README

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)

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")

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

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")

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.