Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

Statnote 29:discriminant analysis

Discriminant analysis (also known as discriminant function analysis or multiple discriminant analysis) is a multivariate statistical method of testing the degree to which two or more populations may overlap with each other. It was devised independently by several statisticians including Fisher, Mahalanobis, and Hotelling ). The technique has several possible applications in Microbiology. First, in a clinical microbiological setting, if two different infectious diseases were defined by a number of clinical and pathological variables, it may be useful to decide which measurements were the most effective at distinguishing between the two diseases. Second, in an environmental microbiological setting, the technique could be used to study the relationships between different populations, e.g., to what extent do the properties of soils in which the bacterium Azotobacter is found differ from those in which it is absent? Third, the method can be used as a multivariate ‘t’ test , i.e., given a number of related measurements on two groups, the analysis can provide a single test of the hypothesis that the two populations have the same means for all the variables studied. This statnote describes one of the most popular applications of discriminant analysis in identifying the descriptive variables that can distinguish between two populations.

Molecular analysis of methicillin-resistant Staphylococcus aureus reveals an absence of plasmid DNA in multidrug-resistant isolates

The number, diversity and restriction enzyme fragmentation patterns of plasmids harboured by 44 multidrug-resistant hospital-acquired methicillin-resistant Staphylococcus aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK) were compared. In addition, pulsed-field gel electrophoresis (PFGE) type following SmaI chromosomal digest and SCCmec element type assignment were ascertained for each isolate. All MR-HA-MRSA and MR-CA-MRSA isolates possessed the type II SCCmec, harboured no plasmid DNA and belonged to one of five PFGE types. Forty-three out of 50 HA-MRSA isolates and all 34 CA-MRSA isolates possessed the type IV SCCmec and all but 10 of the type IV HA-MRSA isolates and nine CA-MRSA isolates carried one or two plasmids. The 19 non-multidrug-resistant isolates (NMR) that did not harbour plasmids were only resistant to methicillin whereas all the NMR isolates harbouring at least one plasmid were resistant to at least one additional antibiotic. We conclude that although plasmid carriage plays an important role in antibiotic resistance, especially in NMR-HA-MRSA and CA-MRSA, the multidrug resistance phenotype from HA-MRSA is not associated with increased plasmid carriage and indeed is characterised by an absence of plasmid DNA. © 2005 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

Analysis of clinical isolates of Propionibacterium acnes by optimised RAPD

Random amplification of polymorphic DNA (RAPD) was evaluated as a genotypic method for typing clinical strains of Propionibacterium acnes. RAPD can suffer from problems of reproducibility if parameters are not standardised. In this study the reaction conditions were optimised by adjusting template DNA concentration and buffer constituents. All isolates were typeable using the optimised RAPD protocol which was found to be highly discriminatory (Simpson's diversity index, 0.98) and reproducible. Typing of P. acnes by optimised RAPD is an invaluable tool for the epidemiological investigation of P. acnes for which no other widely accepted method currently exists. © 2003 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

Statistical analysis in microbiology:statnotes

This book is aimed primarily at microbiologists who are undertaking research and who require a basic knowledge of statistics to analyse their experimental data. Computer software employing a wide range of data analysis methods is widely available to experimental scientists. The availability of this software, however, makes it essential that investigators understand the basic principles of statistics. Statistical analysis of data can be complex with many different methods of approach, each of which applies in a particular experimental circumstance. Hence, it is possible to apply an incorrect statistical method to data and to draw the wrong conclusions from an experiment. The purpose of this book, which has its origin in a series of articles published in the Society for Applied Microbiology journal ‘The Microbiologist’, is an attempt to present the basic logic of statistics as clearly as possible and therefore, to dispel some of the myths that often surround the subject. The 28 ‘Statnotes’ deal with various topics that are likely to be encountered, including the nature of variables, the comparison of means of two or more groups, non-parametric statistics, analysis of variance, correlating variables, and more complex methods such as multiple linear regression and principal components analysis. In each case, the relevant statistical method is illustrated with examples drawn from experiments in microbiological research. The text incorporates a glossary of the most commonly used statistical terms and there are two appendices designed to aid the investigator in the selection of the most appropriate test.

Representational difference analysis:Critical appraisal and method development for the identification of unique DNA sequences from prokaryotes

Representational difference analysis (RDA) has great potential for preferential amplification of unique but uncharacterised DNA sequences present in one source such as a whole genome, but absent from a related genome or other complex population of sequences. While a few examples of its successful exploitation have been published, the method has not been well dissected and robust, detailed published protocols are lacking. Here we examine the method in detail, suggest improvements and provide a protocol that has yielded key unique sequences from a pathogenic bacterial genome. © 2003 Elsevier Science B.V. All rights reserved.