qpgraph-package {qpgraph} | R Documentation |
q-order partial correlation graphs, or qp-graphs for short, are undirected Gaussian graphical Markov models that represent 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.
Package: | qp |
Version: | 0.99.6 |
Date: | 05-02-2009 |
biocViews: | Microarray, Statistics, GraphsAndNetworks |
Suggests: | mvtnorm, graph, Rgraphviz, annotate, genefilter, org.EcK12.eg.db |
License: | GPL version 2 or newer |
URL: | http://functionalgenomics.upf.edu/qp |
qpNrr
estimates non-rejection rates for every pair
of variables.
qpAvgNrr
estimates 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.
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.
qpSampleMvnorm
samples independent observations from
a multivariate normal distribution with a given mean vector and
a given concentration matrix.
qpI2K
builds a random concentration matrix containing
zeroes on those entries associated to pairs of variables that are
disconnected on a given undirected graph.
qpK2R
obtains the partial correlation coefficients
from a given concentration matrix.
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.
qpImportNrr
imports non-rejection rates.
qpFunctionalCoherence
estimates functional coherence of
using Gene Ontology annotations.
This package provides an implementation of the procedures described in (Castelo
and Roverato, 2006, 2008). An example of its use for reverse-engineering of
transcriptional regulatory networks from microarray data is available in the
vignette qpTxRegNet
. This package is a contribution to the Bioconductor
(Gentleman et al., 2004) and gR (Lauritzen, 2002) projects.
R. Castelo and A. Roverato
Maintainer: R. Castelo <robert.castelo@upf.edu>
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., accepted, 2008.
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.