set Enrichment Analysis (GSEA)
Supports over representation analysis (ORA), single sample profiling (SSP) and quantitative enrichment analysis (QEA)
Contains a library of 6300 pre-defined metabolite sets including 85 pathway sets & 850 disease sets
Now part of
Metaboanalyst

71. Enrichment Analysis
Purpose: To test if there are biologically meaningful groups of metabolites that are significantly enriched in your data
Biological meaningful in terms of:
Pathways
Diseases
Genetic variations
Localization
Currently, MSEA only supports human metabolomic data

72. MSEA Accepts 3 kinds of input files
list of metabolite names only (ORA – over representation analysis)
list of metabolite names + concentration data from a single sample (SSP – single sample profiling)
a concentration table with a list of metabolite names + concentrations for multiple samples/patients (QEA – quantitative enrichment analysis)

73. The MSEA Approach ORA SSP QEA Over Representation Analysis
Single Sample Profiling
Quantitative Enrichment Analysis
Compound concentrations
Compound concentrations
Compound concentrations
Compare to normal references
Compound selection
(t-tests, clustering)
Assess metabolite sets directly
Important compound lists
Abnormal compounds
Find enriched biological themes
ORA input
For MSEA
Metabolite set libraries
Biological interpretation

74. Data Set Selected
Here we are using a collection of metabolites identified by NMR (compound list + concentrations) from the urine from 77 lung and colon cancer patients, some of whom were suffering from cachexia (muscle wasting)

75. Start with a Compound List for ORA

76. Upload Compound List Normally GSEA would require a list of all known
genes for the given
platform. Here we
just use the list of
metabolites found
in KEGG. ORA is
a “weak” analysis in
MSEA

77. Perform Compound Name Standardization

78. Name Standardization (cont.)

79. Select a Metabolite Set Library

80. Result

81. Result (cont.)
Click on details
to see more

82. The Matched Metabolite Set
Click on SMPDB
to see more information

83. Phenylalanine and Tyrosine Metabolism in SMPDB

84. Single Sample Profiling (SSP) (Basically used by a physician to analyze a patient)

85. Concentration Comparison

86. Concentration Comparison (cont.)

87. Quantitative Enrichment Analysis (QEA)

88. Result
Click on details
to see more

89. The Matched Metabolite Set

90. Select a Module (Pathway Analysis)

91. Pathway Analysis
Purpose: to extend and enhance metabolite set enrichment analysis for pathways by
Considering pathway structures
Supporting pathway visualization
Currently supports analysis for 21 diverse (model) organisms such as humans, mouse, drosophila, arabadopsis, E. coli, yeast, etc. (KEGG pathways only)

92. Data Set Selected
Here we are using a collection of metabolites identified by NMR (compound list + concentrations) from the urine from 77 lung and colon cancer patients, some of whom were suffering from cachexia (muscle wasting)

93. Pathway Analysis Module

94. Data Upload

95. Perform Data Normalization

96. Select Pathway Libraries

97. Perform Network Topology Analysis

98. Pathway Position Matters
Which positions are important?
Hubs
Nodes that are highly connected (red ones)
Bottlenecks
Nodes on many shortest paths between other nodes (blue ones)
Graph theory
Degree centrality
Betweenness centrality
Junker et al. BMC Bioinformatics 2006

99. Which Node is More Important?
High degree centrality
High betweenness centrality

100. Pathway Visualization

101. Pathway Visualization (cont.)

102. Pathway Impact
Incorporates parameters such as the log fold-change of the DE metabolites, the statistical significance of the set of pathway genes and the topology of the signaling pathway
Combines the pathway topology with the over-representation evidence

103. Result

104. Select a Module (Biomarker Analysis)

105. Biomarker Analysis
Purpose is to find biomarkers using ROC (receiver operator characteristic) curves with high sensitivity and specificity
Maximize AUC under ROC curve while minimizing the number of metabolites used in the biomarker panel
3 different modules (univariate – single marker at a time, multivariate – many combinations of biomarkers, manual – user choice)

106. Select Test Data Set 1

107. Data Set Selected
90 patients (expectant mothers) at 3 months pregnancy
Serum samples
45 patients went on to develop pre-eclampsia at 6-7 months
45 patients had normal pregancies
Trying to find biomarkers for predicting early pre-eclampsia

108. Perform Data Integrity Check

109. Perform Log Normalization

110. Check That It’s Normally Distributed
before
after

111. Select Multivariate Option

112. View ROC Curve

113. Choose a Model (95% conf.)
Select model

114. 95% Confidence Interval

115. Select Sig. Features Tab

116. View VIP Plot

117. Select a Module (Power Analysis)

118. Statistical Power
Statistical power is the ability of a test to detect an effect, if the effect actually exists
A power of 0.8 in a clinical trial means that the study has a 80% chance of ending up with a statistically significant treatment effect if there really was an important difference between treatments.
To answer research questions:
How powerful is my study?
How many samples do I need to have for what I want to get from the study?

119. Statistical Power (cont.)
The statistical power of a test depends:
Sample size,
Significance criterion (alpha)
Effect size
Increase power
Effect size
Sample size
Decrease Power
Significance criterion

120. The Approach How do we get these values?
Effect size can be estimated from a pilot data;
Significance criteria
Single metabolite - p value cutoff (i.e. 0.05, 0.01)
Metabolomics data – FDR (i.e. 0.1)
Sample size is our interest
Power value is our interest
You need to upload a pilot data, and set the criteria, MetaboAnalyst will compute a power vs. sample size curve by computing power values for a range of sample sizes [3, 1000]

121. Power vs. Sample size
At least 60 samples/group will needed to get a power of 0.8

122. Not Everything Was Covered
Clustering (K-means, SOM)
Classification (SVM, randomForests)
Time-series data analysis
Two factor data analysis
Integrative pathway analysis (gene and metabolite)

123. Time Series Analysis in MetaboAnalyst

124. Integrative Pathway Analysis