bigPLScox

PLS models for Cox regression with big data in R

Frédéric Bertrand and Myriam Maumy-Bertrand

https://doi.org/10.32614/CRAN.package.bigPLScox

bigPLScox provides Partial Least Squares (PLS) methods for Cox proportional hazards models, with a particular focus on high dimensional and big memory settings. The package supports classical PLS Cox methods together with accelerated C++ backends that operate directly on bigmemory::big.matrix objects.

The main design goals are:

• Efficient PLS based Cox models for large p and large n
• First class support for file backed big matrices
• Unified prediction, cross validation and diagnostic tools

Standalone benchmarking scripts that complement the vignette live under inst/benchmarks/.

The documentation website and examples are maintained by Frédéric Bertrand and Myriam Maumy.

Conference highlight. Maumy, M. and Bertrand, F. (2023).

"PLS models and their extension for big data". Conference presentation at the Joint Statistical Meetings (JSM 2023), Toronto, Ontario, Canada, Aug 5–10, 2023.

Conference highlight. Maumy, M. and Bertrand, F. (2023).

"bigPLS: Fitting and cross-validating PLS-based Cox models to censored big data". Poster at BioC2023: The Bioconductor Annual Conference, Dana-Farber Cancer Institute, Boston, MA, USA, Aug 2–4, 2023. doi:10.7490/f1000research.1119546.1.

Core modelling functions

The following families of PLS Cox estimators are available.

• coxgpls() and coxgplsDR()
  Generalised PLS Cox regression based on partial likelihood, with an optional deviance residual based variant (coxgplsDR).

• coxsgpls() and coxsgplsDR()
  Sparse PLS Cox estimators that encourage variable selection at the latent component level.

• coxspls_sgpls() and coxspls_sgplsDR()
  Structured sparse PLS Cox versions that support group information.

• DK style estimators
  coxDKgplsDR(), coxDKsgplsDR() and coxDKspls_sgplsDR() implement deviance residual based variants following the DK strategy.

All these functions come in both default and formula interfaces and have matching predict() methods with support for type = "link", "risk" and other standard Cox outputs.

Cross validation helpers are provided through:

• cv.coxgpls(), cv.coxgplsDR()
• cv.coxsgpls(), cv.coxsgplsDR()
• cv.coxspls_sgpls() and cv.coxspls_sgplsDR()
• cv.coxDKgplsDR(), cv.coxDKsgplsDR(), cv.coxDKspls_sgplsDR()

These mirror the criteria used in plsRcox and include time dependent survival metrics.

Big memory PLS Cox backends

The package offers dedicated functions for Cox PLS fits on large matrices, including file backed bigmemory::big.matrix objects.

• big_pls_cox()
  Iterative construction of PLS components for Cox models using big matrices, with optional naive sparsity through keepX.

• big_pls_cox_fast()
  High performance exact PLS Cox backend. It operates on both standard dense matrices and big.matrix inputs and is implemented entirely in C++ for speed.

• big_pls_cox_gd()
  Gradient based optimisation of the Cox partial likelihood in the latent PLS space. The method argument selects the optimisation scheme:

  • "gd" for a basic fixed step gradient descent
  • "bb" for a Barzilai Borwein step size
  • "nesterov" for Nesterov style acceleration
  • "bfgs" for a quasi Newton type update

  All optimisation methods share the same PLS scores and differ only in how the Cox coefficients are updated.

• big_pls_cox_transform()
  Low level interface that applies a trained PLS Cox transformation to new data, used internally by the prediction helpers and also exported for advanced workflows.

Cross validation for the big memory backends is provided by:

• cv.big_pls_cox()
• cv.big_pls_cox_gd()

These functions help select the number of components and compare the exact and gradient based backends.

Prediction, plots and summaries

The following S3 methods are provided for PLS Cox fits.

• predict.big_pls_cox()
  Prediction method for the original big memory PLS Cox solver.

• predict.big_pls_cox_fast()
  Unified prediction interface for exact PLS Cox fits on both dense and big matrices. Supports:

  • type = "link", "risk", "response"
  • type = "components" to return PLS scores
  • comps to select a subset of components
  • coef to supply custom Cox coefficients

• predict.big_pls_cox_gd()
  Prediction for gradient based fits that supports the same type, comps and coef arguments and uses the stored Cox fit by default.

• plot.big_pls_cox() and plot.big_pls_cox_gd()
  Simple visual summaries of component effects, often used together with deviance residual plots.

• summary.big_pls_cox(), summary.big_pls_cox_fast() and summary.big_pls_cox_gd()
  Text summaries that expose the PLS structure, number of components, and the embedded Cox fit.

• print.big_pls_cox(), print.big_pls_cox_gd() and print.summary.big_pls_cox_fast()
  Compact console output for quick inspection.

Several internal PLS models from plsRcox (for example gPLS, sPLS, sgPLS, pls.cox) also have stats::predict() methods registered in the namespace so that standard predict() calls continue to work.

Diagnostics and model selection

bigPLScox provides a range of tools for residual diagnostics, component selection and inspection of gradient based fits.

• Deviance residual tools

  • computeDR() carries out deviance residual computation and can use a pure R or C++ engine, with optional support for big matrices.
  • cox_deviance_residuals() and cox_deviance_residuals_big() implement low level deviance residuals for dense and big memory data.
  • cox_partial_deviance_big() and cox_deviance_details() expose partial deviance and internal calculations.
  • benchmark_deviance_residuals() provides a simple wrapper to compare different implementations on synthetic data.

• Component summaries

  • component_information() extracts per component information such as variance explained and effective variable usage from both big_pls_cox and big_pls_cox_gd fits.
  • select_ncomp() offers information criteria based choices for the number of components, for example AIC or BIC like rules.

• Gradient based diagnostics

  • gd_diagnostics() returns optimisation diagnostics for gradient based backends, including iteration counts, log likelihood progression, gradient norms and step sizes.

These tools are intended to complement classic survival model diagnostics such as survival::coxph() residual plots.

Utilities, data and scaling

A small number of helper functions and data objects round out the package.

• bigscale
  Scaling of big matrices that is compatible with the big memory PLS Cox workflow.

• bigSurvSGD.na.omit() and partialbigSurvSGDv0()
  Interfaces for survival stochastic gradient methods provided by the companion bigSurvSGD package.

• dataCox
  Example survival dataset used in documentation and unit tests.

The package also re exports the %*% and Arith methods used with some big matrix types.

Vignettes and learning material

Several vignettes ship with the package and are accessible once it is installed.

• Getting started with bigPLScox
• Overview of the main modelling functions and their extensions
• Big memory workflows with bigmemory matrices
• Benchmarking bigPLScox against baseline Cox implementations

Refer to the pkgdown site for rendered versions of these documents and a complete function reference:

https://fbertran.github.io/bigPLScox/

Installation

You can install the released version of bigPLScox from CRAN with:

install.packages("bigPLScox")

You can install the development version of bigPLScox from GitHub with:

# install.packages("devtools")
devtools::install_github("fbertran/bigPLScox")

Minimal example

The following minimal example uses the micro array data bundled with the package.

library(bigPLScox)
data(micro.censure)
data(Xmicro.censure_compl_imp)

Y <- micro.censure$survyear
status <- micro.censure$DC
X <- Xmicro.censure_compl_imp

set.seed(123)
fit <- coxgpls(
  Xplan = X,
  time = Y,
  status = status,
  ncomp = 4,
  ind.block.x = c(3, 10, 20)
)
#> Error in colMeans(x, na.rm = TRUE): 'x' must be numeric

summary(fit)
#> Error: object 'fit' not found

A big memory workflow uses bigmemory::big.matrix objects.

library(bigmemory)

X_big <- bigmemory::as.big.matrix(X)

fast_fit <- big_pls_cox_fast(
  X = X_big,
  time = Y,
  status = status,
  ncomp = 4
)

lp <- predict(fast_fit, newdata = X_big, type = "link")
head(lp)
#> [1] -0.4296294 -0.7809034  1.6411946 -1.3885315  1.2299486 -1.7144312

For more elaborate examples, including cross validation and comparisons between the exact and gradient based backends, see the vignettes and the scripts under inst/benchmarks.

Citation

If you use bigPLScox in scientific work, please cite the package and the associated conference material.

Maumy, M. and Bertrand, F. (2023). PLS models and their extension for big data. Joint Statistical Meetings, Toronto, Ontario, Canada.

Maumy, M. and Bertrand, F. (2023). bigPLS: Fitting and cross validating PLS based Cox models to censored big data. BioC2023, Dana Farber Cancer Institute, Boston, MA, poster contribution. doi:10.7490/f1000research.1119546.1.