Difference between revisions of "User:Osborne"
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== Basic Microarray Analysis with geWorkbench == | == Basic Microarray Analysis with geWorkbench == | ||
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| + | ===Background=== | ||
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| + | Correct and complete microarray analysis requires both an understanding of the actual experiment and the statistical and mathmatical tools being used. The tools and techniques being used will vary depending on the type of experiment and what knowledge the user hopes to gain from the experiment. Here we will describe a how to go about analyzing one of the most common types of microarray experiments - differential gene expression on Affymetrix arrays. Most of the techniques described should be suitable for other types of analysis when appropriately modified, but the user is cautioned against applying them blindly to their own data. | ||
===Introduction=== | ===Introduction=== | ||
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====Overview of Microarray Analysis==== | ====Overview of Microarray Analysis==== | ||
| − | # | + | #[[Getting Started]] |
| − | # | + | ## Requirements |
| + | ## Installation | ||
| + | #Preparing the Data | ||
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#Load the data | #Load the data | ||
#Inspect the data | #Inspect the data | ||
Revision as of 13:54, 14 August 2006
Contents
Basic Microarray Analysis with geWorkbench
Background
Correct and complete microarray analysis requires both an understanding of the actual experiment and the statistical and mathmatical tools being used. The tools and techniques being used will vary depending on the type of experiment and what knowledge the user hopes to gain from the experiment. Here we will describe a how to go about analyzing one of the most common types of microarray experiments - differential gene expression on Affymetrix arrays. Most of the techniques described should be suitable for other types of analysis when appropriately modified, but the user is cautioned against applying them blindly to their own data.
Introduction
Overview of Microarray Analysis
- Getting Started
- Requirements
- Installation
- Preparing the Data
- Load the data
- Inspect the data
- Normalize
- T-Test
- GO Analysis
- Promoter Analysis
- Pattern Discovery
- Other Analysis
