qpgraph-package: Estimation of genetic and molecular regulatory networks from...

In rcastelo/qpgraph: Estimation of genetic and molecular regulatory networks from high-throughput genomics data

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.

rcastelo/qpgraph documentation built on April 24, 2024, 5:01 p.m.