Multivariate analyses (principal components analysis, PCA, and hierarchical cluster analysis, HCA) were performed in either Aabel (Gigawiz, Tulsa, OK, USA) or JMP (SAS UK, Marlow, UK) as appropriate.
Multivariate analyses using principal component analysis and hierarchical cluster analysis revealed natural and anthropogenic activities as sources of heavy metal contamination in the borehole water samples.
Spectra were then binned into regions of 0.01 p.p.m., normalised to the total spectral area and the region between 2.6 and 4.0 p.p.m. subjected to multivariate analyses using principal component analysis (PCA).
A matrix of each OTU table representing relative abundance (raw data) was imported into PAST v3.06 (ref. 50) for multivariate statistical analyses (principal component analysis, canonical correspondence analysis) and Pearson correlations.
Exploratory analyses (scatter plots and box plots) and multivariate statistical analyses (principal component analysis, PCA, and correlation analysis, CA) techniques were used to assess and discriminate sources of variations in the dataset.
We characterized differences in the metabolomic profiles of the three groups using multivariate statistical analyses: principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA).
Examples are given in Additional file 7. Bivariate and multivariate analyses revealed three principal correlates of correct patient management (defined as concordance in the three principal domains of staging, co-trimoxazole management, and ART management).
First, the indicators for the four constructs of URE MAPS went through two multivariate analyses: non-linear principal component analysis (NLPCA) and Cronbach's coefficient alpha.
After little or no change is seen due to the rotation and mean estimation steps, the process is deemed complete and the superimposed coordinates for each individual can be used as commensurate variables that describe individual shape and can be subjected to multivariate analyses, such as principal components analysis used here.
In contrast to classical analysis of NMR data which involves time-consuming assignment prior to data evaluation, unsupervised multivariate analyses such as principal component analysis (PCA) [4] is extremely fast and can be applied to the data without manual pre-treatment.
To date, metabolomics data have been limited to relatively standard statistical techniques including univariate analyses and multivariate analyses such as principal component analysis [ 1, 2].
