| Name | lastplot JSON |
| Version |
1.2.1
JSON |
| download |
| home_page | None |
| Summary | LastPlot is a Python package designed to elaborate data into graphs coming from lipid extractions (LC/MS). |
| upload_time | 2024-08-21 12:33:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
 |
| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# Lipid Analysis and Statistical Testing with Plotting for LC-MS Output Transformation (LastPlot)
## What is it
LastPlot 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
LastPlot 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 lastplot
```
\
Alternatively, you can install the package from the source:
```
git clone https://github.com/elide-b/lastplot.git
cd lastplot
pip install .
```
## Usage
Here is one example of how to use LastPlot:
```
import lastplot
# Example usage
df = lastplot.data_workflow(
file_path="My project.xlsx",
data_sheet="Data Sheet",
mice_sheet="Mice ID Sheet",
output_path="C:/Users/[YOUR-USERNAME]/Documents/example",
control_name="WT",
experimental_name=["FTD", "BPD", "HFD"]
)
lastplot.zscore_graph_lipid(
df_final=df,
control_name="WT",
experimental_name=["FTD", "BPD", "HFD"]
output_path="C:/Users/[YOUR-USERNAME]/Documents/example",
palette="tab20b_r",
show=True,
)
```
Returns graphs.
## Examples
For more detailed examples, please check the [example](https://github.com/elide-b/lastplot/tree/master/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.
Raw data
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"description": "# Lipid Analysis and Statistical Testing with Plotting for LC-MS Output Transformation (LastPlot)\n\n## What is it\n\nLastPlot is a Python package designed to elaborate data into graphs coming from lipid extractions (LC/MS).\nStarting from a file containing the **pmol/mg** values per each sample, this package streamlines the process of data\nanalysis and visualization.\n\n## Features\n\nLastPlot includes the following features:\n\n- Data Sanitization: Clean and prepare data for analysis, removing internal standard samples and non value samples.\n- Data Normalization: Normalize values with log10 to ensure consistency across samples.\n- Normality Check: Use the Shapiro-Wilk test to check for normality of residuals.\n- Equality of Variance Check: Use Levene's test to assess the equality of variances.\n- Statistical Significance Annotation: Annotate boxplots with significance levels using t-test, Welch's t-test, or\n Mann-Whitney test depending on the data requirements, through the starbars package.\n- Visualization Tools: Create boxplots to aid in data interpretation.\n\n## Installation\n\nYou can install the package via pip:\n\n```\npip install lastplot\n```\n\n\\\nAlternatively, you can install the package from the source:\n\n```\ngit clone https://github.com/elide-b/lastplot.git\ncd lastplot\npip install .\n```\n\n## Usage\n\nHere is one example of how to use LastPlot:\n\n```\nimport lastplot\n\n# Example usage\ndf = lastplot.data_workflow(\n file_path=\"My project.xlsx\",\n data_sheet=\"Data Sheet\",\n mice_sheet=\"Mice ID Sheet\",\n output_path=\"C:/Users/[YOUR-USERNAME]/Documents/example\",\n control_name=\"WT\",\n experimental_name=[\"FTD\", \"BPD\", \"HFD\"]\n)\n\nlastplot.zscore_graph_lipid(\n df_final=df,\n control_name=\"WT\",\n experimental_name=[\"FTD\", \"BPD\", \"HFD\"]\n output_path=\"C:/Users/[YOUR-USERNAME]/Documents/example\",\n palette=\"tab20b_r\",\n show=True,\n)\n```\n\nReturns graphs.\n\n## Examples\n\nFor more detailed examples, please check the [example](https://github.com/elide-b/lastplot/tree/master/example)\nfolder.\n\n## Contributing\n\nWe welcome contributions!\nIf you have a suggestion that would make this better, please fork the repo and create a pull request. You can also\nsimply open an issue with the tag **\"enhancement\"**.\n\nTo contribute:\n\n1. Fork the repository.\n2. Create a new branch (`git checkout -b feature-branch`).\n3. Commit your changes (`git commit -m 'Add some amazing feature'`).\n4. Push to the branch (`git push origin feature-branch`)\n5. Open a pull request\n\n## License\n\nDistributed under the MIT License. See `LICENSE.txt` for more information.\n",
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