qpgraph-package {qpgraph} | R Documentation

## Estimation of genetic and molecular regulatory networks from high-throughput genomics data

### Description

Estimate gene and eQTL networks from high-throughput expression and genotyping assays.

### Functions

- `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.

### Author(s)

R. Castelo and A. Roverato

### 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. 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.

[Package qpgraph version 2.21.2 Index]