Posterior class probabilities, local, and adjusted IDRs.

Functions for computing posterior cluster probabilities (get.prob) in the general GMCM as well as local and adjusted irreproducibility discovery rates (get.IDR) in the special GMCM.

get.IDR(x, par, threshold = 0.05, ...)

get.prob(x, theta, ...)

Arguments

x	A `matrix` of observations where rows corresponds to features and columns to studies.
par	A vector of length 4 where `par[1]` is mixture proportion of the irreproducible component, `par[2]` is the mean value, `par[3]` is the standard deviation, and `par[4]` is the correlation of the reproducible component.
threshold	The threshold level of the IDR rate.
...	Arguments passed to `qgmm.marginal`.
theta	A list of parameters for the full model as described in `rtheta`.

Value

get.IDR returns a list of length 5 with elements:

idr

A vector of the local idr values. I.e. the posterior probability that x[i, ] belongs to the irreproducible component.

IDR

A vector of the adjusted IDR values.

The number of reproducible features at the specified threshold.

threshold

The IDR threshold at which features are deemed reproducible.

Khat

A vector signifying whether the corresponding feature is reproducible or not.

get.prob returns a matrix where entry (i,j) is the posterior probability that the observation x[i, ] belongs to cluster j.

Note

From GMCM version 1.1 get.IDR has been an internal function. Use get.prop or get.IDR instead. The function can still be accessed with GMCM:::get.idr. get.idr returns a vector where the \(i\)'th entry is the posterior probability that observation \(i\) is irreproducible. It is a simple wrapper for get.prob.

References

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., & Raghunathan, A. (2011). Parametric Characterization of Multimodal Distributions with Non-gaussian Modes. IEEE 11th International Conference on Data Mining Workshops, 2011, 286-292. doi:10.1109/ICDMW.2011.135

Author

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

Examples

set.seed(1123)

# True parameters
true.par <- c(0.9, 2, 0.7, 0.6)

# Simulation of data from the GMCM model
data <-  SimulateGMCMData(n = 1000, par = true.par, d = 2)

# Initial parameters
init.par <- c(0.5, 1, 0.5, 0.9)

# Nelder-Mead optimization
nm.par   <- fit.meta.GMCM(data$u, init.par = init.par, method = "NM")
#>   Nelder-Mead direct search function minimizer
#> function value for initial parameters = 171.783273
#>   Scaled convergence tolerance is 2.55977e-06
#> Stepsize computed as 0.219722
#> BUILD              5 227.072307 113.692422
#> EXTENSION          7 203.422655 80.536529
#> EXTENSION          9 171.783273 0.969765
#> LO-REDUCTION      11 125.403851 0.969765
#> EXTENSION         13 113.692422 -36.695436
#> EXTENSION         15 80.536529 -59.774697
#> REFLECTION        17 2.445024 -73.341823
#> LO-REDUCTION      19 0.969765 -73.341823
#> HI-REDUCTION      21 -36.695436 -73.341823
#> LO-REDUCTION      23 -44.519612 -73.341823
#> LO-REDUCTION      25 -59.774697 -73.341823
#> REFLECTION        27 -65.323185 -75.050504
#> REFLECTION        29 -67.858104 -75.735675
#> HI-REDUCTION      31 -69.377201 -75.735675
#> EXTENSION         33 -72.263424 -85.343583
#> LO-REDUCTION      35 -73.341823 -85.343583
#> EXTENSION         37 -75.050504 -90.646223
#> EXTENSION         39 -75.735675 -95.177646
#> HI-REDUCTION      41 -82.140512 -95.177646
#> LO-REDUCTION      43 -83.887908 -95.177646
#> HI-REDUCTION      45 -85.343583 -95.177646
#> REFLECTION        47 -89.413691 -96.366569
#> REFLECTION        49 -90.646223 -96.897874
#> LO-REDUCTION      51 -94.934992 -97.221745
#> REFLECTION        53 -95.177646 -97.685118
#> EXTENSION         55 -96.366569 -100.599712
#> HI-REDUCTION      57 -96.897874 -100.599712
#> REFLECTION        59 -97.221745 -100.606776
#> LO-REDUCTION      61 -97.685118 -100.606776
#> HI-REDUCTION      63 -98.649070 -100.606776
#> REFLECTION        65 -100.128410 -101.684845
#> LO-REDUCTION      67 -100.537253 -101.684845
#> EXTENSION         69 -100.599712 -101.993131
#> EXTENSION         71 -100.606776 -102.553812
#> HI-REDUCTION      73 -100.868522 -102.553812
#> LO-REDUCTION      75 -101.559228 -102.553812
#> HI-REDUCTION      77 -101.684845 -102.553812
#> LO-REDUCTION      79 -101.993131 -102.553812
#> EXTENSION         81 -102.018479 -102.717295
#> REFLECTION        83 -102.078186 -102.809142
#> LO-REDUCTION      85 -102.373072 -102.809142
#> REFLECTION        87 -102.553812 -102.982773
#> LO-REDUCTION      89 -102.705807 -102.982773
#> LO-REDUCTION      91 -102.717295 -102.982773
#> LO-REDUCTION      93 -102.809142 -102.982773
#> HI-REDUCTION      95 -102.858857 -102.982773
#> REFLECTION        97 -102.872474 -102.988904
#> REFLECTION        99 -102.914137 -103.035337
#> EXTENSION        101 -102.966506 -103.134041
#> LO-REDUCTION     103 -102.982773 -103.134041
#> LO-REDUCTION     105 -102.988904 -103.134041
#> LO-REDUCTION     107 -103.035337 -103.138305
#> REFLECTION       109 -103.108410 -103.175415
#> HI-REDUCTION     111 -103.122287 -103.175415
#> LO-REDUCTION     113 -103.134041 -103.175415
#> REFLECTION       115 -103.138305 -103.177946
#> EXTENSION        117 -103.145600 -103.202221
#> LO-REDUCTION     119 -103.165248 -103.202221
#> REFLECTION       121 -103.175415 -103.212280
#> EXTENSION        123 -103.177946 -103.233265
#> HI-REDUCTION     125 -103.192753 -103.233265
#> LO-REDUCTION     127 -103.202221 -103.233265
#> LO-REDUCTION     129 -103.207320 -103.233265
#> LO-REDUCTION     131 -103.212280 -103.233847
#> HI-REDUCTION     133 -103.226681 -103.233847
#> LO-REDUCTION     135 -103.228143 -103.233847
#> LO-REDUCTION     137 -103.229832 -103.233847
#> REFLECTION       139 -103.232052 -103.234894
#> REFLECTION       141 -103.233265 -103.234996
#> HI-REDUCTION     143 -103.233451 -103.235265
#> REFLECTION       145 -103.233847 -103.235912
#> LO-REDUCTION     147 -103.234894 -103.235912
#> HI-REDUCTION     149 -103.234996 -103.236298
#> HI-REDUCTION     151 -103.235265 -103.236298
#> LO-REDUCTION     153 -103.235825 -103.236298
#> LO-REDUCTION     155 -103.235832 -103.236298
#> LO-REDUCTION     157 -103.235912 -103.236298
#> LO-REDUCTION     159 -103.235974 -103.236298
#> REFLECTION       161 -103.235979 -103.236437
#> HI-REDUCTION     163 -103.236126 -103.236437
#> LO-REDUCTION     165 -103.236211 -103.236437
#> LO-REDUCTION     167 -103.236249 -103.236437
#> HI-REDUCTION     169 -103.236298 -103.236437
#> EXTENSION        171 -103.236342 -103.236483
#> REFLECTION       173 -103.236347 -103.236486
#> HI-REDUCTION     175 -103.236367 -103.236486
#> LO-REDUCTION     177 -103.236433 -103.236486
#> HI-REDUCTION     179 -103.236437 -103.236486
#> LO-REDUCTION     181 -103.236453 -103.236493
#> LO-REDUCTION     183 -103.236474 -103.236494
#> LO-REDUCTION     185 -103.236483 -103.236498
#> LO-REDUCTION     187 -103.236486 -103.236498
#> LO-REDUCTION     189 -103.236493 -103.236501
#> LO-REDUCTION     191 -103.236494 -103.236501
#> HI-REDUCTION     193 -103.236495 -103.236501
#> HI-REDUCTION     195 -103.236498 -103.236501
#> HI-REDUCTION     197 -103.236498 -103.236502
#> REFLECTION       199 -103.236499 -103.236503
#> LO-REDUCTION     201 -103.236500 -103.236503
#> HI-REDUCTION     203 -103.236501 -103.236503
#> Exiting from Nelder Mead minimizer
#>     205 function evaluations used

# Get IDR values
res <- get.IDR(data$u, nm.par, threshold = 0.05)

# Plot results
plot(data$u, col = res$Khat, pch = c(3,16)[data$K])