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.

Neuropathological changes in ten cases of neuronal intermediate filament inclusion disease (NIFID):A study using α-internexin immunohistochemistry and principal components analysis (PCA)

Ten cases of neuronal intermediate filament inclusion disease (NIFID) were studied quantitatively. The α-internexin positive neurofilament inclusions (NI) were most abundant in the motor cortex and CA sectors of the hippocampus. The densities of the NI and the swollen achromatic neurons (SN) were similar in laminae II/III and V/VI but glial cell density was greater in V/VI. The density of the NI was positively correlated with the SN and the glial cells. Principal components analysis (PCA) suggested that PC1 was associated with variation in neuronal loss in the frontal/temporal lobes and PC2 with neuronal loss in the frontal lobe and NI density in the parahippocampal gyrus. The data suggest: 1) frontal and temporal lobe degeneration in NIFID is associated with the widespread formation of NI and SN, 2) NI and SN affect cortical laminae II/III and V/VI, 3) the NI and SN affect closely related neuronal populations, and 4) variations in neuronal loss and in the density of NI were the most important sources of pathological heterogeneity. © Springer-Verlag 2005.

Statnote 27: Principal components analysis (PCA)

In Statnotes 24 and 25, multiple linear regression, a statistical method that examines the relationship between a single dependent variable (Y) and two or more independent variables (X), was described. The principle objective of such an analysis was to determine which of the X variables had a significant influence on Y and to construct an equation that predicts Y from the X variables. ‘Principal components analysis’ (PCA) and ‘factor analysis’ (FA) are also methods of examining the relationships between different variables but they differ from multiple regression in that no distinction is made between the dependent and independent variables, all variables being essentially treated the same. Originally, PCA and FA were regarded as distinct methods but in recent times they have been combined into a single analysis, PCA often being the first stage of a FA. The basic objective of a PCA/FA is to examine the relationships between the variables or the ‘structure’ of the variables and to determine whether these relationships can be explained by a smaller number of ‘factors’. This statnote describes the use of PCA/FA in the analysis of the differences between the DNA profiles of different MRSA strains introduced in Statnote 26.

Data analysis methods in optometry Part 8: Principal components analysis (PCA) and factor analysis (FA)

PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.

Neuropathological heterogeneity in Alzheimer's disease:A study of 80 cases using principal components analysis

Three hypotheses have been proposed to explain neuropathological heterogeneity in Alzheimer's disease (AD): the presence of distinct subtypes ('subtype hypothesis'), variation in the stage of the disease ('phase hypothesis') and variation in the origin and progression of the disease ('compensation hypothesis'). To test these hypotheses, variation in the distribution and severity of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in 80 cases of AD using principal components analysis (PCA). Principal components analysis using the cases as variables (Q-type analysis) suggested that individual differences between patients were continuously distributed rather than the cases being clustered into distinct subtypes. In addition, PCA using the abundances of SP and NFT as variables (R-type analysis) suggested that variations in the presence and abundance of lesions in the frontal and occipital lobes, the cingulate gyrus and the posterior parahippocampal gyrus were the most important sources of heterogeneity consistent with the presence of different stages of the disease. In addition, in a subgroup of patients, individual differences were related to apolipoprotein E (ApoE) genotype, the presence and severity of SP in the frontal and occipital cortex being significantly increased in patients expressing apolipoprotein (Apo)E allele ε4. It was concluded that some of the neuropathological heterogeneity in our AD cases may be consistent with the 'phase hypothesis'. A major factor determining this variation in late-onset cases was ApoE genotype with accelerated rates of spread of the pathology in patients expressing allele ε4.

Characterisation of Aeromonas strains and species by pulsed field gel electrophoresis and principal components analysis

Aeromonas genomes were investigated by restriction digesting chromosomal DNA with the endonuclease XbaI, separation of restriction fragments by pulsed field gel electrophoresis (PFGE) and principal components analysis (PCA) of resulting separation patterns. A. salmonicida salmonicida were unique amongst the isolates investigated. Separation profiles of these isolates were similar and all characterised by a distinct absence of bands in the 250kb region. Principal components analysis represented these strains as a clearly defined homogeneous group separated by insignificant Euclidian distances. However, A. salmonicida achromogenes isolates in common with those of A. hydrophila and A. sobria were shown by principal components analysis to be more heterogeneous in nature. Fragments from these isolates were more uniform in size distribution but as demonstrated by the Euclidian distances attained through PCA potentially characteristic of each strain. Furthermore passaging of Aeromonas isolates through an appropriate host did not greatly modify fragment separation profiles, indicative of the genomic stability of test aeromonads and the potential of restriction digesting/PFGE/PCA in Aeromonas typing.

The relationship between senile plaques, neurofibrillary tangles and amyloid (A4) deposits in Alzheimer’s disease: A Principal Components Analysis

A Principal Components Analysis (PCA) was carried out on the density of lesions revealed by different stains in a total of 47 brain regions from six elderly patients with Alzheimer’s disease (AD). The aim was to determine the relationships between the density of senile plaques (SP) revealed by the Glees and Gallyas stains and A4 deposits and between the plaques and neurofibrillary tangles (NFT) in the same brain region. The analysis indicated that the populations of plaques revealed by the Glees and Gallyas stains were closely related to the A4 protein deposits but none of the lesions were related to NFT. The data suggest: 1) that neocortical regions differ from the hippocampus in the relative development of A4 and NFT; the former having more A4 deposits and the latter more NFT and 2) that the processes that lead to the formation of SP and NFT occur independently of each other in the same brain region.

Neuropathological heterogeneity in frontotemporal lobar degeneration with TDP-43 proteinopathy: a quantitative study of 94 cases using principal components analysis

Studies suggest that frontotemporal lobar degeneration with transactive response (TAR) DNA-binding protein of 43kDa (TDP-43) proteinopathy (FTLD-TDP) is heterogeneous with division into four or five subtypes. To determine the degree of heterogeneity and the validity of the subtypes, we studied neuropathological variation within the frontal and temporal lobes of 94 cases of FTLD-TDP using quantitative estimates of density and principal components analysis (PCA). A PCA based on the density of TDP-43 immunoreactive neuronal cytoplasmic inclusions (NCI), oligodendroglial inclusions (GI), neuronal intranuclear inclusions (NII), and dystrophic neurites (DN), surviving neurons, enlarged neurons (EN), and vacuolation suggested that cases were not segregated into distinct subtypes. Variation in the density of the vacuoles was the greatest source of variation between cases. A PCA based on TDP-43 pathology alone suggested that cases of FTLD-TDP with progranulin (GRN) mutation segregated to some degree. The pathological phenotype of all four subtypes overlapped but subtypes 1 and 4 were the most distinctive. Cases with coexisting motor neuron disease (MND) or hippocampal sclerosis (HS) also appeared to segregate to some extent. We suggest: 1) pathological variation in FTLD-TDP is best described as a ‘continuum’ without clearly distinct subtypes, 2) vacuolation was the single greatest source of variation and reflects the ‘stage’ of the disease, and 3) within the FTLD-TDP ‘continuum’ cases with GRN mutation and with coexisting MND or HS may have a more distinctive pathology.

β-amyloid (Aβ) deposition in cognitively normal brain, dementia with Lewy bodies, and Alzheimer's disease:a study using principal components analysis

The densities of diffuse, primitive, and classic ß-amyloid (Aß) deposits were studied in the temporal lobe in cognitively normal brain, dementia with Lewy bodies (DLB), familial Alzheimer’s disease (FAD), and sporadic AD (SAD). Principal components analysis (PCA) was used to determine whether there were distinct differences between groups or whether Aß pathology was more continuously distributed from group to group. Three principal components (PC) were extracted from the data accounting for 56% of the total variance. Plots of cases in relation to the PC did not result in distinct groups but suggested overlap in Aß deposition between the groups. In addition, there were linear correlations between the densities of Aß deposits and the distribution of the cases along the PC in specific brain regions suggesting continuous variation from group to group. PC1 was associated with the degree of maturation of Aß deposits, PC2 with differences between FAD and SAD, and PC3 with the degree of spread of Aß pathology into the hippocampus. Apolipoprotein E (APOE) genotype was not associated with variation in Aß deposition between cases. PCA may be a useful method of studying the pathological interface between closely related neurodegenerative disorders.

Non-linear principal components analysis : an alternative method for finding patterns in environmental data

The main purpose of this article is to gain an insight into the relationships between variables describing the environmental conditions of the Far Northern section of the Great Barrier Reef, Australia. Several of the variables describing these conditions had different measurement levels and often they had non-linear relationships. Using non-linear principal component analysis, it was possible to acquire an insight into these relationships. Furthermore, three geographical areas with unique environmental characteristics could be identified.

Forest soil short term response to prescribed fire: an approach by Principal Components Analysis

In the current context of serious climate changes, where the increase of the frequency of some extreme events occurrence can enhance the rate of periods prone to high intensity forest fires, the National Forest Authority often implements, in several Portuguese forest areas, a regular set of measures in order to control the amount of fuel mass availability (PNDFCI, 2008). In the present work we’ll present a preliminary analysis concerning the assessment of the consequences given by the implementation of prescribed fire measures to control the amount of fuel mass in soil recovery, in particular in terms of its water retention capacity, its organic matter content, pH and content of iron. This work is included in a larger study (Meira-Castro, 2009(a); Meira-Castro, 2009(b)). According to the established praxis on the data collection, embodied in multidimensional matrices of n columns (variables in analysis) by p lines (sampled areas at different depths), and also considering the quantitative data nature present in this study, we’ve chosen a methodological approach that considers the multivariate statistical analysis, in particular, the Principal Component Analysis (PCA ) (Góis, 2004). The experiments were carried out in a soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, NW Portugal, who was able to maintain itself intact from prescribed burnings from four years and was submit to prescribed fire in March 2008. The soils samples were collected from five different plots at six different time periods. The methodological option that was adopted have allowed us to identify the most relevant relational structures inside the n variables, the p samples and in two sets at the same time (Garcia-Pereira, 1990). Consequently, and in addition to the traditional outputs produced from the PCA, we have analyzed the influence of both sampling depths and geomorphological environments in the behavior of all variables involved.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Representation and analysis of soccer players' actions using principal components

This paper proposes the use of the Principal Components Analysis (PCA) method to represent and to analyse soccer players' actions distribution in the pitch. The seven games of the Brazilian National Team during the 2002 World Cup were analysed. The player's position actions were measured from videotapes in a computer interface. The results were: a) the graphical representation, given by two orthogonal segments in the two directions of maximal variability and centred at the mean of each player's actions position; b) the eccentricity measurement, given by the variability ratio and c) the actions zone area, given by variability product. The results showed that the individual characteristics of acting were well represented by the PCA, allowing comparisons among games and providing insights related to the tactical organisation of the team.

Non-linear principal components analysis: an alternative method for finding patterns in environmental data

The main purpose of this article is to gain an insight into the relationships between variables describing the environmental conditions of the Far Northern section of the Great Barrier Reef, Australia, Several of the variables describing these conditions had different measurement levels and often they had non-linear relationships. Using non-linear principal component analysis, it was possible to acquire an insight into these relationships. Furthermore. three geographical areas with unique environmental characteristics could be identified. Copyright (c) 2005 John Wiley & Sons, Ltd.

The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.