Example 4: Functional characterization of expression data in Acute myeloid leukaemia

We downloaded a microarray experiment from GEO GDS715 (Stegmaie et al. 2004). It describes a set of Acute Myeloid Leukemia (AML) samples treated with a panel of compounds inducing, with different success, their differentiation to mature cells. The gene expression data of each AML sample treated with a compound was compared to the expression data of the negative controls, AML cells and AML cells treated with compounds that do not alter gene expression. For the comparison between both conditions, we applied a Student t-test to every pair of classes: AML+compound and control.

The output of Expression Differential Analysis is a set of lists of genes, sorted by the t statistic or, in other words, by their importance in the difference between the compound action versus AML status. The files are:

AML + sulmazole AML + fluorouridine AML + phenanthroline

Then we want to study the potential cooperative function of the top differentially expressed genes in each condition. An option is to identify the underlying protein-protein interaction network and its statistical relevance. In CellMaps one can do this by going to Analysis > Functional Analysis > Gene Set Network Enrichment (NetworkMiner).

For each input list file, create a new project (e.g. AML_sulmazole) and start a new Gene Set Network Enrichment Analysis job in the Analysis > Functional Analysis > Gene Set Network Enrichment (NetworkMiner) tool. Follow the following steps in the input form:

1. Define your input file
2. Nature of the lists: Genes
3. Choose the organism: Homo sapiens
4. Interactome confidence: Curated
5. Sort ranked list: Descending (checking the order of your rank)
6. Allow one external intermediate in the subnetwork: Yes
7. Submit the job: press the launch button