Nothing
R/GMCM-package.R
In GMCM: Fast Estimation of Gaussian Mixture Copula Models
Defines functions
.onUnload
#' Fast optimization of Gaussian Mixture Copula Models
#'
#' Gaussian mixture copula models (GMCM) are a flexible class of statistical
#' models which can be used for unsupervised clustering, meta analysis, and
#' many other things. In meta analysis, GMCMs can be used to
#' quantify and identify which features which have been reproduced across
#' multiple experiments. This package provides a fast and general
#' implementation of GMCM cluster analysis and serves as an improvement and
#' extension of the features available in the \code{idr} package.
#'
#' @name GMCM-package
#' @aliases GMCM-package GMCM
#' @details If the meta analysis of Li et al. (2011) is to be performed, the
#' function \code{\link{fit.meta.GMCM}} is used to identify the maximum
#' likelihood estimate of the special Gaussian mixture copula model (GMCM)
#' defined by Li et al. (2011). The function \code{\link{get.IDR}}
#' computes the local and adjusted Irreproducible Discovery Rates defined
#' by Li et al. (2011) to determine the level of reproducibility.
#'
#' Tewari et. al. (2011) proposed using GMCMs as an general unsupervised
#' clustering tool. If such a general unsupervised clustering is needed, like
#' above, the function \code{\link{fit.full.GMCM}} computes the maximum
#' likelihood estimate of the general GMCM. The function
#' \code{\link{get.prob}} is used to estimate the class membership
#' probabilities of each observation.
#'
#' \code{\link{SimulateGMCMData}} provide easy simulation from the GMCMs.
#'
#' @author
#' Anders Ellern Bilgrau,
#' Martin Boegsted,
#' Poul Svante Eriksen
#'
#' Maintainer: Anders Ellern Bilgrau <anders.ellern.bilgrau@@gmail.com>
#' @docType package
#' @references
#' Anders Ellern Bilgrau, Poul Svante Eriksen, Jakob Gulddahl Rasmussen,
#' Hans Erik Johnsen, Karen Dybkaer, Martin Boegsted (2016). GMCM:
#' Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula
#' Models. Journal of Statistical Software, 70(2), 1-23.
#' doi:10.18637/jss.v070.i02
#'
#' Li, Q., Brown, J. B. J. B., Huang, H., & Bickel, P. J. (2011).
#' Measuring reproducibility of high-throughput experiments. The Annals of
#' Applied Statistics, 5(3), 1752-1779. doi:10.1214/11-AOAS466
#'
#' Tewari, A., Giering, M. J., & Raghunathan, A. (2011). Parametric
#' Characterization of Multimodal Distributions with Non-gaussian Modes.
#' 2011 IEEE 11th International Conference on Data Mining Workshops,
#' 286-292. doi:10.1109/ICDMW.2011.135
#' @seealso
#' Core user functions: \code{\link{fit.meta.GMCM}},
#' \code{\link{fit.full.GMCM}}, \code{\link{get.IDR}},
#' \code{\link{get.prob}}, \code{\link{SimulateGMCMData}},
#' \code{\link{SimulateGMMData}}, \code{\link{rtheta}},
#' \code{\link{Uhat}}, \code{\link{choose.theta}},
#' \code{\link{full2meta}}, \code{\link{meta2full}}
#'
#' Package by Li et. al. (2011): \code{\link[idr:idr-package]{idr}}.
#' @useDynLib GMCM
#' @importFrom Rcpp evalCpp
#' @importFrom stats approxfun cov.wt cov2cor kmeans optim rchisq rnorm runif
#' @importFrom utils flush.console
#' @examples
#' # Loading data
#' data(u133VsExon)
#'
#' # Subsetting data to reduce computation time
#' u133VsExon <- u133VsExon[1:5000, ]
#'
#' # Ranking and scaling,
#' # Remember large values should be critical to the null!
#' uhat <- Uhat(1 - u133VsExon)
#'
#' # Visualizing P-values and the ranked and scaled P-values
#' \dontrun{
#' par(mfrow = c(1,2))
#' plot(u133VsExon, cex = 0.5, pch = 4, col = "tomato", main = "P-values",
#' xlab = "P (U133)", ylab = "P (Exon)")
#' plot(uhat, cex = 0.5, pch = 4, col = "tomato", main = "Ranked P-values",
#' xlab = "rank(1-P) (U133)", ylab = "rank(1-P) (Exon)")
#' }
#'
#' # Fitting using BFGS
#' fit <- fit.meta.GMCM(uhat, init.par = c(0.5, 1, 1, 0.5), pgtol = 1e-2,
#' method = "L-BFGS", positive.rho = TRUE, verbose = TRUE)
#'
#' # Compute IDR values and classify
#' idr <- get.IDR(uhat, par = fit)
#' table(idr$K) # 1 = irreproducible, 2 = reproducible
#'
#' \dontrun{
#' # See clustering results
#' par(mfrow = c(1,2))
#' plot(u133VsExon, cex = 0.5, pch = 4, main = "Classified genes",
#' col = c("tomato", "steelblue")[idr$K],
#' xlab = "P-value (U133)", ylab = "P-value (Exon)")
#' plot(uhat, cex = 0.5, pch = 4, main = "Classified genes",
#' col = c("tomato", "steelblue")[idr$K],
#' xlab = "rank(1-P) (U133)", ylab = "rank(1-P) (Exon)")
#' }
NULL
#' Reproducibility between U133 plus 2 and Exon microarrays
#'
#' This dataset contains a \code{data.frame} of unadjusted P-values for
#' differential expression between germinal center cells and other B-cells
#' within tonsils for two different experiments. The experiments differ
#' primarily in the microarray platform used. The first column corresponds the
#' evidence from the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array.
#' The second column corresponds to the Affymetrix GeneChip Human Exon 1.0 ST
#' Array.
#' @docType data
#' @name u133VsExon
#' @details Further details can be found in Bergkvist et al. (2014) and
#' Rasmussen and Bilgrau et al. (2014).
#' @format The format of the \code{data.frame} is:
#'
#' \code{'data.frame': 19577 obs. of 2 variables:}\cr
#' \code{$ u133: num 0.17561 0.00178 0.005371 0.000669 0.655261 ...}\cr
#' \code{$ exon: num 1.07e-01 6.74e-10 1.51e-03 6.76e-05 3.36e-01 ...}\cr
#'
#' @author Anders Ellern Bilgrau <anders.ellern.bilgrau@@gmail.com>
#' @references
#' Bergkvist, Kim Steve, Mette Nyegaard, Martin Boegsted, Alexander Schmitz,
#' Julie Stoeve Boedker, Simon Mylius Rasmussen, Martin Perez-Andres et al.
#' (2014). "Validation and Implementation of a Method for Microarray Gene
#' Expression Profiling of Minor B-Cell Subpopulations in Man".
#' BMC immunology, 15(1), 3.
#'
#' Rasmussen SM, Bilgrau AE, Schmitz A, Falgreen S, Bergkvist KS, Tramm AM,
#' Baech J, Jacobsen CL, Gaihede M, Kjeldsen MK, Boedker JS, Dybkaer K,
#' Boegsted M, Johnsen HE (2015). "Stable Phenotype Of B-Cell Subsets Following
#' Cryopreservation and Thawing of Normal Human Lymphocytes Stored in a Tissue
#' Biobank." Cytometry Part B: Clinical Cytometry, 88(1), 40-49.
#' @keywords data
#' @examples
#' data(u133VsExon)
#' str(u133VsExon)
#'
#' # Plot P-values
#' plot(u133VsExon, cex = 0.5)
#'
#' # Plot ranked and scaled P-values
#' plot(Uhat(1-u133VsExon), cex = 0.5)
NULL
#' Reproducibility between Fresh and Frozen B-cell subtypes
#'
#' This dataset contains a \code{data.frame} of \eqn{t}-scores (from a Linear
#' mixed effects model) and \eqn{p}-values for
#' differential expression between pre (Im, N) and post germinal (M, PB) centre
#' cells within peripheral blood.
#' The first and second column contain the the test for the hypothesis of no
#' differentially expression between pre and post germinal cells for the
#' freshly sorted and gene profiled cells.
#' The third and fourth column contain the the test for the hypothesis of no
#' differentially expression between pre and post germinal cells for the
#' cryopreserved (frozen), thawed, sorted, and gene profiled cells.
#' The fifth and sixth column contain the the test for the hypothesis of no
#' differentially expression between fresh and frozen cells.
#' The used array type was Affymetrix Human Exon 1.0 ST microarray.
#'
#' @docType data
#' @name freshVsFrozen
#' @details Further details can be found in Rasmussen and Bilgrau et al. (2015).
#' @format The format of the \code{data.frame} is:
#'
#' \code{'data.frame': 18708 obs. of 6 variables:}\cr
#' \code{$ PreVsPost.Fresh.tstat : num -1.073 -0.381 -1.105 -0.559 -1.054 ...}\cr
#' \code{$ PreVsPost.Fresh.pval : num 0.283 0.703 0.269 0.576 0.292 ...}\cr
#' \code{$ PreVsPost.Frozen.tstat: num -0.245 -0.731 -0.828 -0.568 -1.083 ...}\cr
#' \code{$ PreVsPost.Frozen.pval : num 0.806 0.465 0.408 0.57 0.279 ...}\cr
#' \code{$ FreshVsFrozen.tstat : num 0.836 1.135 -0.221 0.191 -0.783 ...}\cr
#' \code{$ FreshVsFrozen.pval : num 0.403 0.256 0.825 0.849 0.434 ...}\cr
#'
#' @author Anders Ellern Bilgrau <anders.ellern.bilgrau@@gmail.com>
#' @references
#' Rasmussen SM, Bilgrau AE, Schmitz A, Falgreen S, Bergkvist KS, Tramm AM,
#' Baech J, Jacobsen CL, Gaihede M, Kjeldsen MK, Boedker JS, Dybkaer K,
#' Boegsted M, Johnsen HE (2015). "Stable Phenotype Of B-Cell Subsets Following
#' Cryopreservation and Thawing of Normal Human Lymphocytes Stored in a Tissue
#' Biobank." Cytometry Part B: Clinical Cytometry, 88(1), 40-49.
#' @keywords data
#' @examples
#' data(freshVsFrozen)
#' str(freshVsFrozen)
#'
#' # Plot P-values
#' plot(freshVsFrozen[,c(2,4)], cex = 0.5)
#'
#' # Plot ranked and scaled P-values
#' plot(Uhat(abs(freshVsFrozen[,c(1,3)])), cex = 0.5)
NULL
# The following ensures that the DLL is unloaded when the package is unloaded.
# See http://r-pkgs.had.co.nz/src.html
.onUnload <- function(libpath) {
library.dynam.unload("GMCM", libpath) # nocov
}
# 6f037e235ba745d55fea4baa9f32caf64ce2d336ac431578a4ce67ee6e1aebf2
Try the GMCM package in your browser
Any scripts or data that you put into this service are public.
GMCM documentation built on Nov. 6, 2019, 1:08 a.m.
R Package Documentation
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Defines functions .onUnload
#' Fast optimization of Gaussian Mixture Copula Models
#'
#' Gaussian mixture copula models (GMCM) are a flexible class of statistical
#' models which can be used for unsupervised clustering, meta analysis, and
#' many other things. In meta analysis, GMCMs can be used to
#' quantify and identify which features which have been reproduced across
#' multiple experiments. This package provides a fast and general
#' implementation of GMCM cluster analysis and serves as an improvement and
#' extension of the features available in the \code{idr} package.
#'
#' @name GMCM-package
#' @aliases GMCM-package GMCM
#' @details If the meta analysis of Li et al. (2011) is to be performed, the
#' function \code{\link{fit.meta.GMCM}} is used to identify the maximum
#' likelihood estimate of the special Gaussian mixture copula model (GMCM)
#' defined by Li et al. (2011). The function \code{\link{get.IDR}}
#' computes the local and adjusted Irreproducible Discovery Rates defined
#' by Li et al. (2011) to determine the level of reproducibility.
#'
#' Tewari et. al. (2011) proposed using GMCMs as an general unsupervised
#' clustering tool. If such a general unsupervised clustering is needed, like
#' above, the function \code{\link{fit.full.GMCM}} computes the maximum
#' likelihood estimate of the general GMCM. The function
#' \code{\link{get.prob}} is used to estimate the class membership
#' probabilities of each observation.
#'
#' \code{\link{SimulateGMCMData}} provide easy simulation from the GMCMs.
#'
#' @author
#' Anders Ellern Bilgrau,
#' Martin Boegsted,
#' Poul Svante Eriksen
#'
#' Maintainer: Anders Ellern Bilgrau <anders.ellern.bilgrau@@gmail.com>
#' @docType package
#' @references
#' Anders Ellern Bilgrau, Poul Svante Eriksen, Jakob Gulddahl Rasmussen,
#' Hans Erik Johnsen, Karen Dybkaer, Martin Boegsted (2016). GMCM:
#' Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula
#' Models. Journal of Statistical Software, 70(2), 1-23.
#' doi:10.18637/jss.v070.i02
#'
#' Li, Q., Brown, J. B. J. B., Huang, H., & Bickel, P. J. (2011).
#' Measuring reproducibility of high-throughput experiments. The Annals of
#' Applied Statistics, 5(3), 1752-1779. doi:10.1214/11-AOAS466
#'
#' Tewari, A., Giering, M. J., & Raghunathan, A. (2011). Parametric
#' Characterization of Multimodal Distributions with Non-gaussian Modes.
#' 2011 IEEE 11th International Conference on Data Mining Workshops,
#' 286-292. doi:10.1109/ICDMW.2011.135
#' @seealso
#' Core user functions: \code{\link{fit.meta.GMCM}},
#' \code{\link{fit.full.GMCM}}, \code{\link{get.IDR}},
#' \code{\link{get.prob}}, \code{\link{SimulateGMCMData}},
#' \code{\link{SimulateGMMData}}, \code{\link{rtheta}},
#' \code{\link{Uhat}}, \code{\link{choose.theta}},
#' \code{\link{full2meta}}, \code{\link{meta2full}}
#'
#' Package by Li et. al. (2011): \code{\link[idr:idr-package]{idr}}.
#' @useDynLib GMCM
#' @importFrom Rcpp evalCpp
#' @importFrom stats approxfun cov.wt cov2cor kmeans optim rchisq rnorm runif
#' @importFrom utils flush.console
#' @examples
#' # Loading data
#' data(u133VsExon)
#'
#' # Subsetting data to reduce computation time
#' u133VsExon <- u133VsExon[1:5000, ]
#'
#' # Ranking and scaling,
#' # Remember large values should be critical to the null!
#' uhat <- Uhat(1 - u133VsExon)
#'
#' # Visualizing P-values and the ranked and scaled P-values
#' \dontrun{
#' par(mfrow = c(1,2))
#' plot(u133VsExon, cex = 0.5, pch = 4, col = "tomato", main = "P-values",
#' xlab = "P (U133)", ylab = "P (Exon)")
#' plot(uhat, cex = 0.5, pch = 4, col = "tomato", main = "Ranked P-values",
#' xlab = "rank(1-P) (U133)", ylab = "rank(1-P) (Exon)")
#' }
#'
#' # Fitting using BFGS
#' fit <- fit.meta.GMCM(uhat, init.par = c(0.5, 1, 1, 0.5), pgtol = 1e-2,
#' method = "L-BFGS", positive.rho = TRUE, verbose = TRUE)
#'
#' # Compute IDR values and classify
#' idr <- get.IDR(uhat, par = fit)
#' table(idr$K) # 1 = irreproducible, 2 = reproducible
#'
#' \dontrun{
#' # See clustering results
#' par(mfrow = c(1,2))
#' plot(u133VsExon, cex = 0.5, pch = 4, main = "Classified genes",
#' col = c("tomato", "steelblue")[idr$K],
#' xlab = "P-value (U133)", ylab = "P-value (Exon)")
#' plot(uhat, cex = 0.5, pch = 4, main = "Classified genes",
#' col = c("tomato", "steelblue")[idr$K],
#' xlab = "rank(1-P) (U133)", ylab = "rank(1-P) (Exon)")
#' }
NULL
#' Reproducibility between U133 plus 2 and Exon microarrays
#'
#' This dataset contains a \code{data.frame} of unadjusted P-values for
#' differential expression between germinal center cells and other B-cells
#' within tonsils for two different experiments. The experiments differ
#' primarily in the microarray platform used. The first column corresponds the
#' evidence from the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array.
#' The second column corresponds to the Affymetrix GeneChip Human Exon 1.0 ST
#' Array.
#' @docType data
#' @name u133VsExon
#' @details Further details can be found in Bergkvist et al. (2014) and
#' Rasmussen and Bilgrau et al. (2014).
#' @format The format of the \code{data.frame} is:
#'
#' \code{'data.frame': 19577 obs. of 2 variables:}\cr
#' \code{$ u133: num 0.17561 0.00178 0.005371 0.000669 0.655261 ...}\cr
#' \code{$ exon: num 1.07e-01 6.74e-10 1.51e-03 6.76e-05 3.36e-01 ...}\cr
#'
#' @author Anders Ellern Bilgrau <anders.ellern.bilgrau@@gmail.com>
#' @references
#' Bergkvist, Kim Steve, Mette Nyegaard, Martin Boegsted, Alexander Schmitz,
#' Julie Stoeve Boedker, Simon Mylius Rasmussen, Martin Perez-Andres et al.
#' (2014). "Validation and Implementation of a Method for Microarray Gene
#' Expression Profiling of Minor B-Cell Subpopulations in Man".
#' BMC immunology, 15(1), 3.
#'
#' Rasmussen SM, Bilgrau AE, Schmitz A, Falgreen S, Bergkvist KS, Tramm AM,
#' Baech J, Jacobsen CL, Gaihede M, Kjeldsen MK, Boedker JS, Dybkaer K,
#' Boegsted M, Johnsen HE (2015). "Stable Phenotype Of B-Cell Subsets Following
#' Cryopreservation and Thawing of Normal Human Lymphocytes Stored in a Tissue
#' Biobank." Cytometry Part B: Clinical Cytometry, 88(1), 40-49.
#' @keywords data
#' @examples
#' data(u133VsExon)
#' str(u133VsExon)
#'
#' # Plot P-values
#' plot(u133VsExon, cex = 0.5)
#'
#' # Plot ranked and scaled P-values
#' plot(Uhat(1-u133VsExon), cex = 0.5)
NULL
#' Reproducibility between Fresh and Frozen B-cell subtypes
#'
#' This dataset contains a \code{data.frame} of \eqn{t}-scores (from a Linear
#' mixed effects model) and \eqn{p}-values for
#' differential expression between pre (Im, N) and post germinal (M, PB) centre
#' cells within peripheral blood.
#' The first and second column contain the the test for the hypothesis of no
#' differentially expression between pre and post germinal cells for the
#' freshly sorted and gene profiled cells.
#' The third and fourth column contain the the test for the hypothesis of no
#' differentially expression between pre and post germinal cells for the
#' cryopreserved (frozen), thawed, sorted, and gene profiled cells.
#' The fifth and sixth column contain the the test for the hypothesis of no
#' differentially expression between fresh and frozen cells.
#' The used array type was Affymetrix Human Exon 1.0 ST microarray.
#'
#' @docType data
#' @name freshVsFrozen
#' @details Further details can be found in Rasmussen and Bilgrau et al. (2015).
#' @format The format of the \code{data.frame} is:
#'
#' \code{'data.frame': 18708 obs. of 6 variables:}\cr
#' \code{$ PreVsPost.Fresh.tstat : num -1.073 -0.381 -1.105 -0.559 -1.054 ...}\cr
#' \code{$ PreVsPost.Fresh.pval : num 0.283 0.703 0.269 0.576 0.292 ...}\cr
#' \code{$ PreVsPost.Frozen.tstat: num -0.245 -0.731 -0.828 -0.568 -1.083 ...}\cr
#' \code{$ PreVsPost.Frozen.pval : num 0.806 0.465 0.408 0.57 0.279 ...}\cr
#' \code{$ FreshVsFrozen.tstat : num 0.836 1.135 -0.221 0.191 -0.783 ...}\cr
#' \code{$ FreshVsFrozen.pval : num 0.403 0.256 0.825 0.849 0.434 ...}\cr
#'
#' @author Anders Ellern Bilgrau <anders.ellern.bilgrau@@gmail.com>
#' @references
#' Rasmussen SM, Bilgrau AE, Schmitz A, Falgreen S, Bergkvist KS, Tramm AM,
#' Baech J, Jacobsen CL, Gaihede M, Kjeldsen MK, Boedker JS, Dybkaer K,
#' Boegsted M, Johnsen HE (2015). "Stable Phenotype Of B-Cell Subsets Following
#' Cryopreservation and Thawing of Normal Human Lymphocytes Stored in a Tissue
#' Biobank." Cytometry Part B: Clinical Cytometry, 88(1), 40-49.
#' @keywords data
#' @examples
#' data(freshVsFrozen)
#' str(freshVsFrozen)
#'
#' # Plot P-values
#' plot(freshVsFrozen[,c(2,4)], cex = 0.5)
#'
#' # Plot ranked and scaled P-values
#' plot(Uhat(abs(freshVsFrozen[,c(1,3)])), cex = 0.5)
NULL
# The following ensures that the DLL is unloaded when the package is unloaded.
# See http://r-pkgs.had.co.nz/src.html
.onUnload <- function(libpath) {
library.dynam.unload("GMCM", libpath) # nocov
}
# 6f037e235ba745d55fea4baa9f32caf64ce2d336ac431578a4ce67ee6e1aebf2
Try the GMCM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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