Bioinformatics - analysis of microarray data

Guri Feten

The technology of DNA microarray allows monitoring expression levels for thousands of genes simultaneously.

The microarray data are characterized by many measured variables (genes) on only a few observations (replications, parallels). 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 the genes with missing values can be removed, or the missing values can be replaced using prediction. A comparative study of six imputation methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. Different genes do not necessarily share the same optimal prediction method, or receive the same error of prediction. We propose a method for clustering the genes into different clusters, where all the genes in one cluster share the same optimal prediction method, and receive approximately the same error of prediction. Improved prediction of the missing values and the estimated error of prediction may then be achieved.

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. The ratios of fluorescent intensities obtained from the spots on the microarrays is used to determine which genes are divergent in the unknown strain, as well as to predict the copy number of actual genes in the unknown strain.

Supervisors:
Trygve Almøy, associate professor, Dept of Chemistry, Biotechnology and Food Science
Ågot Aakra, researcher, Dept of Chemistry, Biotechnology and Food Science
Ole Christian Lingjærde, associate professor, Dept of Informatics, University of Oslo



Oppdatert: 20.06.05
Utskriftsvennlig versjon : Arkiv : Tips en venn