Aggregation effect in microarray data analysis.

Inferring gene regulatory networks from microarray data has become a popular activity in recent years, resulting in an ever-increasing volume of publications. There are many pitfalls in network analysis that remain either unnoticed or scantily understood. A critical discussion of such pitfalls is long overdue.

Gene selection with the δ-sequence method.

In this chapter, we discuss a method of selecting differentially expressed genes based on a newly discovered structure termed as the δ-sequence. Together with the nonparametric empirical Bayes methodology, it leads to dramatic gains in terms of the mean numbers of true and false discoveries, and in the stability of the results of testing. Furthermore, its outcomes are entirely free from the log-additive array-specific technical noise.

First-spike latency in the presence of spontaneous activity.

A new statistical method for the estimation of the response latency is proposed. When spontaneous discharge is present, the first spike after the stimulus application may be caused by either the stimulus itself, or it may appear due to the prevailing spontaneous activity. Therefore, an appropriate method to deduce the response latency from the time to the first spike after the stimulus is needed.

Detecting intergene correlation changes in microarray analysis: a new approach to gene selection.

Background: Microarray technology is commonly used as a simple screening tool with a focus on selecting genes that exhibit extremely large differential expressions between different phenotypes. It lacks the ability to select genes that change their relationships with other genes in different biological conditions (differentially correlated genes). We intend to enrich the above procedure by proposing a nonparametric selection procedure that selects differentially correlated genes.

A nitty-gritty aspect of correlation and network inference from gene expression data.

Background: All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels.

Results: This paper provides a theoretical consideration of the random effect of signal aggregation and its implications for correlation analysis and network inference.