Researchers are advised to consult personnel of the SC INBRE Biotechnology core to discuss Agilent microarray experimental design and costs. To initiate an Agilent array analysis project, please contact either Dr. Pierre Rivailler at pierre@biol.sc.edu or Dr. Kim Creek at Creek@med.sc.edu.
1. Background correction using normexp algorithm.
2. Normalization within arrays using lowess method.
3. Compute overall intensities (A) and log ratios (M) values.
4. Create new parameterization based on experimental design.
5. Identification of differently expressed genes using either a) fold change (CARMAweb)
or b)
statistics (empirical Bayes moderated t-statistic, the corresponding
P-values adjusted to control the
false discovery rate and empirical Bayes B log
odds of differential expression) generated by
limmaGUI.
6. Significance analysis of gene ontology (GO) terms associated with
differentially expressed genes.
7. Hierarchical clustering of differentially expressed genes and/or samples.
1. An Excel file of normalized overall intensities values (A)
and annotations for all genes.
2. An Excel file of normalized log ratios values (M) and annotations for all
genes.
3. An Excel file of differentially expressed genes containing gene expression
data, filtering metrics and
gene annotations.
4. Three Excel files reporting GO term significance analyses (biological
process, molecular function
and cellular component).
5. An image file (jpg) of clustering analysis.
1. Replication of above using different filtering criteria.
2. Correlation analysis to find genes with specific expression patterns.
3. Sample cluster analysis to explore how different clustering/distance
algorithms define sample
relationships.
4. Training in the use of limmaGUI.
5. Training in the use of carmaweb.