Description Usage Arguments Details Value References See Also Examples
Estimates a probabilistic network of regulatorgene pairs from time series data via ScanBMA or iterative BMA.
1 2 3 4 5 6 
data 
A matrix whose columns correspond to variables or genes and whose
rows correspond to the observations at different time points.
The row names should be the gene names, to used in conjunction with

nTimePoints 
The number of time points at which expression measurements are available.
The number of columns in 
prior.prob 
If included as input, either a single positive fraction representing the
probability of an regulatorgene pair in the network, or else a
matrix in which the (i,j) entry is the estimated prior probability that
gene i regulates gene j. The default value is 
known 
An optional 2column matrix of known (hardcoded) regulatory relationships. 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. 
ordering 
A character string indicating the ordering to be used for the
genes or variables, referring to the options for ordering in function

nvar 
The number of topranked (see 
self 
A logical variable indicating whether or not to allow self edges in modeling. The default is to allow self edges. 
maxreg 
An optional estimate of the maximum number of regulators for any gene in the network. If provided, this is used to help reduce the amount of memory used for the computations. 
control 
A list of control variables affecting the BMA computations.
The functions 
diff0 
A logical variable indicating whether to differentiate between
edges with posterior probability of 0. This includes regulators not
included from when 
diff100 
A logical variable indicating whether to differentiate between
edges with posterior probability of 1.0. If 
verbose 
A logical variable indicating whether or not a detailed information
should be output as the computation progresses. The default value is

networkBMA
is intended for timeseries data in which there are
more variables (gene expression values) than observations (experiments).
For each gene, a linear model is fit to the expression data for all genes
at a particular time point to predict the expression of a particular gene
at the next time point. BMA is used to fit the linear model to identify
the candidate regulators (variables) in the model. The inferred network
consists of candidate regulators and their corresponding posterior
probabilities for each gene.
It is assumed that data is available for all replicates at the same set
of time points.
A network represented as a data frame in which each row corresponds to a
directed edge for which the probability is estimated to be nonzero. The
first column gives the name of the regulator, the second column gives the
name of the regulated gene, and the third column gives the estimated
probability for the regulatorgene pair. Rows are ordered by
decreasing probability estimate.
The summary
function gives the number of inferred edges at
posterior probabilities 0, .5, .75, .90, .95 and 1.0
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 Timeseries 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 timeseries data of a genotyped population, Proceedings of the National Academy of Sciences, 108(48):1943641.
K. Y. Yeung, A. E. Raftery and C. Fraley (2012),
Uncovering regulatory relationships in yeast using networkBMA
,
networkBMA
Bioconductor package vignette.
L. H. Hong, M. Wu1, A. Lee, W. C. Young, A. E. Raftery and K. Y. Yeung, FastBMA and Transitive Reduction for Gene Network Inference. [in preparation]
summary.networkBMA
,
varord
,
ScanBMAcontrol
,
iBMAcontrolLM
,
contabs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  data(dream4)
# there are a total of 5 datasets (networks) in the dream4ts10 data
network < 1
nTimePoints < length(unique(dream4ts10[[network]]$time))
edges1ts10 < networkBMA( data = dream4ts10[[network]][,(1:2)],
nTimePoints = nTimePoints, prior.prob = 0.01)
summary(edges1ts10)
edges1ts10.fastBMA < networkBMA(data=dream4ts10[[network]][,(1:2)], nTimePoints = nTimePoints,
control=fastBMAcontrol(optimize < 4))
summary(edges1ts10.fastBMA)

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 1.43)
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
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