Automize Science

What is it

Automize Science is a Python package designed to elaborate data into graphs coming from lipid extractions (LC/MS). Starting from a file containing the pmol/mg values per each sample, this package streamlines the process of data analysis and visualization.

Features

Automize Science includes the following features:

• Data Sanitization: Clean and prepare data for analysis, removing internal standard samples and non value samples.
• Data Normalization: Normalize values with log10 to ensure consistency across samples.
• Normality Check: Use the Shapiro-Wilk test to check for normality of residuals.
• Equality of Variance Check: Use Levene's test to assess the equality of variances.
• Statistical Significance Annotation: Annotate boxplots with significance levels using t-test, Welch's t-test, or Mann-Whitney test depending on the data requirements, through the starbars package.
• Visualization Tools: Create boxplots to aid in data interpretation.

Installation

You can install the package via pip:

pip install automize_science

Alternatively, you can install the package from the source:

git clone https://github.com/elide-b/automize-science.git
cd automize-science
pip install .

Usage

Here is one example of how to use Automize Science:

import automize_science 

# Example usage
df = automize_science.data_workflow(
    file_path="My project.xlsx",
    data_sheet="Data Sheet",
    mice_sheet="Mice Sheet",
    output_path="C:/Users/[YOUR-USERNAME]/Documents/example",
    control_name="CM",
)

automize_science.zscore_graph_region(
    df_final=df,
    control_name="CM",
    experimental_name="EM",
    output_path="C:/Users/[YOUR-USERNAME]/Documents/example",
    palette="Set2",
    show=True,
)

Returns graphs.

Examples

For more detailed examples, please check the example folder.

Contributing

We welcome contributions! If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add some amazing feature').
4. Push to the branch (git push origin feature-branch)
5. Open a pull request

License

Distributed under the MIT License. See LICENSE.txt for more information.