1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Metabolomic Data Analysis Using MetaboAnalyst
Module 7
Metabolomic Data Analysis Using MetaboAnalyst
David Wishart
Informatics and Statistics for Metabolomics
June 16-17, 2 014

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, probabilistic quotient, 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
Web server designed to handle large sets of LC-MS, GC-MS or NMR-based metabolomic data
Supports both univariate and multivariate data processing, including t-tests, ANOVA, PCA, PLS-DA
Identifies significantly altered metabolites, produces colorful plots, provides detailed explanations & summaries
Links sig. metabolites to pathways via SMPDB

15. MetaboAnalyst Workflow
Data pre-processing
Data normalization
Data analysis
Data annotation

16. Two/multi-group analysis
GC/LC-MS raw spectra
Peak lists
Spectral bins
Concentration table
Spectra processing
Peak processing
Noise filtering
Missing value estimation
Row-wise normalization
Column-wise normalization
Combined approach
Data input
Data processing
Data integrity check
Data normalization
Functional Interpretation
Statistical Exploration
Enrichment analysis
Pathway analysis
Time-series analysis
Two/multi-group analysis
Over representation analysis
Single sample profiling
Quantitative enrichment
analysis
Enrichment analysis
Topology analysis
Interactive visualization
Data overview
Two-way ANOVA
ANOVA - SCA
Time-course analysis
Univariate analysis
Correlation analysis
Chemometric analysis
Feature selection
Cluster analysis
Classification
Outputs
Image Center
Quality checking
Other utilities
Resolution: 150/300/600 dpi
Format: png, tiff, pdf, svg, ps
Methods comparision
Temporal drift
Batch effect
Biolgoical checking
Peak searching
Pathway mapping
Name/ID conversion
Lipidomics
Processed data
Result tables
Analysis report
Images

17. MetaboAnalyst Overview
Raw data processing
Using MetaboAnalyst
Data Reduction & Statistical analysis
Functional enrichment analysis
Using MSEA in MetaboAnalyst
Metabolic pathway analysis
Using MetPA in MetaboAnalyst

18. Example Datasets
Click the “Data Formats” link

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

20. Metabolomic Data Processing

21. 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)

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

23. Alternatively …

24. Data Set Selected
Here we will be selecting 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

25. Data Integrity Check

26. Data Normalization

27. 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, row-wise normalization, column-wise normalization and combined normalization
Row-wise normalization aims to make each sample (row) comparable to each other (i.e. urine samples with different dilution effects)

28. Data Normalization
Column-wise normalization 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
Four methods have been implemented for this purpose – log transformation, autoscaling, Pareto scaling and range scaling

29. Normalization Result

30. 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

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

32. Outlier Removal

33. Noise Reduction

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

35. Data Reduction and Statistical Analysis

36. Common tasks To identify important features
To detect interesting patterns
To assess difference between the phenotypes
To facilitate classification or prediction
NOW ON YOUR OWN

38. ANOVA

39. View Individual Compounds

40. Questions
Q: Which compounds show significant difference among all the neighboring groups (0-15, 15-30, and 30-45)?
Q: For Uracil, are groups 15, 30, 45 significantly different from each other?

41. Overall correlation pattern

42. High resolution image
Specify format
Specify resolution
Specify size

43. Question
Q: In untargeted metabolomics using NMR, researchers often look for region(s) on the spectra showing biggest change in their correlation patterns under different conditions. Can you do that in MetaboAnalyst?
Hint: check the available parameters of Correlation analysis

44. Template 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

45. Template Matching (cont.)
Strong linear
+ correlation
to grain %
Strong linear
- correlation
to grain %

46. Question
Q: Identify compounds that decrease in the first three groups but increase in the last group?

47. PCA Scores Plot

48. PCA Loading Plot
Compounds
most responsible
for separation

49. 3D-PCA

50. Question
Q: Identify compounds that contribute most to the separation between group 15 and 45

51. PLS-DA Score Plot

52. Evaluation of PLS-DA Model
PLS-DA Model evaluated by cross validation of Q2 and R2
More principal components to model improves quality of fit, but try to minimize this value
3 Component (3 PCs)model seems to be a good compromise here
Good R2/Q2 (>0.7)

53. Important Compounds

54. Model Validation

55. Questions Q: What does p < 0.01 mean?
Q: How many permutations need to be performed if you want to claim p value < ?

56. Heatmap Visualization
Note that the Heatmap is not being clustered on Rows (i.e. the % grain in diet)

57. Heatmap Visualization (cont.)

58. Question
Q: Identify compounds with a low concentration in group 0, 15 but increase in the group 35 and 45 Q: Which compound is the only one significantly increased in group 45?

59. Download Results

60. Analysis Report

61. Metabolite Set Enrichment Analysis

62. Metabolite Set Enrichment Analysis (MSEA)
Web tool 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
or Metaboanalyst

63. Enrichment Analysis
Purpose: To test if there are some biologically meaningful groups of metabolites that are significantly enriched in your data
Biological meaningful
Pathways
Disease
Localization
Currently, only supports human metabolomic data

64. MSEA Accepts 3 kinds of input files
1) list of metabolite names only (ORA)
2) list of metabolite names + concentration data from a single sample (SSP)
3) a concentration table with a list of metabolite names + concentrations for multiple samples/patients (QEA)

65. The MSEA approach 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

66. 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)

67. Start with a Compound List

68. 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

69. Compound Name Standardization

70. Name Standardization (cont.)

71. Select a Metabolite Set Library

72. Result

73. Result (cont.)

74. The Matched Metabolite Set

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

76. Single Sample Profiling (cont.)

77. Concentration Comparison

78. Concentration Comparison (cont.)

79. Quantitative Enrichment Analysis

80. Result

81. The Matched Metabolite Set

82. Question
Q: Are these metabolites increased or decreased in the cachexia group?

83. Metabolic Pathway Analysis with MetPA

84. Pathway Analysis
Purpose: to extend and enhance metabolite set enrichment analysis for pathways by
Considering the pathway structures
Supporting pathway visualization
Currently supports 15 organisms

85. Data Upload

86. 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)

87. Normalization

88. Pathway Libraries

89. Network Topology Analysis

90. 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

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

92. Pathway Visualization

93. Pathway Visualization (cont.)

94. Question
Q: Which pathway do you think is likely to be affected the most? Why?

95. Result

96. Not Everything Was Covered
Clustering (K-means, SOM)
Classification (SVM, randomForests)
Time-series data analysis
Two factor data analysis
Data quality checks
Peak searching ….

97. Time Series Analysis in MetaboAnalyst

98. Quality Checking Module