Integrative Bayesian Multiple Regression for Multi-Platform Biomarkers.

IntegMultiReg implements the integrative multi-regression (IMR) model of Chekouo, Stingo, Doecke and Do (2017, Biometrics) and extends it from time-to-event outcomes to continuous (Gaussian) and binary (probit) outcomes.

Given several molecular platforms measured on overlapping but partially missing sets of subjects, IMR partitions subjects into the availability subgroups of a Venn diagram, fits one regression per subgroup, and shares information across availability subgroups through

• non-local (product moment) priors on the regression coefficients, and
• a Markov random field (MRF) prior on the variable-selection indicators,

so that no subject with partially observed platforms is discarded and the same biomarkers tend to be selected across availability subgroups.

Installation

The package contains C code that links against the GNU Scientific Library (GSL), which must be installed first:

• macOS: brew install gsl
• Debian/Ubuntu: sudo apt-get install libgsl-dev
• Windows: GSL is provided by Rtools.

Once available on CRAN, install the package with:

install.packages("IntegMultiReg")

Alternatively, install a local source tarball:

install.packages("IntegMultiReg_0.1.0.tar.gz", repos = NULL, type = "source")

The CRAN checking tools checkbashisms and qpdf are not runtime dependencies. Package users do not need them. Maintainers running R CMD check --as-cran locally can install them with brew install checkbashisms qpdf on macOS or sudo apt-get install devscripts qpdf on Debian/Ubuntu.

Quick start

library(IntegMultiReg)
data("simIMR")
fit <- imr(
  platform_data_list = simIMR$platforms,
  outcome            = simIMR$outcome.binary,
  cov                = simIMR$covariates,
  type_outcome       = "binary",
  nu                 = c(-4, -3, -4),
  sample_mcmc        = c(2000, 1000),
  ssize              = 30,
  seed               = 1
)
fit                       # short summary
summary(fit)              # selected biomarkers per platform
coef(fit)                 # per-platform mPIP matrices
plot(fit, type = "selection")
plot_top_features(fit)    # ranked biomarker bar chart
predict(fit, newdata = simIMR$platforms[1:2], covariates = simIMR$covariates)
cv_imr(fit)               # fold-split predictive assessment using fitted samples

Real-data example

kircIMR is a reduced public UCSC Xena TCGA-KIRC survival example aligned with the Biometrics kidney cancer case study: mRNA expression, miRNA expression, DNA methylation, clinical covariates and right-censored survival. It is derived from public UCSC Xena TCGA-KIRC sampleMap files, not from controlled-access TCGA/GDC files, and contains only a reduced Cox-screened feature panel.

The package replaces TCGA barcodes with package-internal IDs such as KIRC001 and does not distribute a barcode mapping. Users should not attempt participant re-identification or linkage to external resources.

data("kircIMR")
sapply(kircIMR$platforms, dim)
kircIMR$model_subgroup_sizes
kirc_fit <- imr(
  kircIMR$platforms,
  kircIMR$outcome.survival,
  cov = kircIMR$covariates,
  type_outcome = "right.censored",
  nu = c(-4, -3, -4),
  sample_mcmc = c(4000, 1000),
  ssize = 30,
  seed = 1
)

See the package vignette vignette("IntegMultiReg") for a complete walk-through.

Reference

Chekouo T, Stingo FC, Doecke JD, Do K-A (2017). "A Bayesian Integrative Approach for Multi-Platform Genomic Data: A Kidney Cancer Case Study." Biometrics, 73(2), 615–624. https://doi.org/10.1111/biom.12587

When using kircIMR, please also acknowledge TCGA, the National Cancer Institute Genomic Data Commons, and UCSC Xena as the public data sources.