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: https://www.biorxiv.org/content/10.1101/532846v2
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.
You can use meld as follows:
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)
If you have any questions or require assistance using MELD, please contact us at https://krishnaswamylab.org/get-help