1. Metabolomics Research & Consultancy CJ Alexander, CA Hackett & JW McNicol With collaborations below as indicated from: 1The James Hutton Institute, UK 2Teagasc, Crops Environment and Land Use Programme, Ireland Aarhus University, Denmark
Metabolomics is a rapidly developing field and scientists at The James Hutton Institute apply various technologies in plant breeding, quality & nutrition. BioSS provides consultancy support in design & analysis. We also investigate opportunities for further developments with new statistical techniques & data types.
Conventional Statistical Analysis
The statistical approach BioSS uses for the analysis of metabolomics datasets involves two strands:
Multivariate where a PCA is used to explore the larger sources of variation amongst the experimental samples and identify the metabolites responsible for these differences
Univariate where ANOVA is performed individually on each metabolite. A False Discovery Rate calculation allows the specific limit for significance to be chosen for those metabolites showing a significant treatment effects. A Hierarchical Cluster Analysis then illustrates the relationships among this subset
Developments in Statistical Analysis
Techniques such as ANOVA Simultaneous Components Analysis (ASCA) may provide an improvement on our conventional approach
Essentially, for each ANOVA model term (main effects & interactions), ASCA performs a PCA on the table of effects.
Example: potato tuber life cycle. A single factor experiment with 11 levels through life cycle stage
ASCA biplot below shows variation of metabolites (marked ‘o’) and the vectors show the factor level loadings. PC1 distinguished metabolites which are increasing from those which are decreasing over the life cycle
Linking Metabolomic & Transcriptomic Datasets
LVT Shepherd1, PE Hedley1, JA Morris1, D McRae1 & JA Sungurtas1 HV Davies1
In a potato tuber bruising experiment, both microarray & metabolomic responses were available for the same samples in the same designed experiment. The treatment factors were: Genotypes: Storage Times: 3 durations
Which genes affect which metabolites?
ANOVA performed separately for each of the genes & metabolites
Eight possible groupings of treatment factor significances e.g. one grouping would be those responses which were significant for Genotype & Storage Time but no significant interaction
Partition the genes & metabolites into their respective significance groups
For each metabolite in a significance group, predict the response using a regression tree with all the genes
Prune the regression tree
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PC1 (31%)
mQTL Analysis A Foito1, S Byrne2,3, D Stewart1 & S Barth2
BioSS have analysed mQTL experiments with JHI collaborators on several crops (blackcurrant, raspberry, oats & potatoes). Shown here is a ryegrass mapping population experiment consisting of:
Parent plants (P_m, P_f); F1 plants; F2 offspring
Phase 1: two replicate blocks were grown in the field
Phase 2: metabolites measured in laboratory with GCMS Non Polar analysis.
Such multi-phase experiments are becoming more common and benefit from BioSS input for the careful design and statistical analysis required
For the metabolite Octacosanol, a graph of the LOD score profile identified a large QTL on linkage group 4
Consistent differences can be seen between the recessive QTL genotype b and the dominant genotype d for each batch
Linkage Group 4
LOD score profile
Means for batch number at different
levels of D561623: Generation F2
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Octacosanol
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Future Work
Pre-processing of spectra
Evaluation of other multivariate methods such as Bayesian Independent Component Analysis
Utilising metabolic pathway databases (such as KEGG)
LOD Score 10
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Average sed
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The potato bruising experiment received funding from the United States Department of Agriculture
The ryegrass experiment received funding from Irish Department of Agriculture, Fisheries and Marine
D d