Description Functions Author(s) References
Estimate gene and eQTL networks from high-throughput expression and genotyping assays.
qpNrr
estimates non-rejection rates for every pair
of variables.
qpAvgNrr
estimates average non-rejection rates for
every pair of variables.
qpGenNrr
estimates generalized average non-rejection rates
for every pair of variables.
qpEdgeNrr
estimate the non-rejection rate of one
pair of variables.
qpCItest
performs a conditional independence test
between two variables given a conditioning set.
qpHist
plots the distribution of non-rejection rates.
qpGraph
obtains a qp-graph from a matrix of
non-rejection rates.
qpAnyGraph
obtains an undirected graph from a matrix of
pairwise measurements.
qpGraphDensity
calculates and plots the graph density
as function of the non-rejection rate.
qpCliqueNumber
calculates the size of the largest
maximal clique (the so-called clique number or maximum clique size) in
a given undirected graph.
qpClique
calculates and plots the size of the largest
maximal clique (the so-called clique number or maximum clique size)
as function of the non-rejection rate.
qpGetCliques
finds the set of (maximal) cliques of
a given undirected graph.
qpRndWishart
random generation for the Wishart
distribution.
qpCov
calculates the sample covariance matrix, just as
the function cov()
but returning a dspMatrix-class
object which efficiently stores such a dense symmetric matrix.
qpG2Sigma
builds a random covariance matrix from an
undrected graph. The inverse of the resulting matrix contains zeroes
at the missing edges of the given undirected graph.
qpUnifRndAssociation
builds a matrix of uniformly random
association values between -1 and +1 for all pairs of variables that
follow from the number of variables given as input argument.
qpK2ParCor
obtains the partial correlation coefficients
from a given concentration matrix.
qpIPF
performs maximum likelihood estimation of a
sample covariance matrix given the independence constraints from
an input list of (maximal) cliques.
qpPAC
estimates partial correlation coefficients and
corresponding P-values for each edge in a given undirected graph,
from an input data set.
qpPCC
estimates pairwise Pearson correlation coefficients
and their corresponding P-values between all pairs of variables from an
input data set.
qpRndGraph
builds a random undirected graph with a
bounded maximum connectivity degree on every vertex.
qpPrecisionRecall
calculates the precision-recall curve
for a given measure of association between all pairs of variables in a
matrix.
qpPRscoreThreshold
calculates the score threshold at a
given precision or recall level from a given precision-recall curve.
qpFunctionalCoherence
estimates functional coherence of
a given transcriptional regulatory network using Gene Ontology
annotations.
qpTopPairs
reports a top number of pairs of variables
according to either an association measure and/or occurring in a given
reference graph.
qpPlotNetwork
plots a network using the Rgraphviz
library.
This package provides an implementation of the procedures described in (Castelo
and Roverato, 2006, 2009) and (Tur, Roverato and Castelo, 2014).
An example of its use for reverse-engineering of
transcriptional regulatory networks from microarray data is available in the
vignette qpTxRegNet
and, the same directory, contains a pre-print of a book chapter describing the basic functionality of the package which serves the purpose of a basic users's guide. This package is a contribution to the Bioconductor
(Gentleman et al., 2004) and gR (Lauritzen, 2002) projects.
R. Castelo and A. Roverato
Castelo, R. and Roverato, A. A robust procedure for Gaussian graphical model search from microarray data with p larger than n. J. Mach. Learn. Res., 7:2621-2650, 2006.
Castelo, R. and Roverato, A. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comput. Biol. 16(2):213-227, 2009.
Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K. Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M. Rosinni, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, T.Y.H. and Zhang, J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol., 5:R80, 2004.
Lauritzen, S.L. gRaphical Models in R. R News, 3(2)39, 2002.
Tur, I., Roverato, A. and Castelo, R. Mapping eQTL networks with mixed graphical Markov models. Genetics, 198:1377-1393, 2014.
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