index.qmd

---
title: "Introduction to Python for Biologists"
---

## Introduction

This introduction to Python workshop will provide beginners with experience loading, exploring, and visualising biological data using the pandas and matplotlib libraries. The example data used is clinical and gene expression data from the METABRIC breast cancer dataset, allowing participants to work with realistic biomedical data structures and learn how to generate meaningful summaries and plots.

## Learning Objectives

By the end of this workshop, you will be able to:

- Load tabular data into Python using pandas 
- Calculate basic statistics such as mean and median
- Filter and slice data based on clinical features
- Produce simple plots such as scatter plots and boxplots using matplotlib
- Modify the appearance of plots 


## Prerequisites {#prerequisites}

Before starting this course you will need to ensure that your computer is set up with the required software. If you have any difficulty installing any of this software then please contact the trainer at <a href="mailto:sandun.rajapaksa@petermac.org">sandun.rajapaksa@petermac.org</a> for help.

### Step 1: Installing Python

There are multiple ways you can use Python. The easiest and most convenient way is to install Python on your own computer. However, if you prefer to avoid the installation process or need additional computational capabilities the alternative option is to use the cluster.

#### Install Python on your own computer

For new users, we recommend installing **Anaconda**. Anaconda conveniently installs *Python*, the *Jupyter Notebook*, and other commonly used packages.

```{=html}
<details>
<summary style="color: #4492B4; font-weight: 600;" >
Windows
</summary>
```
If you have admin rights, follow [Anaconda Navigator Installation](https://www.anaconda.com/docs/getting-started/anaconda/install#windows-installation). Otherwise, contact the IT Support.

</details>

```{=html}
<details>
<summary style="color: #4492B4; font-weight: 600;" >
macOS
</summary>
```
Install the Anaconda Navigator from the [Anaconda Navigator Installation](https://www.anaconda.com/docs/getting-started/anaconda/install#macos-linux-installation).

</details>

```{=html}
<details>
<summary style="color: #4492B4; font-weight: 600;" >
Linux
</summary>
```
Install the Anaconda Navigator from [here](https://www.anaconda.com/docs/getting-started/anaconda/install#macos-linux-installation).

</details>

Once installed, open the anaconda-navigator directly or type the following command in the terminal to open it.

```{python}
#| eval: false
anaconda-navigator
```

![Figure 1: Anaconda Navigator](vignettes/images/anaconda-navigator.png){fig-align="center"}

**Useful links:**

-   [Getting started with Navigator](https://docs.anaconda.com/free/navigator/getting-started/)
-   [How to create/manage environments?](https://www.anaconda.com/docs/getting-started/working-with-conda/environments)

### Step 2: Installing an Interactive Computing Environment (ICE)

Once Python is installed, choose an interface to write and run code. We recommend either **Jupyter Notebook** (classic) or **JupyterLab** (the newer, more flexible interface). Both run locally in your browser and work well for this workshop.

#### Install Jupyter Notebook (Classic)

- Open Anaconda Navigator and install Jupyter Notebook.
- Launch Jupyter Notebook from Anaconda Navigator, or start it from a terminal:

```{python}
#| eval: false
jupyter notebook
```

- Your browser will open the Notebook home page.

![Figure 2: Jupyter Notebook](vignettes/images/jupyter-notebook.png)

**Resources:**

- [Installation guide](https://docs.jupyter.org/en/latest/install.html)
- [Documentation](https://jupyter-notebook.readthedocs.io/en/latest/)

#### Install JupyterLab (Recommended)

- In Anaconda Navigator, install JupyterLab and click Launch; or start it from a terminal:

```{python}
#| eval: false
jupyter lab
```

- JupyterLab provides tabs, file browser, terminals, and more in one interface. It opens in your default web browser.

**Resources:**

- [Getting started](https://jupyterlab.readthedocs.io/en/stable/)
- [Overview](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html)

Either Notebook or JupyterLab is fine for this workshop. Pick the one you prefer. If you're new and want a modern UI, try JupyterLab.

**Useful links:**

-   [Jupyter Notebook Documentation](https://jupyter-notebook.readthedocs.io/en/latest/)
-   [How to use Jupyter Notebooks - DataCamp](https://www.datacamp.com/tutorial/tutorial-jupyter-notebook)
-   [Jupyter Notebook: An Introduction](https://realpython.com/jupyter-notebook-introduction/)

### Step 3: Installing a Python library

If you are currently using Python using conda (or Anaconda), a Python library can be installed with Anaconda or Miniconda. For example, to install the `pandas`, `numpy` and `matplotlib` libraries of Python use the following command on the terminal.

```{python} 
#| eval: false
conda install -c conda-forge pandas matplotlib numpy
```

If you installed Python using Pip, then a Python library can be installed via pip from Python Package Index ([PyPI](https://pypi.org/project/pandas)). To install the required libraries of Python use the following command on the terminal.

```{python} 
#| eval: false
pip install pandas matplotlib numpy
```

If Anaconda Navigator is installed and you prefer to use the Navigator instead of typing commands on a terminal refer to [Installing and managing Python packages](https://www.anaconda.com/docs/tools/anaconda-navigator/getting-started#managing-python).

## Course Outline

{{< include _includes/syllabus.qmd >}}

## Data

The Metabric study characterized the genomic mutations and gene expression profiles for 2509 primary breast tumours. In addition to the gene expression data generated using microarrays, genome-wide copy number profiles were obtained using SNP microarrays. Targeted sequencing was performed for 2509 primary breast tumours, along with 548 matched normals, using a panel of 173 of the most frequently mutated breast cancer genes as part of the Metabric study.

**References:**

-   [Curtis *et al.*, Nature 486:346-52, 2012](https://pubmed.ncbi.nlm.nih.gov/22522925)
-   [Pereira *et al.*, Nature Communications 7:11479, 2016](https://www.ncbi.nlm.nih.gov/pubmed/27161491)

Both the clinical data and the gene expression values were downloaded from [cBioPortal](https://www.cbioportal.org/study/summary?id=brca_metabric).

We excluded observations for patient tumor samples lacking expression data, resulting in a data set with fewer rows.

The following table illustrates the column names and descriptions of the metabric data frame we will be using for subsequent analysis. 

::: scrolling
```{=html}
<table class="no-spacing" cellspacing="0" >
  <caption>Description of column names in the metabric dataset</caption>
  <thead>
    <tr>
      <th>Column Name</th>
      <th style="padding-left: 120px; " >Description</th>
    </tr>
  </thead>
  <tbody>
    <tr><td>Patient_ID</td>	<td>Identifier to uniquely specify a patient.</td></tr>
    <tr><td>Cohort</td>	<td>Study group or cohort to which the patient belongs.</td></tr>
    <tr><td>Age_at_diagnosis</td>	<td>Age at Diagnosis</td></tr>
    <tr><td>Survival_time/Os_Months</td>	<td>Overall survival in months since initial diagnosis.</td></tr>
    <tr><td>Survival_status/Os_Status</td>	<td>Overall patient survival status.</td></tr>
    <tr><td>Vital_status</td>	<td>The survival state of the person.</td></tr>
    <tr><td>Chemotherapy</td>	<td>Chemotherapy.</td></tr>
    <tr><td>RadioTherapy</td>	<td>RadioTherapy</td></tr>
    <tr><td>Tumor_size</td>	<td>Tumor size in mm.</td></tr>
    <tr><td>Tumor_stage</td>	<td>Tumor stage.</td></tr>
    <tr><td>Neoplasm_histologic_grade/Grade</td>	<td>Numeric value to express the degree of abnormality of cancer cells, a measure of differentiation and aggressiveness.</td></tr>
    <tr><td>Lymph_nodes_examined_positive</td>	<td>Number of lymph nodes positive</td></tr>
    <tr><td>Lymph_nodes_status</td> <td>Lymph nodes status</td></tr>
    <tr><td>Cancer_type</td>	<td>Cancer Type</td></tr>
    <tr><td>ER_status</td>	<td>ER Status measured by IHC</td></tr>
    <tr><td>PR_Status</td>	<td>PR Status</td></tr>
    <tr><td>HER2_status</td>	<td>HER2 Status</td></tr>
    <tr><td>HER2_status_measured_by_SNP6</td>	<td>HER2 status measured by SNP6</td></tr>
    <tr><td>PAM50</td>	<td>Pam50 + Claudin-low subtype.</td></tr>
    <tr><td>3-gene_classifier</td>	<td>3-Gene classifier subtype</td></tr>
    <tr><td>Nottingham_prognostic_index</td>	<td>Nottingham prognostic index</td></tr>
    <tr><td>Cellularity</td>	<td>Tumor Content</td></tr>
    <tr><td>Integrative_cluster</td>	<td>Integrative Cluster</td></tr>
    <tr><td>Mutation_count</td>	<td>Mutation count</td></tr>
    <tr><td>ESR1</td>	<td>ESR1 Expression data</td></tr>
    <tr><td>ERBB2</td>	<td>ERBB2 Expression data</td></tr>
    <tr><td>PGR</td>	<td>PGR Expression data</td></tr>
    <tr><td>TP53</td>	<td>TP53 Expression data</td></tr>
    <tr><td>PIK3CA</td>	<td>PIK3CA Expression data</td></tr>
    <tr><td>GATA3</td>	<td>GATA3 Expression data</td></tr>
    <tr><td>FOXA1</td>	<td>FOXA1 Expression data</td></tr>
    <tr><td>MLPH</td>	<td>MLPH Expression data</td></tr>
  </tbody>
</table>
```
:::

## Credits and Acknowledgements

This content was adapted from the following course materials:

-   [OHI Data Science Training](http://ohi-science.org/data-science-training/index.html)
-   [Plotting and Programming in Python](https://swcarpentry.github.io/python-novice-gapminder/)

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