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 `idr`

package.

If the meta analysis of Li et al. (2011) is to be performed, the
function `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 `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 `fit.full.GMCM`

computes the maximum
likelihood estimate of the general GMCM. The function
`get.prob`

is used to estimate the class membership
probabilities of each observation.

`SimulateGMCMData`

provide easy simulation from the GMCMs.

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

Core user functions: `fit.meta.GMCM`

,
`fit.full.GMCM`

, `get.IDR`

,
`get.prob`

, `SimulateGMCMData`

,
`SimulateGMMData`

, `rtheta`

,
`Uhat`

, `choose.theta`

,
`full2meta`

, `meta2full`

Package by Li et. al. (2011): `idr`

.

Anders Ellern Bilgrau, Martin Boegsted, Poul Svante Eriksen

Maintainer: Anders Ellern Bilgrau <anders.ellern.bilgrau@gmail.com>

# 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 if (FALSE) { 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)#> iter 10 value -1213.470575 #> iter 20 value -1213.524390 #> final value -1213.524559 #> converged# Compute IDR values and classify idr <- get.IDR(uhat, par = fit) table(idr$K) # 1 = irreproducible, 2 = reproducible#> #> 1 2 #> 4136 864if (FALSE) { # See clustering results par(mfrow = c(1,2)) plot(u133VsExon, cex = 0.5, pch = 4, main = "Classified genes", col = c("tomato", "steelblue")[idr$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)") }