Simca p 11

All data were presented as means ± SD. Multivariate statistical analysis was performed using SIMCA-P 11.5 software (Umetrics, Umeå, Sweden). Principal component analysis (PCA) was used first in all samples to observe the general separation. Partial least-squares-discriminant analysis (PLS-DA) was used to discriminate metabolite patterns between the OGTT time points.
Statistical analysis was performed using SPSS 13.0 (SPSS, Inc, Chicago, IL, USA). Comparisons between HLP and healthy control were assessed with student’s t test for continuous variables. Within subject contrasts were used to compare values during the OGTT with zero-time values with the paired t test. Spearman correlation analysis was performed for the whole group using the percent change in metabolites from fasting to the 2-h sample and clinical parameters. Two-sided tests of significance were used, and a p value of less than 0.05 was considered to be statistically significant.

The spectra were phased, baseline-corrected, and referenced to TSP (at 0.0 ppm) as well as quantified and qualified using a commercial software package (Chenomx NMR suite 4.6, Chenomx, Inc., Edmonton, Alberta, Canada) with the Chenomx 500-MHz (pH 6–8) library. The spectra data were pre-processed using normalization and scaling to remove possible bias arising due to sample handling and sample variability. Normalization (by sum) was performed in order to minimize possible differences in concentration between samples. In addition, the spectral region from 5.0 to 4.7 ppm containing the residual water resonance was excluded, and all subsequent analysis used centred scaling.
After data pre-processing, a univariate statistical analysis (Student′s t-test) was used to verify the significance of difference in metabolite levels between the CHH dsRNA-injected group and the saline-injected group. p<0.05 was considered to indicate a statistically significant difference. These significantly changed metabolites were further analyzed by heuristic methods of dimension reduction (principal component analysis [PCA]) using commercially available software (SIMCA-P 11.5; Umetrics, Umeå, Sweden), and all subsequent analysis used centred scaling. The number of PCA components was calculated using an auto-fit model in SIMCA-P (Umetrics).

A variety of preprocessing methods for the spectroscopic data were compared to extract the useful information from noise, such as normalization, baseline, Savitzky-Golay smoothing, Savitzky-Golay smoothing plus first-order derivatives, Savitzky-Golay smoothing plus second-order derivatives, spectroscopic transformation, multiplicative scatter correction, standard normal variate transformation, and wavelet de-nosing of spectra. SIMCA P +11.5 (Umetrics AB, Umea, Sweden) and Unscrambler 9.7 (Camo software, Oslo, Norway) served as chemometric tools for data preprocessing.