qpgraph-package           package:qpgraph           R Documentation

_T_h_e _q-_o_r_d_e_r _p_a_r_t_i_a_l _c_o_r_r_e_l_a_t_i_o_n _g_r_a_p_h _l_e_a_r_n_i_n_g _s_o_f_t_w_a_r_e, _q_p_g_r_a_p_h.

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_D_e_t_a_i_l_s:


       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:        <URL: http://functionalgenomics.upf.edu/qp>

_F_u_n_c_t_i_o_n_s:


        *  '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.

_A_u_t_h_o_r(_s):

     R. Castelo and A. Roverato

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

_R_e_f_e_r_e_n_c_e_s:

     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.

