A Probe-Level Data File Format Used by 'aroma.affymetrix' [deprecated]

DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.)

Methods for Accessing Huge Amounts of Data [deprecated]

DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor). The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system. This way the size limit of the data is set by the file system and not the memory.)

Friendly R Startup Configuration

Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.

Sequence Globally Unique Identifier (SEGUID) Checksums

Implementation of the original Sequence Globally Unique Identifier (SEGUID) algorithm [Babnigg and Giometti (2006) ] and SEGUID v2 (< https://www.seguid.org>), which extends SEGUID v1 with support for linear, circular, single- and double-stranded biological sequences, e.g. DNA, RNA, and proteins.

Methods for Reading dChip Files

Functions for reading DCP and CDF.bin files generated by the dChip software.

Parallelize Common Functions via One Magic Function

The futurize() function transpiles calls to sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem . By combining this function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |> futurize()', 'purrr::map(xs, fcn) |> futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |> futurize()'. Other map-reduce packages that can be "futurized" are 'BiocParallel', 'plyr', 'crossmap', 'pbapply' packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. 'boot', 'caret', 'fgsea', 'fwb', 'gamlss', 'glmmTMB', 'glmnet', 'kernelshap', 'lme4', 'metafor', 'mgcv', 'partykit', 'riskRegression', 'seriation', 'shapr', 'SimDesign', 'strucchange', 'tm', 'TSP', and 'vegan') and on Bioconductor (e.g. 'DESeq2', 'GenomicAlignments', 'GSVA', 'Rsamtools', 'scater', 'scuttle', 'SingleCellExperiment', and 'sva').

Copy-Number Analysis of Large Microarray Data Sets

Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.

Test Suite for 'Future API' Backends

Backends implementing the 'Future' API , as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.

Get the Same, Personal, Free 'TCP' Port over and over

An R implementation of the cross-platform, language-independent "port4me" algorithm (< https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.

An Inclusive, Unifying API for Progress Updates

A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. 'lapply(...) |> futurize()' and 'purrr::map(...) |> futurize()', which uses future.apply::future_lapply() and furrr::future_map() internally. The package is compatible with Shiny applications.