A gene expression pattern in blood for the early detection of Alzheimer's disease.

A whole genome screen was performed using oligonucleotide microarray analysis on blood from a large clinical cohort of Alzheimer's disease (AD) patients and control subjects as clinical sample. Blood samples for total RNA extraction were collected in PAXgene tubes, and gene expression analysis performed on the AB1700 Whole Genome Survey Microarrays. When comparing the gene expression of 94 AD patients and 94 cognitive healthy controls, a Jackknife gene selection based method and Partial Least Square Regression (PLSR) was used to develop a disease classifier algorithm, which gives a test score indicating the presence (positive) or absence (negative) of AD.

Mixture models as a method to find present and divergent genes in comparative genomic hybridization studies on bacteria.

Comparative genomic hybridization (CGH) using microarrays is performed on bacteria in order to test for genomic diversity within various bacterial species. The microarrays used for CGH are based on the genome of a fully sequenced bacterium strain, denoted reference strain. Labelled DNA fragments from a sample strain of interest and from the reference strain are hybridized to the array.

A discussion concerning the inclusion of variety effect when analysis of variance is used to detect differentially expressed genes.

In microarray studies several statistical methods have been proposed with the purpose of identifying differentially expressed genes in two varieties. A commonly used method is an analysis of variance model where only the effect of interaction between variety and gene is tested. In this paper we argue that in addition to the interaction effects, the main effect of variety should simultaneously also be taken into account when posting the hypothesis.

Regression as a method to predict copy numbers in comparative genomic hybridization studies on bacteria.

Comparative genomic hybridizations (CGH) using microarrays are performed with bacteria in order to determine the level of genomic similarity between various strains. The microarrays applied in CGH experiments are constructed on the basis of the genome sequence of one strain, which is used as a control, or reference, in each experiment. A strain being compared with the known strain is called the unknown strain.

Prediction of missing values in microarray and use of mixed models to evaluate the predictors.

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction.