mnempy

Files:

main.py contains the main nem and mnem functions and a function to simulate data

low.py contains internal helper functions

Install:

Install within its directory.

pip install -e .

Usage:

Create data from two networks with three gene, two effectors per gene and 15 samples.

import mnempy as mn

n = 3
K = 2
m = 2
s = 15

sim = mn.sim(K, n, m, s)

We extract the data object and the perturbation map (which gene has been perturbed in which sample) from the simulation object.

D = sim["D"]
rho = sim["rho"]

Finally, we use the mnem algorithm to compute the mixture and can compare to the ground truth.

result = mn.mnem(D, rho, K)
result["phis"]
sim["phis"]

Several runs are suggested, depending on noise and complexity of the ground truth. If the number of components K is unknown, several different Ks are suggested. However, the log likelihood cannot be compared directly. Some form of Information Criterion like BIC or AIC (not implemented) is necessary to select the best K.

DISCLAIMER:

This program is fully functional and computes a mixture of directed graphs based on the M&NEM algorithm (Pirkl & Beerenwinkel. 2018).

However, this program is written in python as a programming exercise and should only be used for testing purposes (feedback is appreciated). For real data analysis I recommend the more thoroughly tested R/Bioconductor package mnem (https://github.com/cbg-ethz/mnem).

References:

Pirkl, M., Beerenwinkel, N.; Single cell network analysis with a mixture of Nested Effects Models, Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i964-i971, https://doi.org/10.1093/bioinformatics/bty602.

MartinFXP/mnempy

ErrorLooks like something went wrong!

About

Resources

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Uh oh!

Languages

Error
Looks like something went wrong!

Packages