Figure 1.
OPLS-DA model results for HBV group in positive mode (A). 3-D of OPLS-DA model for HBV group (B). Loading plot of OPLS-DA of HBV in positive mode (C). Panel D shows the combination of S- and VIP-score plots constructed from the supervised OPLS analysis of urine (ESI+ mode). Ions with the highest abundance and correlation in the HBV group with respect to the controls are present on the upper far right hand quadrant, whereas ions with the lowest abundance and correlation in the HBV group with respect to the control group are residing in the lower far left hand quadrant.
Figure 2.
OPLS-DA model results for HBV group in negative mode (A). 3-D of OPLS-DA model for HBV group (B). Loading plot of OPLS-DA of HBV in positive mode (C). Panel D shows the combination of S- and VIP-score plots constructed from the supervised OPLS analysis of urine (ESI− mode).
Figure 3.
Heatmap visualization and hierarchical clustering analysis with MetaboAnalyst's data annotation tools constructed based on the differential metabolites of importance for the urine of HBV (A).
Rows: samples; Columns: metabolites; Color key indicates metabolite expression value, blue: Lowest, red: highest. The significance analysis for microarrays method used to select 4 marker metabolites (B). ROC curves for the diagnosis between controls and HBV patients using data from conventional clinical chemistry markers and the metabolic markers (C). The unsupervised clustering dendrogram of prediction and diagnostic potential of the marker metabolites, tested our approach using a second set of HBV (n = 10) patients and control subjects (n = 10) to be blindly selected (D).
Table 1.
Identification of urinary biomarkers in HBV cases.
Figure 4.
Construction of the altered phenylalanine metabolism pathways in human HBV disease.
The map was generated using the reference map by KEGG. CO represents entry number of compound.