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Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781782175117
Length 306 pages
Edition 1st Edition
Languages
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Author (1):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
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Table of Contents (16) Chapters Close

Bioinformatics with Python Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Python and the Surrounding Software Ecology FREE CHAPTER 2. Next-generation Sequencing 3. Working with Genomes 4. Population Genetics 5. Population Genetics Simulation 6. Phylogenetics 7. Using the Protein Data Bank 8. Other Topics in Bioinformatics 9. Python for Big Genomics Datasets Index

Performing Principal Components Analysis


Principal Components Analysis (PCA) is a statistical procedure to perform a reduction of dimension of a number of variables to a smaller subset that is linearly uncorrelated. Its practical application in population genetics is assisting the visualization of relationships of individuals that is being studied.

While most of the recipes in this chapter make use of Python as a "glue language" (Python calls external applications that actually do most of the work) with PCA, we have an option, that is, we can either use an external application (for example, EIGENSOFT smartpca) or use scikit-learn and perform everything on Python. We will perform both.

Getting ready

You will need to run the first recipe in order to use the hapmap10_auto_noofs_ld_12 PLINK file (with alleles recoded as 1 and 2). PCA requires LD-pruned markers; we will not risk using the offspring here because it will probably bias the result. We will use the recoded PLINK file with alleles as...

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