Univariate analysis T-test
For two-group data, the number of data was applied on 62 metabolites in urine samples. These compounds could be identified using correlation analysis which uses chemical techniques to determine each component in the urine. Correlation analysis depends mainly on comparison between reference components and the components of the sample, if they are identical they will be shown in red but if they are not identical or not correlated they will be colored in blue, the intense of color explains the correlation degree between the sample and reference. MetaboAnalyst provides t-test, it supports both unpaired and paired analyses, and the univariate analyses provide a preliminary overview about features that are potentially significant in discriminating the conditions under study. For paired fold change analysis, the algorithm first counts the total number of pairs with fold changes that are consistently above/below the specified Fold Change threshold for each variable [3].
Figure 1 shows the important features identified by T-tests. Table 1 shows the details of these features;
Figure 1: Computed tomography analysis and lumbar skeletal muscles measurement which are lighted with red, showing correlated variables between skeletal muscle index according to height, weight body mass index and total muscle cross sectional area in cubic centimeters.
| Table 1: Various applications of metabolomics in transplantation of liver, kidney and heart. | |||
| Organ | Condition | Metabolite(s) Increased | Metabolite(s) Decreased |
| Kidney (Human) | Chronic Renal Failure | TMAO (Trimethylamine N-oxide), Dimethylamine, Urea, Creatinine (serum) | |
| Liver (Human) | Ischemia | Methylarginine Dimethylarginine (liver catheter) | |
| Kidney (Human) | Graft Dysfunction | TMAO, Dimetheylamine Lactate, Acetate, Succinate, Glycine, Alanine, (urine) | |
| Liver (Human) | Graft Dysfunction | Glutamine (serum & urine) | Urea (urine) |
| Liver (Human) | Post-transplant | Phosphatidylcholine (bile) | |
| Kidney (Human) | Graft Dysfunction CsA Toxicity | TMAO, Alanine, Lactate, Citrate (urine & serum) | |
| Heart (Human) | Rejection | Nitrate (urine) | |
| Kidney (Human) | Acute Rejection | Nitrates, Nitrites, Nitric oxide metabolites (urine) | |
Figure 2: Important features selected by t-tests with threshold 0.05. The red circles represent features above the threshold. Note the p-values are transformed by -log10 so that the more significant features (with smaller p-values) will be plotted higher on the graph. It shows that 2 compounds are only significant which are uracil and isoleucine.
The purpose of fold change is to compare absolute value changes between two group means. Therefore, the data before column normalization will be used instead. Also note, the result is plotted in log2 scale, so that same fold change (up/down regulated) will have the same distance to the zero baselines [4].
As shown in table 2, t-test explains the result of the two most significant compounds which are uracil and isoleucine to distinct their p-values, -log10 (p) and False Discovery Rate.
| Table 2: Important features identified by t-tests shows result of the only 2 significant compounds uracil and isoleucine using t-test, p-value, -log10 (p) and False Discovery Rate. | |||||
| Compound Name | t-test | p-value | -log 10 (p) | FDR | |
| 1 | Uracil | -3.7185 | 0.0003842 | 3.4154 | 0.024204 |
| 2 | Isoleucine | -3.3838 | 0.0011396 | 2.9432 | 0.035898 |
Correlation analysis
Correlation analysis can be used to visualize the overall correlations between different features. It can also be used to identify which features are correlated with a feature of interest. Correlation analysis can also be used to identify if certain features show particular patterns under different conditions, we need to define a pattern in the form of a series of hyphenated numbers [5].
Principal component analysis
PCA is an unsupervised method aiming to find the directions that best explain the variance in a data set without referring to class labels. The data are summarized into much fewer variables called scores which are weighted average of the original variables, figure 3 is pairwise score plots providing an overview of the various separation patterns among the most significant PCs, figure 4 is the scree plot showing the variances explained by the selected PCs, figure 5 shows the 2-D scores plot between selected PCs, figure 6 shows the 3-D scores plot between selected PCs, figure 7 shows the loadings plot between the selected PCs, figure 8 shows the biplot between the selected PCs.
Figure 3: Correlation analysis of the urine compounds shows negative correlations between most compounds.
Figure 4: Pairwise score plots between the cachexia group (red) and control group (green), the explained variance of each PC is shown in the corresponding diagonal cell.
Figure 5: Scree plot shows the variance explained by PCs. The green line on top shows the accumulated variance explained; the blue line underneath shows the variance explained by individual PC for each compound.
Figure 6: Scores plot between the selected PCs of cachexic group and control group. The explained variances are shown in brackets.
Figure 7: 3D score plot between the cachexic and control group showing the highest score for cachexic group.
As shown in figure below, PCs shows the score blot between cachexic group (red circles) and control group (green circle), PC1 has a score reaches 10.3% while PC2 shows a score reaches 9.7%.
If we wanted to make confirmation test we can use 3D score plot which compares between cachexic group (red triangles) and control group (green triangles) we will find similar results of the previous score plot test with a third score of the third dimension PC3 reaches 6.9%, we find that cachexic group has the highest score in the diagram.
Heatmap clustering
In (agglomerative) hierarchical cluster analysis, each sample begins as a separate cluster and the algorithm proceeds to combine them until all samples belong to one cluster. Two parameters need to be considered when performing hierarchical clustering. The first one is similarity measure, euclidean distance, pearson’s correlation, spearman’s rank correlation. The other parameter is clustering algorithms, including average linkage (clustering uses the centroids of the observations), complete linkage (clustering uses the farthest pair of observations between the two groups), single linkage (clustering uses the closest pair of observations) and Ward’s linkage (clustering to minimize the sum of squares of any two clusters). Heatmap is often presented as a visual aid, figure 9 shows the clustering result in the form of a dendrogram [6].
Figure 8: Loadings plot for the selected principal components showing the highest loading plot for sucrose and the lowest plot for 2-oxoglutarate.
Figure 9: PCA biplot between the principle components showing the same results of figure 7. You may want to test different centering and scaling normalization methods for the biplot to be displayed properly.
According to these results, we can interpret from the amount of sucrose and glucose that there is a close correlation between cachexia and getting diabetes because the amount of sugar should be not significant in normal people or at least below sugar threshold. As shown in figure 10, clustering result shown as heatmap (distance measure using Euclidean, and clustering algorithm using ward. D to see that red for the highest concentration and blue for the lowest concentrations the highest expression of compound are glucose in cachexic patients and lactose were the least one is creatinine in cachexic patients.
Figure 10: Clustering result shown as heatmap (distance measure using euclidean and clustering algorithm using ward. D to see that red for the highest concentration and blue for the lowest concentrations the highest expression of compound are glucose in cachexic patients and lactose were the least one is creatinine in cachexic patients.
