https://github.com/yeolab/bonvoyage

:triangular_ruler: Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
https://github.com/yeolab/bonvoyage

Last synced: 2 months ago
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:triangular_ruler: Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.

• Host: GitHub
• URL: https://github.com/yeolab/bonvoyage
• Owner: YeoLab
• License: bsd-3-clause
• Created: 2015-11-20T20:13:09.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2017-06-29T14:31:07.000Z (over 7 years ago)
• Last Synced: 2024-08-02T16:44:02.073Z (6 months ago)
• Language: Python
• Homepage: http://yeolab.github.io/bonvoyage/
• Size: 8.35 MB
• Stars: 7
• Watchers: 38
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Authors: AUTHORS.md

Awesome Lists containing this project

• awesome-single-cell - bonvoyage - [Python] - 📐 Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction. (Software packages / RNA-seq)

README

        
![Bonvoyage logo: A bottle of champagne as the y=-x + 1 line on a Cartesian plane](https://raw.githubusercontent.com/YeoLab/bonvoyage/master/logo/v2/logo-128.png)

[![Build Status](https://travis-ci.org/YeoLab/bonvoyage.svg?branch=master)](https://travis-ci.org/YeoLab/bonvoyage)[![](https://img.shields.io/pypi/v/bonvoyage.svg)](https://pypi.python.org/pypi/bonvoyage)

## What is `bonvoyage`?

Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.

* Free software: BSD license

* Documentation: https://yeolab.github.io/bonvoyage

## Installation

To install `anchor`, we recommend using the

[Anaconda Python Distribution](http://anaconda.org/) and creating an

environment, so the `anchor` code and dependencies don't interfere with

anything else. Here is the command to create an environment:

```

conda create -n anchor-env pandas numpy matplotlib seaborn scikit-learn

```

### Stable (recommended)

To install this code from the Python Package Index, you can install on the

command line via `pip`:

```

pip install bonvoyage

```

### Bleeding-edge (for the brave)

To install this code, clone this github repository and use `pip` to install

    git clone [email protected]:yeolab/bonvoyage

    cd bonvoyage

    pip install .  # The "." means "install *this*, the folder where I am now"

## Usage

To use `bonvoyage` to get waypoints, you want your `data` to be a `pandas`

DataFrame of shape (n_samples, n_features)

```python

import bonvoyage

wp = bonvoyage.Waypoints()

waypoints = wp.fit_transform(data)

```

`bonvoyage` is modeled after `scikit-learn` in is method of creating a

transforming object and then running `fit_transform()` to perform the computation.

To plot the waypoints, use a `waypointplot`, which can do either `"scatter"` or

`"hex"` plot types. By default, `hexbin` plots are used:

```python

import bonvoyage

bonvoyage.waypointplot(waypoints)

```

![Hexbin waypoints](figures/iPSC_hexbin.png)

You can also specify to use `scatter`:

```python

import bonvoyage

bonvoyage.waypointplot(waypoints, kind='scatter')

```

![Scatter waypoints](figures/iPSC_scatter.png)

To add color, give a series or other `groupby`-able object:

```python

import bonvoyage

bonvoyage.waypointplot(waypoints, kind='scatter', features_groupby=modalities)

```

![Scatter, colored by modality waypoints](figures/iPSC_scatter_modality.png)

## History

### 1.0.0 (2017-06-28)

* Added tests and examples

### 0.1.0 (2015-09-15)

* First release on PyPI.