Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)

Introducing scikit-learn with a PCA example

PCA is a statistical procedure that’s used to perform a reduction of the dimension of a number of variables to a smaller subset that is linearly uncorrelated. In Chapter 6, we saw a PCA implementation based on using an external application. In this recipe, we will implement the same PCA for population genetics but will use the scikit-learn library. Scikit-learn is one of the fundamental Python libraries for machine learning and this recipe is an introduction to the library. PCA is a form of unsupervised machine learning – we don’t provide information about the class of the sample. We will discuss supervised techniques in the other recipes of this chapter.

As a reminder, we will compute PCA for 11 human populations from the HapMap project.

Getting ready

You will need to run the first recipe from Chapter 6 in order to generate the hapmap10_auto_noofs_ld_12 PLINK file (with alleles recorded as 1 and 2). From a population...