Microarray analysis techniques

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA, RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment. Such experiments generate a very large volume of genetic data that can be difficult to analyze, especially in the absence of good gene annotation. Most microarray manufacturers, such as Affymetrix, provide commercial data analysis software with microarray equipment such as plate readers. Commercial systems for gene network analysis such as Ingenuity [1] and Pathway studio create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as GenMAPP also aid in organizing and visualizing gene network data procured from one or several microarray experiments. A wide variety of microarray analysis tools are availible through Bioconductor written in the R programming language. The frequently cited SAM Excel module and other microarray tools [2] are available through Stanford University.

Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail.

Specialized software tools for statistical analysis to determine the extent of over- or under-expression of a gene in a microarray experiment relative to a reference state have also been developed to aid in identifying genes or gene sets associated with particular phenotypes. One such method of analysis, known as Gene Set Enrichment Analysis (GSEA), uses a Kolmogorov-Smirnov-style statistic to identify groups of genes that are regulated together^[3]. This third-party statistics package offers the user information on the genes or gene sets of interest, including links to entries in databases such as NCBI's GenBank and curated databases such as Biocarta and Gene Ontology. A related system, PAINT [4] performs a statistical analysis on gene promoter regions, identifying over and under representation of transcription factor response elements.

References

1. ^ Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545-50.