qpgraph-package {qpgraph} | R Documentation

## The q-order partial correlation graph learning software, qpgraph.

### Description

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.

### Details

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

### Functions

- `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.

### Author(s)

R. Castelo and A. Roverato

Maintainer: R. Castelo <robert.castelo@upf.edu>

### References

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.

[Package qpgraph version 1.0.0 Index]