FGT Part 4 - Identifying Differentially Gene Expression in Microarray

Describe the strengths and weakness of filtering on fold change to identify differentially expressed genes from gene expression microarray data

Fold Change
Fold Filtering

When analysing a microarray gene expression dataset it is important to assess the general quality of the data. Describe three methods by which data quality can be assessed. For each method indicate how low and high quality data can be distinguished. 
Check Spike-In
Visual inspection of distribution using scatter plots
Check internal control genes
Check replicate variability

Describe how you might use a scatter plot or MA (MvsA) plot to visually assess the quality of a microarray gene expression data?
M = log2(R/G), log ratio intensity, which means difference between log intensity.
A = 1/2log2(RG) average log intensity
Assume M=0 because most of the gene are not different.
If different apply normalisation

Non-parametric statistical tests can be used as an alternative to parametric statistical test for the identificationof differentially expressed genes in microarray gene expression profiling experiments. Describe in principle how a non-parametric test might be performed and indicate one advantage and one disadvantage of using such test as alternative to parametric test!
Parametric & Non parametric test

Biological consideration
Pooling

Volcano Plot are usually used in the analysis and interpretation of gene expression experiments. What is volcano plot and how it can be used to aid identification of differentially expressed genes?

Describe how functional enrichment analysis can be applied to the results of a microarray experiment. Briefly outline the principle of underlying the calculation of functional enrichment statistics!