https://github.com/yeolab/anchor

:anchor: Find bimodal, unimodal, and multimodal features in your data
https://github.com/yeolab/anchor

Last synced: 24 days ago
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:anchor: Find bimodal, unimodal, and multimodal features in your data

• Host: GitHub
• URL: https://github.com/yeolab/anchor
• Owner: YeoLab
• License: bsd-3-clause
• Created: 2015-11-17T18:21:29.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-10-26T00:12:35.000Z (over 5 years ago)
• Last Synced: 2024-05-22T16:41:41.031Z (about 1 month ago)
• Language: Python
• Homepage: https://yeolab.github.io/anchor
• Size: 1.68 MB
• Stars: 25
• Watchers: 17
• Forks: 12
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Lists

• awesome-single-cell - anchor - [Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data (Software packages / RNA-seq)
• awesome-single-cell - anchor - [Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data (Software packages / RNA-seq)
• awesome_single_cell - anchor - [Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data (Software packages / RNA-seq)

README

        
![Anchor logo](https://raw.githubusercontent.com/YeoLab/anchor/master/logo/v1/logo.png)

[![](https://img.shields.io/travis/YeoLab/anchor.svg)](https://travis-ci.org/YeoLab/anchor)[![](https://img.shields.io/pypi/v/anchor.svg)](https://pypi.python.org/pypi/anchor)[![codecov](https://codecov.io/gh/YeoLab/anchor/branch/master/graph/badge.svg)](https://codecov.io/gh/YeoLab/anchor)

## What is `anchor`?

Anchor is a python package to find unimodal, bimodal, and multimodal features in any data that is normalized between 0 and 1, for example alternative splicing or other percent-based units.

* Free software: BSD license

* Documentation: https://YeoLab.github.io/anchor

## 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 scipy numpy matplotlib seaborn

```

### Stable (recommended)

To install this code from the Python Package Index, you'll need to specify ``anchor-bio`` (``anchor`` was already taken - boo).

```

pip install anchor-bio

```

### Bleeding-edge (for the brave)

If you want the latest and greatest version, clone this github repository and use `pip` to install

```

git clone [email protected]:YeoLab/anchor

cd anchor

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

```

## Usage

`anchor` was structured like `scikit-learn`, where if you want the "final

answer" of your estimator, you use `fit_transform()`, but if you want to see the

intermediates, you use `fit()`.

If you want the modality assignments for your data, first make sure that you

have a `pandas.DataFrame`, here it is called `data`, in the format (samples,

features). This uses a log2 Bayes Factor cutoff of 5, and the default Beta

distribution parameterizations (shown [here]())

```python

import anchor

bm = anchor.BayesianModalities()

modalities = bm.fit_transform(data)

```

If you want to see all the intermediate Bayes factors, then you can do:

```python

import anchor

bm = anchor.BayesianModalities()

bayes_factors = bm.fit(data)

```

## History

### 1.1.1 (2017-06-29)

- In `infotheory.binify`, round the decimal numbers before they are written as strings

### 1.0.1 (2017-06-28)

- Documentation and build fixes

### 1.0.0 (2017-06-28)

* Updated to Python 3.5, 3.6

### 0.1.0 (2015-07-08)

* First release on PyPI.