modACDC (version 2.0.1)

coVar: coVar

Description

Function to calculate ACDC covariances within a data pair for all samples

Usage

coVar(dataPair, fullData)

Value

Co-expression profile, or pairwise covariances for all samples, vector for given features

Arguments

dataPair

column indices of two genes to calculate covariance between

fullData

dataframe or matrix with samples as rows, all probes as columns; each entry should be numeric gene expression or other molecular data values

Author

Katelyn Queen,

Details

Co-expression for a single sample, s, is defined as $$c_{s,j,k} \equiv \left(g_{s,j}-\bar{g_j}\right)\left(g_{s,k}-\bar{g_k}\right)$$ where \(g_{s,j}\) denotes the expression of gene j in sample s and \(\bar{g_j}\) denotes the mean expression of gene j in all samples.

Denoting the sample size as N, coVar returns the co-expression profile across all samples: $$c_{j,k} = (c_{1,j,k}, c_{2,j,k}, ... , c_{N,j,k})$$

References

Martin P, et al. Novel aspects of PPARalpha-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study. Hepatology, in press, 2007.

Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. ACDC: a general approach for detecting phenotype or exposure associated co-expression. Frontiers in Medicine (2023) 10. doi:10.3389/fmed.2023.1118824.

Examples

#load CCA package for example dataset
library(CCA)

# load dataset
data("nutrimouse")

# run function with first two samples
coVar(dataPair = c(1, 2), 
      fullData = nutrimouse$lipid)