Package Health Score

45 / 100

security
Security review needed
popularity
Limited
maintenance
Inactive
community
Sustainable

Popularity

Limited

GitHub Stars: 344
Forks: 97
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package magic-impute, we found that it has been starred 344 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Mar 11, 2021
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 5
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

GPL-2.0

Non-Permissive License

We noticed that this project uses a license which requires less permissive conditions such as disclosing the source code, stating changes or redistributing the source under the same license. It is advised to further consult the license terms before use.

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 30
Open PR: 1
Last Release: 4 years ago
Last Commit: 4 years ago

Further analysis of the maintenance status of magic-impute based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for magic-impute is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 20
Funding: No

With more than 10 contributors for the magic-impute repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like magic-impute is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.6

Age: 6 years
Latest Release: 4 years ago
Dependencies: 9 Direct / 25 Total
Versions: 15
Maintainers: 1
Wheels: OS Independent

Readme

Markov Affinity-based Graph Imputation of Cells (MAGIC)

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).

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

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')
plt.show()
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:

Help

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

magic-impute dependencies

future graphtools matplotlib numpy pandas scikit-learn scipy scprep tasklogger

FAQs

What is magic-impute?

MAGIC. Visit Snyk Advisor to see a full health score report for magic-impute, including popularity, security, maintenance & community analysis.

Is magic-impute popular?

The python package magic-impute receives a total of 856 weekly downloads. As such, magic-impute popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is magic-impute well maintained?

We found indications that magic-impute is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is magic-impute safe to use?

While scanning the latest version of magic-impute, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 16 November-2024, at 18:59 (UTC).

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magic-impute

MAGIC For more information about how to use this package see README

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Package Health Score

Popularity

Direct Usage Popularity

Security

Security and license risk for latest version

Non-Permissive License

Maintenance

Commit Frequency

Community

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Package

Readme

Markov Affinity-based Graph Imputation of Cells (MAGIC)

Installation

Installation with pip

Installation from GitHub

Usage

Example data

Interactive command line

Help

magic-impute dependencies

FAQs

What is magic-impute?

Is magic-impute popular?

Is magic-impute well maintained?

Is magic-impute safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

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Check your requirements.txt

Snyk Vulnerability Scanner

Readme

Markov Affinity-based Graph Imputation of Cells (MAGIC)

Installation

Installation with pip

Installation from GitHub

Usage

Example data

Interactive command line

Help

magic-impute dependencies

FAQs

What is magic-impute?

Is magic-impute popular?

Is magic-impute well maintained?

Is magic-impute safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

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