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Tutorial 2: Introduction

This tutorial leads you through the process of preparing a dataset that has missing values, clustering it, and then visualizing the clustering results.

 

Skills You Will Learn:

How to import gene expression data from a tabular file into the GeneLinkerô database.

How to import a gene list.

How to import a variable (class labels).

How to estimate missing values.

How to rename a dataset in the Experiments navigator.

How to perform a hierarchical clustering experiment.

How to view experiment results in a matrix tree plot.

How to generate a report and export an image.

 

Dataset Information

The National Cancer Institute (NCI) maintains a set of 60 human cancer cell lines (NCI60). They are used in cDNA microarray studies to assess the gene expression profiles, as well as in screening anti-cancer drugs Reference 1.

The purpose of this tutorial is to demonstrate GeneLinkerô analysis and how it creates new perspectives on important biomedical relationships. A number of GeneLinkerô functions are used to go through the analysis in a step-by-step fashion. The approach is similar to that in Reference 1.

The data consists of expression measurements for 1416 differentially expressed genes (normalized log(Cy3/Cy5)) for 60 cancer cell lines. This is referred to in Reference 1 and in this tutorial as the t-matrix.  Other NCI60 datasets, including the gene expression data for all 9,703 genes (all_genes), drug activities against the 60 cell lines (A-matrix and A118-matrix), and the gene-drug correlation data (AT-matrix), are not discussed here.

Please see Reference 1 and Reference 2 for more detailed discussions of the original experiments and data.

 

Tutorial Length

This tutorial should take about 20 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, exit the program by selecting Exit from the File menu. The data and experiments you have performed to that point will be saved automatically by the application. The next time you start GeneLinkerô, you can continue on with the next step in the tutorial.