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GeneLinkerô Tour - Introduction

 

Welcome to GeneLinkerô

Thank you for choosing GeneLinkerô as your gene expression analysis system. The GeneLinkerô family of products are designed to help you discover underlying patterns in the data generated by modern high-throughput gene expression measurement techniques; the first step in discovering new relationships among genes.

 

Introduction

This tour describes the GeneLinkerô main window and outlines the program's major functionality groups (e.g. data import, preprocessing, clustering, visualization, and for platinum - classification). The fastest way to learn to use GeneLinkerô is to finish this tour and then run the tutorials.

Some additional functions not covered in this tour are GeneLinker's Scripting and Meta-Scripting capability. These advanced features greatly enhance GeneLinker's ease-of-use by allowing repetitive actions to be performed automatically.

 

Terminology

Term

Definition

Dataset

A dataset is either a raw or preprocessed set of expression values for a number of genes over a number of samples. A dataset can have reliability measurements  or variables associated with it. For a complete description see Datasets Overview and Reliability Measures.

  • A standard dataset contains a single value for each gene for every sample (some may be replicate measurements within or between chips; in an incomplete dataset, one or more values are null or missing).

  • A two-color dataset contains two values for each gene for every sample. One value is the treatment expression level and the other is the control expression level. See Two-Color Data.

Experiment

An experiment is a dataset that has had its gene or sample order organized by the application of an experiment process such as clustering.

Variable

In GeneLinkerô, a variable is a column of data other than gene expression values used to differentiate samples. See Variables Overview.

A variable can store:

  • Phenotypic observations about the samples.

e.g. malignant vs. benign.

  • Predictions of phenotypes by a trained classifier.

e.g. predicted malignant vs. predicted benign.

  • Information about experimental conditions.

e.g. high dose vs. low dose; time the sample was taken; animal A vs. animal B vs. animal C, etc.