Metabolomics a Promising

1
Metabolomics a Promising omics Science
By Susan Simmons University of North Carolina
Wilmington
2
Collaborators

• Dr. David Banks, Duke
• Dr. Chris Beecher, University of Michigan
• Dr. Xiaodong Lin, University of Cincinnati
• Dr. Young Truong, UNC
• Dr. Jackie Hughes-Oliver, NC State
• Dr. Stanley Young, NISS
• Dr. Ann Stapleton, UNCW Biology
• Dr. Robert Simmons, MD

3
What is Metabolomics?

• The word metabolome was first used less than a
  decade ago (1998) and referred to all low
  molecular mass compounds synthesized and
  modified by a living cell or organism
  (Villas-Boas, 2007)
• The complete human metabolome consists of
  endogenous (1800) and exogenous metabolites
  (MANY!!)
• Human Metabolome Project

4
(No Transcript)
5
Fluorene degradation - Reference pathway
(www.genome.jp/KEGGKyoto Encyclopedia of Genes
and Genomes)
6
Mass Distribution of Compounds in the Human
Metabolome
7
History of Metabolomics

• Machinery to detect metabolites have existed
  since the late 1960s
• First paper appeared in 1971 (Robinson and
  Pauling)
• First paper involving metabolomics came about
  in the late 1990s

8
Why Metabolomics can be promising

• Easy to use screening for disease
• Assist in identifying gene function
• Drug discovery
• Assessment of toxicity (especially liver
  toxicity) in new drugs.
• Nutrigenomics and diet strategies

9
Genomics,Proteomics and Metabolomics
10
The emerging science of Metabolomics
11
Metabolomics
12
Biochemical Profile Map to Metabolic Pathways
13
Data Collection and Measurement Issues

• To obtain data, a tissue sample is taken from a
  patient. Then
• The sample is prepped and put onto wells on a
  silicon plate.
• Each wells aliquot is subjected to gas and/or
  liquid chromatography.
• After separation, the sample goes to a mass
  spectrometer.

14
(No Transcript)
15
Data Collection and Measurement Issues

• The sample prep involves stabilizing the sample,
  adding spiked-in calibrants, and creating
  multiple aliquots (some are frozen) for QC
  purposes. This is roboticized.
• Sources of error in this step include
• within-subject variation
• within-tissue variation
• contamination by cleaning solvents
• calibrant uncertainty
• evaporation of volatiles.

16
Data Collection and Measurement Issues

• The result of this is a set of m/z ratios and
  timestamps for each ion, which can be viewed as a
  2-D histogram in the m/z x time plane.
• One now estimates the amount of each metabolite.
  This entails normalization, which also introduces
  error.
• The caveats pointed out in Baggerley et al.
  (Proteomics, 2003) apply.

17
Data Collection and Measurement Issues

• Baseline correction
• Alignment
• Estimating quantity of specific metabolites.

18
(No Transcript)
19
Data Collection and Measurement Issues

• Let z be the vector of raw data, and let x be the
  estimates. Then the measurement equation is
• G(z) x µ e
• where µ is the vector of unknown true values
  and e is decomposable into separate components.
• For metabolite i, the estimate Xi is
• gi(z) lnS wij ??sm(z) c(m,t)dm dt.

20
Data Collection and Measurement Issues

• The law of propagation of error (this is
  essentially the delta method) says that the
  variance in X is about
• Sni1 (?g /? zi)2 Varzi
• Si?k 2 (?g/?zi)(?g/?zk) Covzi, zk
• The weights depend upon the values of the spiked
  in calibrants, so this gets complicated.

21
Data Collection and Measurement Issues

• Cross-platform experiments are also crucial for
  medical use. This leads to key comparison
  designs. Here the same sample (or aliquots of a
  standard solution or sample) are sent to multiple
  labs. Each lab produces its spectrogram.
• It is impossible to decide which lab is best, but
  one can estimate how to adjust for interlab
  differences.

22
Data Collection and Measurement Issues

• The Mandel bundle-of-lines model is what we
  suggest for interlaboratory comparisons. This
  assumes
• Xik ai ßi ?k eik
• where Xik is the estimate at lab i for
  metabolite k, ?k is the unknown true quantity of
  metabolite k, and
• eik N(0,sik2).

23
Data Collection and Measurement Issues

• To solve the equations given values from the
  labs, one must impose constraints. A Bayesian
  can put priors on the laboratory coefficients and
  the error variance.
• Metabolomics needs a multivariate version, with
  models for the rates at which compounds
  volatilize.

24
(No Transcript)
25
(No Transcript)
26
Statistical issues

• Many missing values!!!
• Outliers
• Distribution of metabolites are not normally
  distributed
• nltp
• Correlated metabolites

27
Statistical Issues

• PCA or ICA
• Partial Least Squares
• Clustering
• Random Forest, SVM
• rSVD

28
Statistical issues

• Dealing with missing values
• Replacing missing values by 0s is not
  necessarily a good idea. Not truly 0.
• Minimum, half-min, uniform(0, minimum)
• Random forest imputation
• Observing conditional distribution (Dr. Young
  Truong at UNC)

29
Statistical Issues

• Prediction and Classification
• Partial least squares
• Random Forest
• SVM
• Neural networks

30
Statistical Issues

• Identifying relationships
• MDS
• Clustering
• rSVD (PowerMV from NISS)

31
ALS metabolomic data set

• We had abundance data on 317 metabolites from 63
  subjects. Of these, 32 were healthy, 22 had ALS
  but were not on medication, and 9 had ALS and
  were taking medication.
• The goal was to classify the two ALS groups and
  the healthy group.
• Here pgtn. Also, some abundances were below
  detectability.

32
ALS metabolomic data set

• Using the Breiman-Cutler code for Random Forests,
  the out-of-bag error rate was 7.94 29 of the
  ALS patients and 29 of the healthy patients were
  correctly classified.
• 20 of the 317 metabolites were important in the
  classification, and three were dominant.
• RF can detect outliers via proximity scores.
  There were four such.

33
ALS Metabolomic data set

• Several support vector machine approaches were
  tried on this data
• Linear SVM
• Polynomial SVM
• Gaussian SVM
• L1 SVM (Bradley and Mangasarian, 1998)
• SCAD SVM (Fan and Li, 2000)
• The SCAD SVM had the best loo error rate, 14.3.

34
ALS Metabolomic data set

• Robust SVD (Liu et al., 2003) is used to
  simultaneously cluster patients (rows) and
  metabolites (columns). Given the patient by
  metabolite matrix X, one writes
• Xik ri ck eik
• where ri and ck are row and column effects.
  Then one can sort the array by the effect
  magnitudes.

35
ALS metabolomic data set

• To do a rSVD use alternating L1 regression,
  without an intercept, to estimate the row and
  column effects. First fit the row effect as a
  function of the column effect, and then reverse.
  Robustness stems from not using OLS.
• Doing similar work on the residuals gives the
  second singular value solution.

36
(No Transcript)
37
NCI data set

• NCI 60 cell lines
• 9 cancer types breast, CNS, colon, melanoma,
  renal, leukemia, prostate, ovarian, lung
• GC-LS
• Melanoma vs CNS (8 cell lines for melanoma and 6
  cell lines for CNS)

38
Variable Importance using RF
39
Component 1 versus 2
40
Useful websites

• Deconvolution of peaks, software AMDIS
  (http//chemdata.nist.gov/massspc/amdis NIST,
  Gaithersburg, USA)
• Human Metabolome database (www.hmdb.ca)
• KEGG (www.genome.jp/kegg)
• http//www.niss.org/PowerMV/
• Many, many others

41
Concluding Remarks

• Many interesting statistical issues still need to
  be addressed.
• Measurement issues and interlaboratory
  differences need to be properly addressed.
• Statistical issues in analyzing metabolomic data
  still remain an interesting challenge.
• Metabolomics is an important part in
  understanding systems biology.