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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.

David van Dijk, et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. 2018. Cell.

Website of David van Dijk: https://davidvandijk.squarespace.com

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

• Epithelial-to-Mesenchymal Transition Tutorial
• Bone Marrow Tutorial

• Epithelial-to-Mesenchymal Transition Tutorial
• Bone Marrow Tutorial

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

• Python
  • Installation
    • Installation with pip
    • Installation from GitHub
  • Usage
    • Quick Start
    • Tutorials
• Matlab
  • Instructions for the Matlab version
• R
  • Installation
    • Installation from CRAN
    • Installation from GitHub
  • Usage
    • Quick Start
    • Tutorials
• Help

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

pip install --user magic-impute

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

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)

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

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)

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 to install Python and pip together, or otherwise you can install pip from https://pip.pypa.io/en/stable/installing/.

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

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 .

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))

You can read the MAGIC tutorial by running help(Rmagic::magic). For a working example, see the Rmarkdown tutorials at http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/MAGIC/blob/master/Rmagic/inst/examples/bonemarrow_tutorial.html and http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/MAGIC/blob/master/Rmagic/inst/examples/emt_tutorial.html or in Rmagic/inst/examples.

If you have any questions or require assistance using MAGIC, please contact us at https://krishnaswamylab.org/get-help.