COBRA: Higher-order correction of persistent batch effects in correlation networks

Given a gene (co-)expression matrix and a design matrix, COBRA returns a decomposition of the covariance as a linear combination of components, one for each variable in the design matrix.

COBRA can be applied for batch correction, covariate-specific co-expression analysis, and to study the effect of various parameters on the observed patterns of co-expression.

Usage

In netbooks we provide a simple tutorial to show how to use COBRA in different applications. Check-out the R Version and Python Version. For an interactive playground click here!

COBRA is part of the Network Zoo, and the source code is available both in and netZooR and netZooPy.

Minimal examples

Python

from netZooPy.cobra import *
import numpy as np

# Generate toy data
G = 1000 # Genes
n = 100 # Samples (e.g. individuals)
q = 2 # Covariates in the design matrix

# expression of size (G, n); design matrix of size (G, q)
expression = np.random.random((G, n))
X = np.vstack(([1 for i in range(n)], np.random.rand(n))).T # The first column of X is an intercept

# Run COBRA
psi, Q, d, g = cobra(X, expression)

R

library(netZooR)

# Generate toy data
G <- 1000 # Genes
n <- 100 # Samples (e.g. individuals)
q <- 2 # Covariates in the design matrix

# expression of size (G, n); design matrix of size (G, q)
expression <- matrix(runif(G * n), nrow = G)
X <- cbind(rep(1, n), runif(n)) # The first column of X is an intercept

# Run COBRA
cobra_obj <- cobra(X, estimates)

# Extract matrix Q and covariate-specific eigenvalues psi
Q <- cobra_obj$Q
psi <- cobra_obj$psi

Structure of the repo

• data contains the data used and generated in the experiments. Note that the version on GitHub does not contain all the data. Please download them from Zenodo.
• experiments contains the source code to reproduce our experiments.
• figures are the output figures from the experiments.

Appreciation

• Marieke Kuijjer for the assistance in the ENCODE data pipeline.
• Anahita Babaie, Rebekka Burkholz, Chen Chen, Derrick DeConti, Dawn DeMeo, Tara Eicher, Viola Fanfani, Jonas Fischer, Intekhab Hossain, Camila Lopes-Ramos, Panagiotis Mandros, John Quackenbush, Enakshi Saha, Katherine Shutta, and Ava Wilson for thoughtful critiques and discussions.

Citation

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