Functional principal component data analysis: a new method for analysing microbial community fingerprints.

A common approach to molecular characterisation of microbial communities in natural environments is the amplification of small subunit (SSU) rRNA genes or genes encoding enzymes essential for a particular ecosystem function. A range of 'fingerprinting' techniques are available for the analysis of amplification products of both types of gene enabling quantitative or semi-quantitative analysis of relative abundances of different community members, and facilitating analysis of communities from large numbers of samples, including replicates. Statistical models that have been applied in this context suffer from a number of unavoidable limitations, including lack of distinction between closely adjacent bands or peaks, particularly when these differ significantly in intensity or size. Current approaches to the analysis of banding structures derived from gels are typically based on standard multivariate analysis methods such as principal component analysis (PCA) which do not consider structure of DGGE gels but treat the intensity of each band as independent from the other bands, ignoring local neighbourhood structures. This paper assesses whether a new statistical analytical technique, based on functional data analysis (FDA) methods, improves the discriminatory ability of molecular fingerprinting techniques. The approach regards band intensities as a mathematical function of the location on the gel and explicitly includes neighbourhood structure in the analysis. A simulation study clearly reveals the weaknesses of the standard PCA approach as opposed to the FDA approach, which is then used to analyse experimental DGGE data.