> For the complete documentation index, see [llms.txt](https://hq-1.gitbook.io/essential-python-for-genome-science/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://hq-1.gitbook.io/essential-python-for-genome-science/content-organization.md).

# Chapter Contents

## Scope of Writing

I want to write a practical coding book, mainly talking about how to prepare the data for analysis, how to handle analysis results for visualization. I will not explain the theory of analysis in detail (e.g., statistics, machine learning, or biological aspects), but I will provide materials when I mention them. I do hope to keep this brief and straightforward, not to write an encyclopedia of genome science analysis 🧐.

## Chapter 1. Understand Raw Data

In this chapter, I will talk about process of generating the "raw" data from the Illumina sequencing, with an emphasize on the **universal principles** of different technologies. I will also introduce the datasets used throughout the book:

* A bulk RNA-seq dataset from ENCODE
* A single-cell RNA-seq dataset from Allen Brain Institute
* A single-cell snmC-seq2 dataset from my research project

## Chapter 2. Work environment

In this chapter, I will imagine if I have a new computer or server account, the steps of setting up my work environment.

* How to install python and all genomic science tools/packages/software?
* How to do data analysis on the jupyter notebook/lab?
* Some tips on the system/shell level

## Chapter 3. Data Cleaning

In this chapter, I will talk about using pandas with different genome data files. If you do data analysis, you will [spend > 60% of your time cleaning](https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#71548df46f63) your data. If you do not know pandas well, data cleaning can take even longer.

## Chapter 4. Genome Science Data

In this chapter, I will explain the genome science data format in detail. I will also introduce essential tools associated with each data format, including their python versions!

## Chapter 5. Python Basics

In this chapter, I will summarize critical concepts related to the python language, such as "pointer", "everything is an object". I will also list some language skills that prettify your code and significantly improve your efficiency. 

The guide books for me in this chapter are "[Python Cookbook](https://www.amazon.com/Python-Cookbook-Recipes-Mastering-ebook/dp/B00DQV4GGY)" and "[Fluent Python](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008)". They change my understanding of the python language.

## Chapter 6. Data Visualization

In this chapter, I will mainly talk about the matplotlib and seaborn package to make publication level figures. I will explain the matplotlib package in detail, and reproduce complex figures line by line from scratch.

## Chapter 7. Use R in Python

In this chapter, I will talk about using rpy2 to integrate useful R packages into python.

![](/files/-M4GaSZcHGCyNnSwJ_1r)