q-order partial correlation graphs, or qp-graphs for short, are undirected Gaussian graphical Markov models built from q-order partial correlations. They are useful for learning undirected graphical Gaussian Markov models from data sets where the number of random variables p exceeds the available sample size n as, for instance, in the case of microarray data where they can be employed to reverse engineer a molecular regulatory network.
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.
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.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. (2002). gRaphical Models in R. R News, 3(2)39.