subtyper

CircleCI

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Subtype and staging analyses in disease modeling

Common tasks for subtyping include making measurements of data consistency, filtering for subjects with good/high quality data, training the subtyping algorithm, predicting the subtypes in new data and visualizing results, often over time. Many helper functions of data exploration, manuscript preparation, model checking, covariate or site/confound control etc.

This package expects population-level data frames with longitudinal data.

Installing

The pre-release version of the package can be pulled from GitHub using the devtools package:

    # install.packages("devtools")
    devtools::install_github("stnava/subtyper", build_vignettes=TRUE)
    remotes::install_github("stnava/subtyper", build_vignettes=TRUE)

or the smaller remotes package:

    remotes::install_github("stnava/subtyper", build_vignettes=TRUE)

See the file .circleci/config.yml for hints about installing the many dependencies. A few other hints below that you may encounter depending on your system, version of R, etc.

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
if (!requireNamespace("Biobase", quietly = TRUE))
  BiocManager::install("Biobase")
BiocManager::install("globaltest")
BiocManager::install("sva")
install.packages("NMF")
sudo apt install libgmp-dev
# then install ClusterR

pgenlibr

# brew install libsm
gh repo clone chrchang/plink-ng
cd plink-ng/2.0/pgenlibr/
# edit src/Makevars to include  -I/System/Volumes/Data/Users/stnava/code/extern/plink-ng/2.0/simde
R CMD INSTALL . # FIXME not on arm64 yet
remotes::install_github('jhmadsen/DDoutlier') # was booted off CRAN
install.packages( 
c("ggplot2", "caret", "ClusterR", "coca", "dCUR", "dplyr", "effectsize", "Evacluster", "flexclust", "fpc", "gaston", "ggpubr", "ggthemes", "ggstatsplot", "ggbeeswarm", "globaltest", "gridExtra", "imbalance", "mlr3", "mlr3cluster", "mlr3pipelines", "wesanderson", "Hmisc", "plyr", "data.table", "mclust", "NMF", "pheatmap", "gprofiler2", "magrittr",  "fastICA", "pgenlibr", "VarSelLCM", "visreg"))

OSX specific notes: Running R in a Clean Environment

Why Use This?

Sometimes, issues arise in R installations due to user-specific environment variables (such as LD_LIBRARY_PATH, DYLD_LIBRARY_PATH, R_LIBS, or PKG_CONFIG_PATH). These can conflict with R’s ability to find the correct system libraries or load shared objects (.so / .dylib files) during package compilation or loading.

To avoid these issues, you can launch R in a minimal, clean environment, preserving only the necessary HOME and PATH variables.

Command 

env -i HOME=$HOME PATH="/opt/homebrew/bin:/usr/bin:/bin" /opt/homebrew/bin/R

For developers

The repository includes a Makefile to facilitate some common tasks.

Running tests

$ make test. Requires the testthat package. You can also specify a specific test file or files to run by adding a “file=” argument, like $ make test file=logging. testthat::test_package() will do a regular-expression pattern match within the file names (ignoring the test- prefix and the .R file extension).

Updating documentation

$ make doc. Requires the roxygen2 package.