Metadata-Version: 2.1
Name: meld
Version: 1.0.0
Summary: MELD
Home-page: https://github.com/KrishnaswamyLab/MELD
Author: Daniel Burkhardt, Krishnaswamy Lab, Yale University
Author-email: daniel.burkhardt@yale.edu
License: Dual License - See LICENSE file
Download-URL: https://github.com/KrishnaswamyLab/MELD/archive/v1.0.0.tar.gz
Description: # MELD
        ### Quantifying the effect of experimental perturbations at single-cell resolution
        
        
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        ### Quick Start
        * [**Guided tutorial in Python**](https://nbviewer.jupyter.org/github/KrishnaswamyLab/MELD/blob/master/notebooks/Wagner2018_Chordin_Cas9_Mutagenesis.ipynb).
        
        ### Introduction
        
        MELD is a Python package for quantifying the effects of experimental perturbations. For an in depth explanation of the algorithm, read our manuscript on BioRxiv.
        
        [**Quantifying the effect of experimental perturbations at single-cell resolution**. Daniel B Burkhardt\*, Jay S Stanley\*, Alexander Tong, Ana Luisa Perdigoto, Scott A Gigante, Kevan C Herold, Guy Wolf, Antonio J Giraldez, David van Dijk, Smita Krishnaswamy. BioRxiv. doi:10.1101/532846.](<https://www.biorxiv.org/content/10.1101/532846v4>)
        
        The goal of MELD is to identify populations of cells that are most affected by an experimental perturbation. Rather than clustering the data first and calculating differential abundance of samples within clusters, MELD provides a density estimate for each scRNA-seq sample for every cell in each dataset. Comparing the ratio between the density of each sample provides a quantitative estimate the effect of a perturbation at the single-cell level. We can then identify the cells most or least affected by the perturbation.
        
        ### Installation
        
        
        ```
        pip install --user meld
        ```
        
        ### Requirements
        
        MELD requires Python >= 3.6. All other requirements are installed automatically by ``pip``.
        
        ### Usage example
        
        ```
           import numpy as np
           import meld
        
           # Create toy data
           n_samples = 500
           n_dimensions = 100
           data = np.random.normal(size=(n_samples, n_dimensions))
           sample_labels = np.random.choice(['treatment', 'control'], size=n_samples)
        
           # Estimate density of each sample over the graph
           sample_densities = meld.MELD().fit_transform(data, sample_labels)
        
           # Normalize densities to calculate sample likelihoods
           sample_likelihoods = meld.utils.normalize_densities(sample_densities)
        ```
        
Keywords: big-data,manifold-learning,computational-biology
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: doc
