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hetNSC

Heteroscedastic nearest shrunken centroid (NSC) classification for high-dimensional, low-sample-size data such as gene expression.

The package implements six classifiers:

Label	Thresholding	Variance in test statistic and discriminant
STh	soft	pooled: the original PAM (Tibshirani et al. 2002)
HTh	hard	pooled (Sahtout, Wang and Ghimire 2024)
OTh	order	pooled (Sahtout, Wang and Ghimire 2024)
STh3	soft	class-specific (Sahtout and Wang)
HTh3	hard	class-specific (Sahtout and Wang)
OTh3	order	class-specific (Sahtout and Wang)

The threshold is chosen by the refined (“deep”) cross-validation search of Sahtout, Wang and Ghimire (2024).

Installation

# from a local copy of the source package
install.packages("hetNSC_0.1.0.tar.gz", repos = NULL, type = "source")

# or, once it is on GitHub
# remotes::install_github("<user>/hetNSC")

The package only depends on base R and MASS.

Quick start

Expression matrices have genes in rows and samples in columns (as in pamr).

library(hetNSC)

set.seed(8900)
dat <- simulate_hetero_data(ntr1 = 15, ntr2 = 20, nte1 = 50, nte2 = 50,
                            p = 1000, a = 14)

fit <- hnsc(dat$xtr, dat$ytr, method = "HTh3")   # or method = "hard", variance = "hetero"
fit
pred <- predict(fit, dat$xte)                    # class labels
mean(pred != dat$yte)                            # test error
predict(fit, dat$xte, type = "posterior")        # posterior probabilities
fit$selected.genes                               # genes used

Main functions

Function	Purpose
`hnsc()`	Fit a classifier with the threshold selected by deep CV
`predict()`	Class labels, posterior probabilities, shrunken centroids or selected genes
`hnsc_train()`, `hnsc_cv()`, `hnsc_select()`	Threshold grid fit, cross-validation, deep CV search
`compare_nsc()`	Repeated-run comparison of homoscedastic vs heteroscedastic versions (protocol of the paper); `summary()` and `recall_table()` summarise it
`simulate_hetero_data()`	Correlated-gene simulation designs (class B only, or both classes)
`compute_rho2()`	Relative heterogeneity and bias-variance crossover check (Section 2.4)
`make_cm()`, `per_class_recall()`, `balanced_acc()`	Class-level metrics
`hnsc_methods()`	Table of the six classifiers

See vignette("hetNSC") for a walk-through, and system.file("scripts", "simulation_correlated.R", package = "hetNSC") for the full correlated-gene simulation studies.

Reproducibility

The original research scripts are kept in inst/legacy. For the same random seed the package returns the same selected thresholds, cross-validation errors, test errors, numbers of genes and posterior probabilities as those scripts; tests/testthat/test-legacy.R checks this.

Citation

citation("hetNSC")

Sahtout, M. O. and Wang, H. Heteroscedastic nearest shrunken centroid classification: Methods and applications to high-dimensional data. Statistical Applications in Genetics and Molecular Biology (accepted).
Sahtout, M. O., Wang, H. and Ghimire, S. (2024). Different thresholding methods on nearest shrunken centroid algorithm. Communications in Statistics - Simulation and Computation, 53(3), 1444-1460. https://doi.org/10.1080/03610918.2022.2047201

License

GPL-2 (GNU General Public License, version 2 only; see LICENSE). This is the licence of the R package pamr by Hastie, Tibshirani, Narasimhan and Chu, from which parts of the code are adapted.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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