Biostatistics

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures.

Novel post-genomics experiments such as metabolomics provide datasets that are highly multivariate and often reflect an underlying experimental design, developed with a specific experimental question in mind. ANOVA-simultaneous component analysis (ASCA) can be used for the analysis of multivariate data obtained from an experimental design instead of the widely used principal component analysis (PCA). This increases the interpretability of the model in terms of the experimental question.

BACKGROUND

INTRODUCTION:

Plant metabolomics experiments yield large amounts of data, too much to be interpretable by eye. Multivariate data analyses are therefore essential to extract and visualise the information of interest.

OBJECTIVE:

Because multivariate statistical methods may be remote from the expertise of many scientists working in the metabolomics field, this overview provides a step-by-step description of a multivariate data analysis, starting from the experiment and ending with the figures appearing in scientific journals.

The detection of hormone secretion episodes is important for understanding normal and abnormal endocrine functioning, but pulse identification from hormones measured with short interval sampling is challenging. Furthermore, to obtain useable results, the model underlying hormone secretion and clearance must be augmented with restrictions based on biologically acceptable assumptions. Here, using the assumption that there are only a few time points at which a hormone is secreted, we used a modern penalized nonlinear least-squares setup to select the number of secretion events.

BACKGROUND: