Usage

The usage example in this section requires you to download the data files from folder data or the urnc repository first. For a description of the contents of this folder, see section Data.

For those without prior Python experience, a comprehensive guide on how to install Python and Adadmire is available at github.com/spang-lab/adadmire/docs/source/manual.pdf.

Example 1

from adadmire import admire, penalty
import numpy as np

# Load Feist et al example data into python
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)
print(lam)

# 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 (46)
print(position_cont) # Position in X
print(D_cor) # Corrected D
print(n_disc) # Number of discrete anomalies (0)
print(position_disc) # Position in D

Example 2

from adadmire import admire, place_anomalies_continuous
import numpy as np

X = np.load('data/Higuera_et_al/scaled_data_raw.npy') # continuous data
D = np.load('data/Higuera_et_al/pheno.npy') # discrete data
levels = np.load('data/Higuera_et_al/levels.npy') # levels of discrete variables

# Use original data set and create simulations by introducing artificial anomalies with various strengths
X_ano, pos = place_anomalies_continuous(X, n_ano = 1360, epsilon = np.array([0.6, 0.8, 1.0, 1.2, 1.4]))
# n_ano: how many anomalies should be introduced?
# epsilon: defines "strength" of introduced anomalies

# Define lambda sequence of penalty values
lam = penalty(X, D, min= -2.25, max = -1.5, step =0.25)

# Now detect anomalies in simulation with eps = 1.0
X_cor, n_cont, position_cont, D_cor, n_disc, position_disc = admire(X_ano[2],D,levels, lam)

Example 3

from adadmire import impute
import numpy as np

# Load data containing missing values in continuous features
X = np.load('data/Higuera_et_al/data_na_scaled.npy')

# Load data containing missing values in discrete features
D = np.load('data/Higuera_et_al/pheno_na.npy')

print(np.sum(np.isnan(X))) # 1360
print(np.sum(np.isnan(D))) # 120

levels = np.load('data/Higuera_et_al/levels.npy') # levels of discrete variables

# Define Lambda sequence
lam_zero = np.sqrt(np.log(X.shape[1] + D.shape[1]/2)/X.shape[0])
lam_seq = np.array([-1.75,-2.0,-2.25])
lam = [pow(2, x) for x in lam_seq]
lam = np.array(lam)
lam = lam_zero * lam

# Now impute with ADMIRE
X_imp, D_imp,lam_o = impute(X,D,levels,lam)
print(np.sum(np.isnan(X_imp))) # 0
print(np.sum(np.isnan(D_imp))) # 0

Data

In the directory data you can find two sub directories:

• Feist_et_al: contains data set as described in Feist et al, 2018 and Buck et al, 2023.

  • data_raw.xlsx: raw, unscaled data, contains measurements of 100 samples and 49 metabolites

  • scaled_data_raw.npy: numpy file containing scaled version of data_raw.xlsx

  • pheno_with_simulations.xlsx: pheno data corresponding to data_raw.xlsx, also contains cell stimulations

  • pheno.npy: numpy file corresponding to pheno_with_simulations.xlsx (only contains variables batch and myc)

  • levels.npy: numpy file containing the levels of the discrete variables in pheno.npy

• Higuera_et_al: contains down sampled data set from Higuera et al, 2015 as described in Buck et al, 2023.

  • data_raw.xlsx: raw, unscaled data, contains measurements of 400 samples and 68 proteins (down sampled from Higuera et al, 2015)

  • scaled_data_raw.npy: numpy file containing scaled version of data_raw.xlsx

  • pheno_.xlsx: pheno data corresponding to data_raw.xlsx

  • pheno.npy: numpy file corresponding to pheno.xlsx

  • levels.npy: numpy file containing the levels of the discrete variables in pheno.npy

  • data_na_scaled.npy: numpy file containing scaled version of data_raw.xlsx where 5% of the values are missing

  • pheno_na.npy: numpy file corresponding to pheno.xlsx with 5% of missing values included

References

Feist et al, 2018

Feist, Maren, et al. “Cooperative stat/nf-kb signaling regulates lymphoma metabolic reprogramming and aberrant got2 expression.” Nature Communications, 2018

Higuera et al, 2015

Higuera, Clara, et al. “Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome.” PLOS ONE, 2015

Buck et al, 2023

Buck, Lena et al. “Anomaly detection in mixed high dimensional molecular data” Bioinformatics, 2023