Systems Biology (Methods in Molecular Biology)

(i.e., selection of exclusion regions, binning, and

normalization).

5. Mean centered the data: subtract the mean value of each vari-
   able from the original data of that bin.

3.4 Statistical
Analysis of NMR Data

1. Reduce the data by using Principal Component Analysis. This
   process assigns to each sample a score relative to each extracted
   component (Principal Component, PC). The extracted com-
   ponents are independent of each other by construction, thus
   they are non-overlapping features of the studied system. Use
   the component scores to plot PC maps of the samples which
   best provide an indication of the differences between the classes
   (healthy or disease groups) in terms of metabolic similarity.


2. Carry out separate inferential statistics (t-test) on the different
   component scores, so as to check for the statistical significance
   of the between groups differences.


3. Compare the metabolic profiles and the clinical features of each
   patient by Pearson’s correlation and/or ANOVA test, having as
   dependent variables the components and as regressors (sources
   of variation) potential modulating or confounding factors.


4. After having verified the absence of potentially confounding
   factors on the PCs, apply a linear discriminant analysis (LDA)
   to the components so as to develop a predictive model for the
   classification of patients in healthy or disease groups.


5. In the case of statistically significant effects of confounding
   factors on discriminant components, correct (covariance analy-
   sis or partial correlation analysis) for the effect of the above-
   mentioned factors. This procedure will allow estimating the
   actual degree of association between DA-based membership
   class probability and clinical status.

We investigated the NMR data by using Principal Component

Analysis (PCA) carried out on samples from young patients with

cystic fibrosis (CF) and healthy children. Five components are

sufficient to explain the 40% of the variance in the metabolic data.

The score plot in Fig.1-NMR shows a clear separation between the

CF and healthy children on the PC1 (p¼0.001 byt-test) and PC4

(p<0.0001 byt-test).

In this study, since the metabolic status of the CF patients could

be influenced by several variables such as age, gender, and antibiotic

and probiotic assumption, we decided to assess whether any of

these factors could influence the separation between CF patients

and healthy children. To address age and gender as potential con-

founding factors, the metabolic profiles and the clinical features of

each child were compared by Pearson’s correlation, while for asses-

sing antibiotic and probiotic assumption variables, the metabolic

332 Luca Casadei et al.