Spotlight
on
Chemometrics’
the
‘Theory
of
Dr. Jeroen Jansen, jj.jansen@science.ru.nl
Dr. Lionel Blanchet, l.blanchet@science.ru.nl
Prof. Dr. Lutgarde Buydens, l.buydens@science.ru.nl
The Laboratory of Analytical Chemistry (LAC) at the Radboud University Nijmegen, headed by Prof.
Lutgarde Buydens, is one of the oldest chemometrics groups in the world. Chemometrics is the
research field interested in the analysis of complex chemical data. Omics data are therefore logically
of particular chemometric interest and a key element in our research portfolio. Conversely, scientific
and technological developments in metabolomics require ever-more advanced statistical methods
that answer the very complex biological research questions. Such synergy between both fields may
induce a next generation of thought-provoking challenges that stretch current methods to the limits
and thereby spark the development of new approaches.
The field of chemometrics until recently relied heavily on two methods: Principal Component
Analysis to describe the chemistry of complex analytical measurements, and Partial Least Squares
Regression to predict specific properties from these measurements. Any literature search for
‘analysis of metabolomics data’ shows how the love for these methods flourishes on. However, the
LAC aims to ascend on these specific methods, to reach a strong theoretical—currently lacking—basis
for chemometrics. For this we started a public-private partnership, through the Netherlands ‘Top
Institute for COmprehensive Analytical Science and Technology’ (TI-COAST). In this project, called
‘Analysis of Large data sets By Enhanced Robust Techniques’ (ALBERT), several Dutch (bio)chemical
companies collaborate with chemometricians and statisticians from the Universities of Nijmegen and
Groningen to create a pipeline. In this line the information quality within any metabolomics (or
other chemical) dataset can be quantitatively evaluated, based on solid theoretical grounds (Figure
1). This then forms the basis for the pre-processing to remove identified artefacts and thereby
highlight the biologically relevant data. This can then be analysed by PCA or PLS, but potentially by
more advanced methods that are tailor-made to answer the biological question at hand. Also here
the theoretical basis is maintained, because each step in the pipeline is supported by two essential,
but often overlooked aspects: validation and visualisation. This pipeline will assure peer
metabolomics researchers they can both trust and understand results from the high-quality
chemometric models at the basis of a joint publication.
ALBERT: Towards Chemometric Theory
Step 1: Collect
data
Step 2:
Evaluate data
quality
Step 3:
Preprocess
data
Step 4: Analyse
data
Validation and visualization
Figure 1 Schematic depiction of the steps in the ALBERT pipeline
Our laboratory has applied this pipeline and their interconnection in several studies. For example, we
Intuitively expect that the concentration of a compound within a particular metabolic pathway
changes proportionally to the other metabolites in the same pathway. However, in reality these
concentration changes may follow much more complex behaviour: metabolic systems—biological
problems in general—are inherently non-linear. The chemometrician’s best friends (PCA and PLS) are
unable to detect these relations in such cases and more advanced methods are required. Kernel
transformations are particularly promising for this, for two reasons. Kernels can linearize almost any
type of relation, which are then open for exploration by PCA or PLS. Although kernel-based methods
are generally recognized as powerful, their use was severely hampered by the impossibility of
visualization of the biological information (metabolites) that underlie the model, in e.g. a ‘ biplot’.
Our group recently solved this problem, by implementing ‘pseudo-samples’ [1] that reconstruct the
role of each metabolite in the model, even for nonlinear biological differences. This aspect can even
be further extended to the linear and non-linear complementary information that GC-MS and NMR
contain on complex diseases like Multiple Sclerosis (Figure 2) [2].
Late stage
Early stage
Figure 2 Loading plot of pseudo-samples trajectories for selected NMR and GC-MS variables, adapted from reference [2].
Another forte of chemometrics that is very useful to metabolomics, is the strong analytical basis that
underlies it. This is not only reflected in analytical chemistry but also in the thought processes that lie
beneath. Chemometrics therefore forms an abstract plane, on which concepts from remote scientific
fields like psychology and marketing may cross-fertilize with those in systems biology. A prominent
field in these social science disciplines is the study of ‘individual differences’, that can be very useful
to create an ‘individualized metabolomic fingerprint’. In collaboration with the Biosystems Data
Analysis group at the University of Amsterdam, we have modelled the ecological response of a group
of plants to herbivory, specifically the heterogeneity in this response between otherwise completely
comparable plants and the emergence of sub-groups—chemotypes—in this response. For this we
adapted the Simultaneous Component Analysis with Individual Differences constraints (SCA-IND)
method [3], originally developed for assessing differences between sensory panel members for
product tests. This method provided a view on this heterogeneity that was both ecologically and
biochemically understandable (Figure 3). We believe this method can be applied broadly in
metabolomic studies. Specifically its application in Personalized Health and Medicine, which focuses
on the heterogeneity among patients, seems extremely promising. This is another very clear example
of how chemometrics may facilitate cross-fertilization between research fields, even within biology.
A
B
Individual Score
4
Loading
6
0
NEO
2
i
ii
2
4
6
GBC micromol/mg dry weight
1
7
Time(days)
14
PRO
RAPH
ALY
GNL
GNA
4OH
GBN
GBC
4MeOH
NAS
NEO
0
0
GBC
ii
i
Group Score
NEO micromol/mg dry weight
8
D
C
10
1
7
14
Time(days)
Glucosinolate
Figure 3 SCA-IND model on the ecological plant response. A. Raw data (circles: control plants, triangles: attacked plants;
grey: 1 day after attack, white: 7 days after, black: 14 days after. A negative relation between NEO and GBC clearly
emerges during the experiment i. plant with high level of Neoglucobrassicin (NEO), ii. High-Glucobrassicin Plant) B. this
emergence becomes clear in the ‘Group-level’ scores of attacked plants. C. Loadings show this heterogeneity mainly
associates with Glucobrassicin and Neoglucobrassicin D. the Individual-level scores of the model show distinct
Glucobrassicin and Neoglucobrassicin-responding plant groups, 14 days after attack—the emergence of ‘Response
Chemotypes’; plants i and ii from panel A are indicated.
References
1.
2.
3.
Postma, G.J., P.W.T. Krooshof, and L.M.C. Buydens, Opening the kernel of kernel partial least
squares and support vector machines. Analytica Chimica Acta, 2011. 705(1–2): p. 123-134.
Smolinska, A., et al., Interpretation and Visualization of Non-Linear Data Fusion in Kernel
Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis. PLoS ONE,
2012. 7(6): p. e38163.
Jansen, J., et al., Individual differences in metabolomics: individualised responses and
between-metabolite relationships. Metabolomics, 2012. 8(0): p. 94-104.