MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.
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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 and imputation of single cells applied to single-cell RNA sequencing data, as described in Van Dijk D et al. (2018), Recovering Gene Interactions from Single-Cell Data Using Data Diffusion, Cell https://www.cell.com/cell/abstract/S0092-8674(18)30724-4.

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


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

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 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 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.

Help

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