vignette: The subsets of the yeast-rapamycin time-series data from...

In networkBMA: Regression-based network inference using Bayesian Model Averaging

Description

Contains four data objects used to illustrate the networkBMA package in the accompanying vignette.

brem.data

An 85 by 111 subset of the data used for network inference in yeast in Brem et~al. (2002) and Brem and Kruglyak (2005). The rows correspond to genes and the columns to experiments. Provided courtesy of Rachel Brem.

referencePairs

A 2-column data frame giving 287 regulator-gene pairs among the selected set of genes reported from the literature. The known regulatory relationships are not necessarily included in the reference network.

reg.known

A 2-column matrix of known (hard-coded) regulatory relationships among the 100 gene subset. The first column gives the name of the regulator, and the second column gives the name of the target gene. The gene names should be consistent with the data.

reg.prob

A 100 by 100 matrix in which the (i,j) entry is the estimated prior probability that gene i regulates gene j (from Lo et al. 2011).

timeSeries

A data frame in which the first two columns are factors identifying the replicate and time (in minutes) after drug perturbation, and the remaining 100 columns are the expression measurements for a subset of 100 genes from the yeast-rapamycin experiment described in Yeung et al. (2011).

Usage

data(vignette)

References

R. B. Brem and G. Yvert and R. Clinton and L. Kruglyak (2002), Genetic dissection of transcriptional regulation in budding yeast, Science 296(5568):752-755.

R. B. Brem and L. Kruglyak (2005), The landscape of genetic complexity across 5,700 gene expression traits in yeast, Proceedings of the National Academy of Sciences 102(5):1572-1577.

K. Lo, A. E. Raftery, K. M. Dombek, J. Zhu, E. E. Schadt, R. E. Bumgarner and K. Y. Yeung (2012), Integrating External Biological Knowledge in the Construction of Regulatory Networks from Time-series Expression Data, BMC Systems Biology, 6:101.

K. Y. Yeung, K. M. Dombek, K. Lo, J. E. Mittler, J. Zhu, E. E. Schadt, R. E. Bumgarner and A. E. Raftery (2011), Construction of regulatory networks using expression time-series data of a genotyped population, Proceedings of the National Academy of Sciences, 108(48):19436-41.

See Also

iterateBMAlm

Examples

data(vignette)

gene <- "YNL037C"                                                              
variables <- which(rownames(brem.data) != gene)                               
control <- iBMAcontrolLM(OR = 50, nbest = 20, thresProbne0 = 5)               
iBMAmodel.YNL037C <- iterateBMAlm( x = t(brem.data[variables,]),               
                           y = unlist(brem.data[gene,]), control = control)   

Example output

Loading required package: BMA
Loading required package: survival
Loading required package: leaps
Loading required package: robustbase

Attaching package: 'robustbase'

The following object is masked from 'package:survival':

    heart

Loading required package: inline
Loading required package: rrcov
Scalable Robust Estimators with High Breakdown Point (version 1.4-7)

Loading required package: Rcpp

Attaching package: 'Rcpp'

The following object is masked from 'package:inline':

    registerPlugin

Loading required package: RcppArmadillo
Loading required package: RcppEigen

Attaching package: 'RcppEigen'

The following objects are masked from 'package:RcppArmadillo':

    fastLm, fastLmPure

networkBMA documentation built on Jan. 28, 2021, 2:02 a.m.