This session will be held in the Erskine Building, Room 445
15:40 — 16:00
Gregory Falzon
University of New England
Second-Harmonic Laser Microscopy promises to be a useful diagnostic modality for breast cancer. Statistical image analysis has provided key insights into the differences between images of normal, benign and malignant breast tissue. Spectral analysis of image features coupled with a support-vector machine classifier is demonstrated to accurately separate normal from tumour tissue. Further analysis of the tumour group using the multi-scale, multi-directional, steerable pyramid filter has revealed features that can be used to separate benign from malignant breast tissue. The classifier presented can serve as a prototype for devices developed to serve in a clinical setting.
16:00 — 16:20
Debbie Leader
The University of Auckland
The incorporation of biological information into the microarray analysis process has become increasingly important. One reason for doing this is to provide a biologically meaningful interpretation of the analysis results. While the incorporation of such information is well documented in terms of detecting differentially expressed genes, less work has been done on extending these ideas into the classification of biologically distinct samples. We describe a method for incorporating gene set information, such as KEGG pathway or Gene Ontology details into the classification process. This approach utilises principal co-ordinates analysis (PCO) to create a summary of gene set activity, and then uses these summaries as explanatory variables in the classification and prediction process. This procedure is illustrated via application to a breast cancer data set published by Wang et al (2005, The Lancet, vol. 365).
16:20 — 16:40
Jennifer Wilcock
University of Auckland
Alan Lee
University of Auckland
In two-stage case-control studies, outcome status and one or more inexpensive covariates are observed for a large sample but additional, more expensive covariates are collected for a subsample only, selected by random sampling from the strata defined at the first stage. Large efficiency and/or cost gains are possible using two-stage rather than one-stage studies of comparable cost or power. Here we demonstrate a method for designing two-stage studies to obtain the best possible precision under specified cost constraints, by applying an efficient semi- parametric maximum likelihood approach due to Scott and Wild (University of Auckland) which has been developed for the analysis of a class of generalised case-control designs.
As with all model-based approaches, the ‘optimal’ design found is sensitive to the values of the model parameters used for deriving the design. If the design parameters are particularly inaccurate this may result in an ‘optimal’ design which is less efficient than that which would have been derived using a more robust design approach. The efficiency of the design depends on the sampling fractions within each stratum, and here a method will be presented for comparing designs with ‘optimal’ to those with balanced second stage sample sizes, under specified cost constraints.
Independent component analysis and statistical parametric mapping of the relationship between personality and brain blood flow in normal males
16:40 — 17:00
In Kang
University of Canterbury
Marco Reale
University of Canterbury
Carl Scarrott
University of Canterbury
Irene L Hudson
University of South Australia
Robin Tuner
University of New South Wales
Medical images are an important source of information about physiological processes, but they are often deteriorated by noise due to various sources of interference and other phenomena that affect the measurement processes in imaging and data acquisition systems. The images are mixtures of unknown combinations of sources summing differently at each of the sensors. Independent component analysis (ICA) [Hyv�rinen, A. and Oja, E., 2001] is an effective method for removing artifacts and separating sources of the brain signals from medical images. In this study, we assess the relationship between regional cerebral blood flow (rCBF) and all seven of the Temperament and Character Index (TCI) personality traits using ICA. ICA can assess the difference in rCBF between quartile groups for each personality trait to identify brain regions. Significant clusters of activation (increasing level of trait associated with increasing blood flow) or deactivation (decreasing level of trait associated with increasing level of blood flow) were found in relation to all seven TCI traits. The ICA linear model results showed that a significant relationship in specific regions of the brain. Graphs of the average regional cerebral blood flow highlighted the existence of non-linear relationships delineated by the independent components. These results support previous work showing a biological basis for the TCI model [Cloninger, C., 2002] and non-linear model [Turner et al 2003, Turner, R. 2005].