Package {EDOtrans}

Type: Package
Title: Euclidean Distance-Optimized Data Transformation
Version: 0.3.5
Maintainer: Jorn Lotsch <j.lotsch@em.uni-frankfurt.de>
Description: A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
Imports: methods, stats, utils, cABCanalysis, opGMMassessment, Rcpp (≥ 1.0.0)
URL: https://github.com/JornLotsch/EDOtrans
Depends: R (≥ 3.5.0)
NeedsCompilation: yes
Packaged: 2026-05-21 08:05:44 UTC; joern
Author: Jorn Lotsch ORCID iD [aut, cre], Alfred Ultsch [aut]
Repository: CRAN
Date/Publication: 2026-05-21 08:40:07 UTC

Euclidean distance-optimized data transformation

Description

The package provides the necessary functions for performing the EDO data transformation.

Usage

EDOtrans(Data, Cls, PlotIt = FALSE, FitAlg = "normalmixEM", Criterion = "LR",
                     MaxModes = 8, MaxCores = getOption("mc.cores", 2L), Seed = "simple")

Arguments

Data

the data as a numerical vector (1D).

Cls

the class information, if any, as a vector of similar length as instances in the data.

PlotIt

whether to plot the fit directly.

FitAlg

which fit algorithm to use: "ClusterRGMM" = GMM from ClusterR, "densityMclust" from mclust, "DO" from DistributionOptimization (slow), "MCMC" = NMixMCMC from mixAK, or "normalmixEM" from mixtools.

Criterion

which criterion should be used to establish the number of modes from the best GMM fit: "AIC", "BIC", "FM", "GAP", "LR" (likelihood ratio test), "NbClust" (from NbClust), "SI" (Silverman).

MaxModes

for automated GMM assessment: the maximum number of modes to be tried.

MaxCores

for automated GMM assessment: the maximum number of processor cores used under Unix.

Seed

Seed value for reproducibility. Options: "auto" (complex seed recovery, slow), "simple" (fast reproducible seed, default), or integer (exact control, recommended). Use integers for systematic testing with different seeds, "simple" for general use, or "auto" when you need to maintain exact RNG state continuity. This parameter is needed because the opGMMassessment function, which is called within EDOtrans, requires a "Seed" parameter for reproducible results.

Value

Returns a list of transformed data and class assignments.

DataEDO

the EDO transformed data.

EDOfactor

the factor by which each data value has been divided.

Cls

the class information for each data instance.

Author(s)

Jorn Lotsch and Alfred Ultsch

References

Ultsch A, Lotsch J. Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans). BMC Bioinformatics. 2022 Jun 16;23(1):233, https://doi.org/10.1186/s12859-022-04769-w

Examples

## example 1
data(iris)
IrisEDOdata <- EDOtrans(Data = as.vector(iris[,1]), Cls = iris$Species, Seed = 42)

## example 2
data(iris)
IrisEDOdata <-
  EDOtrans(Data = as.vector(iris[,1]), Cls = as.integer(iris$Species), MaxModes = 1, Seed = 42)

## example 3
data(iris)
set.seed(42)
IrisEDOdata <-
  EDOtrans(Data = as.vector(iris[,1]), Cls = iris$Species)

Example data of hematologic marker expression.

Description

Data set of 4 flow cytometry-based lymphoma makers from 1559 cells from healthy subjects (class 1) and 1441 cells from lymphoma patients (class 2).

Usage

data("FACSdata")

Details

Size 3000 x 4 , stored in FACSdata$[FS,CDa,CDb,CDd] Original classes 2, stored in FACSdata$Cls

Examples

data(FACSdata)
str(FACSdata)

Example data an artificial Gaussioan mixture.

Description

Dataset of 3000 instances with 3 variables that are Gaussian mixtures and belong to classes Cls = 1, 2, or 3, with different means and standard deviations and equal weights of 0.7, 0.3, and 0.1, respectively.

Usage

data("GMMartificialData")

Details

Size 3000 x 3, stored in GMMartificialData$[Var1,Var2,Var3]

Classes 3, stored in GMMartificialData$Cls

Examples

data(GMMartificialData)
str(GMMartificialData)

Random Seed Recovery from RNG State

Description

Functions for recovering the original seed value that produced the current random number generator state. Provides both R and C++ implementations with the C++ version offering significantly improved performance for large search spaces.

Usage

get_seed(range = NULL, fallback_seed = 42, max_search = 2147483647,
  step_size = 50000, use_cpp = TRUE, ...)

get_seed_cpp(range = NULL, fallback_seed = 42, max_search = 2147483647,
  step_size = 50000, batch_size = 10000, verbose = TRUE)

Arguments

range

Optional integer vector of specific seed values to search. If provided, only these seeds will be tested instead of systematic range searching.

fallback_seed

Integer seed value to return if no matching seed is found during the search process (default: 42).

max_search

Maximum seed value to search up to when performing systematic range searching. Must be a positive integer within the valid range for R's random number generator (default: 2147483647).

step_size

Step size for systematic range searching when no specific range is provided. Larger values speed up search but may miss the target seed if it falls between steps (default: 50000).

use_cpp

Logical; if TRUE, uses the fast C++ implementation via get_seed_cpp(). If FALSE, falls back to the slower R implementation with a warning (default: TRUE).

batch_size

Integer specifying the number of seeds to process in each C++ batch operation. Larger batches are more memory efficient but require more RAM. Only used in get_seed_cpp() (default: 10000).

verbose

Logical; if TRUE, prints progress information during the search process including batch progress and timing information. Only used in get_seed_cpp() (default: TRUE).

...

Additional arguments passed to get_seed_cpp() when use_cpp = TRUE.

Details

The functions work by systematically testing seed values to find one that reproduces the current RNG state stored in .Random.seed. The search process:

Performance Considerations:

The C++ implementation (get_seed_cpp()) provides significant performance improvements:

Search Strategy:

Memory Management:

The C++ implementation uses batched processing controlled by batch_size to:

Value

Returns an integer representing the seed value that reproduces the current random number generator state. If no matching seed is found within the search parameters, returns the fallback_seed value.

Note

See Also

set.seed, .Random.seed

Examples

## Basic seed recovery after generating random numbers
set.seed(123)
recovered_seed <- get_seed()
print(recovered_seed)