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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.

For R and MATLAB implementations of MAGIC, see

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

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


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://
cd MAGIC/python
python install --user


Example data

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')
magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)

Interactive command line

We have included two tutorial notebooks on MAGIC usage and results visualization for single cell RNA-seq data.

EMT data notebook:

Bone Marrow data notebook:


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

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