https://github.com/atarashansky/magic

MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.

https://github.com/atarashansky/magic

Repository

MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.

https://github.com/atarashansky/MAGIC/blob/master/

Markov Affinity-based Graph Imputation of Cells (MAGIC)
-------------------------------------------------------

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Markov Affinity-based Graph Imputation of Cells (MAGIC) is an algorithm for denoising high-dimensional data most commonly applied to single-cell RNA sequencing data. MAGIC learns the manifold data, using the resultant graph to smooth the features and restore the structure of the data.

To see how MAGIC can be applied to single-cell RNA-seq, elucidating the epithelial-to-mesenchymal transition, read our [publication in Cell](https://www.cell.com/cell/abstract/S0092-8674(18)30724-4).

[David van Dijk, et al. **Recovering Gene Interactions from Single-Cell Data Using Data Diffusion**. 2018. *Cell*.](https://www.cell.com/cell/abstract/S0092-8674(18)30724-4)

MAGIC has been implemented in Python, Matlab, and R.

#### To get started immediately, check out our tutorials:  
##### Python  
* [Epithelial-to-Mesenchymal Transition Tutorial](http://nbviewer.jupyter.org/github/KrishnaswamyLab/MAGIC/blob/master/python/tutorial_notebooks/emt_tutorial.ipynb)  
* [Bone Marrow Tutorial](http://nbviewer.jupyter.org/github/KrishnaswamyLab/MAGIC/blob/master/python/tutorial_notebooks/bonemarrow_tutorial.ipynb)  
##### R  
* [Epithelial-to-Mesenchymal Transition Tutorial](http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/MAGIC/blob/master/Rmagic/inst/examples/emt_tutorial.html)  
* [Bone Marrow Tutorial](http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/MAGIC/blob/master/Rmagic/inst/examples/bonemarrow_tutorial.html)  






Magic reveals the interaction between Vimentin (VIM), Cadherin-1 (CDH1), and Zinc finger E-box-binding homeobox 1 (ZEB1, encoded by colors).



### Table of Contents

  * [Python](#python)
     * [Installation](#installation)
        * [Installation with pip](#installation-with-pip)
        * [Installation from GitHub](#installation-from-github)
     * [Usage](#usage)
        * [Quick Start](#quick-start)
        * [Tutorials](#tutorials)
  * [Matlab](#matlab)
     * [Instructions for the Matlab version](#instructions-for-the-matlab-version)
  * [R](#r)
     * [Installation](#installation-1)
        * [Installation from CRAN](#installation-from-cran)
        * [Installation from GitHub](#installation-from-github-1)
     * [Usage](#usage-1)
        * [Quick Start](#quick-start-1)
        * [Tutorials](#tutorials-1)
  * [Help](#help)

## Python

### Installation

#### Installation with pip

To install with `pip`, run the following from a terminal:

    pip install --user magic-impute

#### Installation from GitHub

To clone the repository and install manually, run the following from a terminal:

    git clone git://github.com/KrishnaswamyLab/MAGIC.git
    cd MAGIC/python
    python setup.py install --user

### Usage

#### Quick Start

The following code runs MAGIC on test data located in the MAGIC repository.

    import magic
    import pandas as pd
    import matplotlib.pyplot as plt
    X = pd.read_csv("MAGIC/data/test_data.csv")
    magic_operator = magic.MAGIC()
    X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
    plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
    plt.show()
    magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)

#### Tutorials

You can read the MAGIC documentation at https://magic.readthedocs.io/. We have included two tutorial notebooks on MAGIC usage and results visualization for single cell RNA-seq data.

EMT data notebook: http://nbviewer.jupyter.org/github/KrishnaswamyLab/MAGIC/blob/master/python/tutorial_notebooks/emt_tutorial.ipynb

Bone Marrow data notebook: http://nbviewer.jupyter.org/github/KrishnaswamyLab/MAGIC/blob/master/python/tutorial_notebooks/bonemarrow_tutorial.ipynb

## Matlab

### Instructions for the Matlab version
1. run_magic.m -- MAGIC imputation function
2. test_magic.m -- Shows how to run MAGIC. Also included is a function for loading 10x format data (load_10x.m)

## R

### Installation

To use MAGIC, you will need to install both the R and Python packages.

If `python` or `pip` are not installed, you will need to install them. We recommend
[Miniconda3](https://conda.io/miniconda.html) to install Python and `pip` together,
or otherwise you can install `pip` from https://pip.pypa.io/en/stable/installing/.

#### Installation from CRAN

In R, run this command to install MAGIC and all dependencies:

    install.packages("Rmagic")

In a terminal, run the following command to install the Python
repository.

    pip install --user magic-impute

#### Installation from GitHub

To clone the repository and install manually, run the following from a terminal:

    git clone git://github.com/KrishnaswamyLab/MAGIC.git
    cd MAGIC/python
    python setup.py install --user
    cd ../Rmagic
    R CMD INSTALL .

### Usage

#### Quick Start

After installing the package, MAGIC can be run by loading the library and calling `magic()`:

    library(Rmagic)
    library(ggplot2)
    data(magic_testdata)
    MAGIC_data <- magic(magic_testdata, genes=c("VIM", "CDH1", "ZEB1"))
    ggplot(MAGIC_data) +
      geom_point(aes(x=VIM, y=CDH1, color=ZEB1))

#### Tutorials

You can read the MAGIC tutorial by running `help(Rmagic::magic)`. For a working example, see the Rmarkdown tutorials at  and  or in `Rmagic/inst/examples`.

## Help

If you have any questions or require assistance using MAGIC, please contact us at .

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