Researchers are advised to consult personnel of the MUSC Proteogenomics Facility to discuss Affymetrix microarray experimental design and costs. Researchers with Affymetrix data not generated by the MUSC Proteogenomics Facility will need to upload their data to the MUSC Proteogenomics Server. Please contact Saurin D. Jani at jani@musc.edu for assistance. To initiate an Affymetrix array analysis project, please contact Saurin D. Jani at jani@musc.edu.
1. Assessment of hybridization quality and sample correlation
using the AffyQC algorithm.
2. Normalization of data using one of the following procedures: MAS5, RMA, gcRMA,
or MBEI(dChip).
3. Identification of differently expressed genes using either a) fold change
and/or t-test for two-
sample experiments; b) 1-way ANOVA for 3- or more-sample
experiments.
4. Significance analysis of gene ontology (GO) terms associated with
differentially expressed genes.
5. Hierarchical clustering of differentially expressed genes and/or samples.
1. A quality control report (AffyQC pdf document).
2. An Excel file of normalized hybridization values and annotations for all
genes.
3. An Excel file of differentially expressed genes containing gene expression
data, filtering metrics and
gene annotations.
4. Three HTML 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 the ArrayQuest web-based analysis tool.
5. Training in the use of the dChip analysis software.