| coVar {modACDC} | R Documentation |
coVar
Description
Function to calculate ACDC covariances within a data pair for all samples
Usage
coVar(dataPair, fullData)
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 |
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})
Value
Co-expression profile, or pairwise covariances for all samples, vector for given features
Author(s)
Katelyn Queen, kjqueen@usc.edu
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)