The document discusses using hierarchical cluster analysis (HCA) to evaluate metabolomic sample processing methods. It describes two goals: 1) Use HCA to cluster samples based on raw data similarities and correlations to determine the impact of extraction and treatment methods on data variance. Extraction had the greatest effect, with ACN:/IPA/water and MeOH/CH3Cl/water samples most similar. 2) Use HCA to cluster metabolites based on z-scaled data and correlations to identify groups of related metabolites and evaluate the robustness of different correlation measures. Clusters extracted from the correlation-based dendrogram contained metabolites that shared biological functions.

1. Biology
Chemistry
Informatics
Evaluation of metabolomic
sample processing methods using
hierarchical cluster analysis
Cluster Analysis
Goal:
Use hierarchical cluster analysis (HCA) to evaluate
data variance structure
Topics:
1. Evaluate sample and variable similarities
2. Identify the effect of data transformation,
distance and linkage methods on data
similarities

2. Clustering data
Biology
Chemistry
Informatics
Cluster Analysis
Goal:
Use HCA to cluster samples (Use DATA: Pumpkin data 1.csv)
Visualize:
1. Sample (row) raw similarities as a heat map
2. Annotate heatmap with extraction and treatment type
3. Select cluster distance and linkage method to cluster the samples
4. Determine the effect of data transformations on the cluster structure
(view as a dendrogram)
Exercises:
1. What factor, extraction or treatment, has the greatest contribution to
the data variance structure?
2. Describe the effect of clustering raw data or sample correlations

3. Biology
Chemistry
Raw data matrix visualized as
a heatmap
samples
variables
Cluster Analysis
Informatics

4. Raw data matrix organized by HCA
Biology
Chemistry
Informatics
Cluster Analysis
•ACN:/IPA/water|fresh and
MeOH/CH3Cl/water|dried
display distinct patterns in
metabolites which are most
similar to each other
•Sample similarities are linked
to metabolite magnitudes

5. Biology
Chemistry
Clustering based on sample
correlations (spearman)
Informatics
Cluster Analysis
•100% MeOH/fresh is the
most dissimilar protocol from
all others
•ACN:/IPA/water and
MeOH/CH3Cl/water are most
similar to each other
•Sample similarities are
decoupled from metabolite
magnitudes

6. Clustering metabolites
Biology
Chemistry
Informatics
Goal 2: Use HCA to evaluate metabolite similarities
Cluster Analysis
Visualize:
1.Z-scaled and correlation based variable clustering
2.Use a dendrogram to extract variable clusters
3.Select two variables from the same cluster and visualize their
correlation
Exercise:
1.Do the clustered variables share biological functions?
2.Which type of correlation is most robust to outliers?
3.Are the correlations for the visualized variable independent of
extraction/treatment?

7. Z-scaled variable clusters
Biology
Chemistry
Cluster Analysis
Informatics

8. Correlation based variable clusters
Biology
Chemistry
Cluster Analysis
Informatics

9. Biology
Chemistry
Extraction of clusters of
correlated variables
Informatics
less similar
most similar cluster
Cluster Analysis
lowest common
branch height
more similar

10. Biology
Chemistry
Cluster Analysis
Informatics
Correlation among cluster
members (4)