IBIS (Integrated Bayesian Inference System) offers powerful search capabilities into your data. It can identify non-linear and combinatorial patterns of gene expression that characterize different toxicity responses, disease states, or treatment outcomes. Furthermore, it can be used to build classifiers that can identify these patterns in new samples.
IBIS is used most commonly as a search tool, to identify single genes and small gene sets that show interesting expression patterns relative to the sample classification. We will work through an example related to personalized medicine. We will attempt to identify patterns of basal gene expression that are predictive of drug response, using the NCI 60 data from the Developmental Therapeutics Program and the Genomics and Bioinformatics Group, both from the National Cancer Institute, National Institutes of Health. In this experiment 60 cancer cell lines from various tissues had their basal gene expression level measured. Each cell line was also exposed to a number of anti-cancer treatments, and the GI50 was measured. A valuable question to ask is whether the pre-treatment basal expression can be used to predict the effectiveness of a compound. This would provide a molecular basis for selecting appropriate therapies. IBIS can help to answer these types of questions by identifying gene expression patterns that are characteristic of effective or ineffective compounds.
IBIS has a number of different parameters that allow you to search for different types of biologically plausible relationships in the data. We will start with identifying simple but perhaps less predictive patterns, and introduce more effective models.
The simplest type of predictive gene expression patterns involve only a single gene, and are linear in nature. These patterns are often expressed as rules, such as when PSA levels are high, prostate cancer is likely. IBIS can be used to identify these types of patterns.
This tutorial should take about 45 minutes, depending on how long you spend investigating the data, and how fast your machine is.
If you must stop part way through the tutorial, simply exit the program by selecting Exit from the File menu. The data and experiments you have performed to that point are saved automatically by GeneLinkerô. The next time you start GeneLinkerô, you can continue on with the next step in the tutorial.