Microarray Core Facility

Introduction of Microarray Data Analysis

Large complexity of raw gene expression data generated by oligonucleotide chips, spotted arrays, or whatever technology is used, create challenging data analysis and data management problems. Such volumes of data are too large to analyze by simple sorting in spreadsheets, or plotting as graphs. Each type of microarray has it own unique analysis features. Here are general procedures to analysis microarray:
Analysis Stage Description Examples of Methods
Normalization Remove systematic variation. Equalized overall signal across array to be compared, ensures linearity of response across abundance classes. Whole chip
Per gene
Dye swap
Comparative Compares expression of a gene across two or more samples to determine significant changes in expression. ANOVA
Fold change
Rank order (MAS 5 etc.)
Clustering Identifies significant correlation in expression data across experiments/conditions Hierarchical clustering
k-means clustering
Self-organizing maps
and many more
Biological overlay Identify functions for give genes; functional clusters of genes; hypothesis generation Multi-database access (Source)
Functional grouping (Gene Ontology, KEGG, GenMAPP)
PubMed Correlations (PubGene)