We present a new instance of Laplace's second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric...

In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that...

As a powerful tool for analyzing full conditional (in-)dependencies between random variables, graphical models have become increasingly popular to infer genetic networks based on gene expression data. However, full (unconstrained) conditional relationships between random variables can be only...

Visualisation methods for exploring microarray data are particularly important for gaining insight into data from gene expression experiments, such as those concerned with the development of an understanding of gene function and interactions. Further, good visualisation techniques are useful for...

Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from...

Normalization is an important step in the analysis of microarray data of transcription profiles as systematic non-biological variations often arise from the multiple steps involved in any transcription profiling experiment. Existing methods for data normalization often assume that there are few...

We present a novel, cost efficient two-phase design for predictive clinical gene expression studies: early marker panel determination (EMPD). In Phase-1, genome-wide microarrays are used only for a small number of individual patient samples. From this Phase-1 data a panel of marker genes is...

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a...

Given a multiple testing situation, the null hypotheses that appear to have sufficiently low probabilities of truth may be rejected using a simple, nonparametric method based on decision theory. This applies not only to posterior levels of belief, but also to conditional probabilities in the...

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new...