Functions for detecting anomalies in molecular data sets using Mixed Graphical Models.
Enter the following commands in a shell like bash, zsh or powershell:
pip install -U adadmireThe usage example in this section requires that you first download the data files from the data folder. For a description of the contents of this folder, see section Data of the adadmire documentation site.
from adadmire import admire, penalty
import numpy as np
# Load example data
X = np.load('data/Feist_et_al/scaled_data_raw.npy') # continuous data
D = np.load('data/Feist_et_al/pheno.npy') # discrete data
levels = np.load('data/Feist_et_al/levels.npy') # levels of discrete variables
# Define lambda sequence of penalty values
lam = penalty(X, D, min= -2.25, max = -1.5, step =0.25)
# Get anomalies in continuous and discrete data
X_cor, n_cont, position_cont, D_cor, n_disc, position_disc = admire(X, D, levels, lam)
print(X_cor) # corrected X
print(n_cont) # number of continuous anomalies
print(position_cont) # position in X
print(D_cor) # corrected D
print(n_disc) # number of discrete anomalies
print(position_disc) # position in DYou can find more usage examples in the Usage section of adadmire's documentation site.
You can find the full documentation for adadmire at spang-lab.github.io/adadmire. Amongst others, it includes chapters about: