Comparison of microarray preprocessing methods.

Data preprocessing in microarray technology is a crucial initial step before data analysis is performed. Many preprocessing methods have been proposed but none has proved to be ideal to date. Frequently, datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness, to inform further experimentation while data are yet restricted. In this paper, we compared the performance of four popular methods, namely MAS5, Li & Wong pmonly (LWPM), Li & Wong subtractMM (LWMM), and Robust Multichip Average (RMA). The comparison is based on the analysis carried out on sets of laboratory-generated data from the Bioinformatics Lab, National Institute of Cellular Biotechnology (NICB), Dublin City University, Ireland. These experiments were designed to examine the effect of Bromodeoxyuridine (5-bromo-2-deoxyuridine, BrdU) treatment in deep lamellar keratoplasty (DLKP) cells. The methodology employed is to assess dispersion across the replicates and analyze the false discovery rate. From the dispersion analysis, we found that variability is reduced more effectively by LWPM and RMA methods. From the false positive analysis, and for both parametric and nonparametric approaches, LWMM is found to perform best. Based on a complementary q-value analysis, LWMM approach again is the strongest candidate. The indications are that, while LWMM is marginally less effective than LWPM and RMA in terms of variance reduction, it has considerably improved discrimination overall.

Seizure characterisation using frequency-dependent multivariate dynamics.

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on the recent use of multivariate techniques to study the cross-correlation dynamics between electroencephalographic (EEG) channels. The maximum overlap discrete wavelet transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency-dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency-dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics.

Techniques for clustering gene expression data.

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognise these limitations and addresses them. As such, it provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for clustering methods considered.

Optimisation and parallelisation strategies for Monte Carlo simulation of HIV infection.

In recent years, the study of immune response behaviour through mathematical and computational models has attracted considerable efforts. The dynamics of key cell types, and their interactions, has been a primary focus in terms of building a picture of how the immune system responds to a threat. Discrete methods, based on lattice Monte-Carlo (MC) models, with their flexibility and relative simplicity have previously been used to model the immune system behaviour. However, due to speed and memory constraints, large-scale simulations cannot be done on a single computer. Key issues in the reduction of simulation time are code optimisation and code parallelisation. In this paper, optimisation and parallelisation solutions are discussed, with reference to existing MC simulation code for dynamics of HIV infection.

Survival with AIDS in Ireland.

OBJECTIVE: To analyse the pattern of survival for patients diagnosed with AIDS in Dublin. METHODS: Data on 193 patients visiting the Department of Genitourinary Medicine of a Dublin Hospital, over a period of 7 years with AIDS, were analysed, and survival patterns were investigated. RESULTS: The cumulative probability of survival for the cohort was 69.0 +/- 3.3% at 1 year and 6.5 +/- 2.5% at 5 years. Median survival was 576 days. Year of diagnosis and disease group to which the patient belonged at diagnosis had a significant influence on the survival of the cohort (P < 0.0006 and P < 0.02, respectively). Age group, disease group, and year of diagnosis showed significant differences between strata (P < 0.01, P < 0.002, and P < 0.04, respectively). Patients aged 35-39 years showed longer median survival times (715 days) than all other age groups (median survival = 547 days; P < 0.04). Patients whose disease group at diagnosis was opportunistic disease(s) alone (Centers for Disease Control 1987 criteria, stage IV group C1) had a significantly longer median survival (672 days) than all others (median survival 281 days; P < 0.0002). Survival did not differ significantly by sex or risk group alone, nor did it differ significantly by manifestation of disease when grouped according to recognised criteria. Treatment with antiretroviral therapy had a significant influence on the survival of the cohort (P < 0.0002), and the treatment group showed a significant difference between strata (P < 0.0002). This result must be qualified by the fact that, first, 11 of the 26 patients not receiving therapy died within 1 month of diagnosis and people who survived longer had a greater chance of beginning treatment, and that, secondly, criteria for antiretroviral therapy allocation were not entirely clear. CONCLUSIONS: The overall survival time found by this study is comparable to that found in other studies in developed countries of the survival of persons with AIDS. Patterns of survival for age groups and manifestation of disease show some contrasting features, mainly owing to the demographic profile of the patients and the high proportion of intravenous drug users.

AIDS in Ireland: the reporting delay distribution and the implementation of integral equation models.

This paper deals with two basic aspects concerning the modelling of AIDS incidence in the context of Irish data. We describe initially the adjustment of the number of AIDS cases (Xij) to allow for reporting delays, where a simple form of the likelihood function for the Xij is supported by GLIM. Subsequently, we consider the accessibility of numerical solution (through a NAG routine) of the integral equation models generated by the back-projection method for the adjusted AIDS cases. Results for the Irish data are summarized for various choices of the incidence distribution.

Relation of habitual diet to fasting plasma insulin concentration and the insulin response to oral glucose.

Blood glucose and plasma insulin levels during a 50 g oral glucose tolerance test were measured in a population sample of 220 people (109 men; 111 women), who also earlier recorded their diet over a 2-d period. In the men, but not the women, there was a statistically significant inverse relationship between total daily energy intake and both fasting and post-load plasma insulin concentrations. This correlation, which was independent of the degree of adiposity, may be a confounding factor in epidemiological studies relating plasma insulin levels to subsequent coronary heart disease.