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R package: RRLab

Package definition

The RRLab R package is a growing collection of experimental techniques and quality-of-life tools born from years of hands-on data science, modeling, and exploration. Built as a personal lab bench in R, it reflects a constant search for better ways to understand high-dimensional data, test bold ideas, and streamline the grind of daily analysis. The main interesting functions are RRFE and KDEA! Best QOL function is libraryR!

Function examples

RR-Feature extraction (RRFE)

This algorithm evaluates each possible combination of contrasts (generated by PCA) and applies seeded knn per contrast to identify the best contrast which achieved the highest classification accuracy. Then the resulted contrast is resulted and the most important features are extracted (based on this contrast).

See the example and results

Leave-p-Out Cross-Validation Differential Expression Analysis (KDEA or LpO CV DEA)

This algorithm randomly resamples the original dataset using only 80% of all samples for K folds. Each fold is processed using limma, Pvalues and LogFC is generated and stored. Median LogFC and amount of significant ocurrences are ranked and combined to a combined rank. An image is generated indicative of the variation and significance based on the collective DEA results. The Ranked features are resulted and the robust features from the image are resulted in a list.

See the example and results

RR general characteristics of data scan (RRscan)

This function will evaluate all distribution properties per feature and per sample. Also detects potential outliers based on hclust, mean+sd, var+sd(var).

See the example and results

Automatic package loader (libraryR)

Simplifies loading and installing packages from CRAN or Bioconductor.

See the example and results

Installation

First, you need to install the devtools package. You can do this from CRAN. install.packages("devtools")

RRLab relies on several other packages. Make sure these are installed: ggplot2, ggfortify, matlib, fitdistrplus, caret, limma, tidyverse, ggsci, showtext, foreach, doParallel, parallel, progress, data.table, and splitstackshape.

To install the RRLab package devtools::install_github("Rrtk2/RRLab/RRLab")

Once loaded with library(RRLab), the package automatically checks GitHub for new releases and notifies you if an update is available.

Running tests

The test suite lives in RRLab/tests/testthat. After installing the package, execute:

r testthat::test_package("RRLab")

Contributing

Contributions to RRLab are welcome. Please review our Contributing Guidelines for details on reporting issues, submitting feature requests, and proposing code changes. All contributions should adhere to our coding standards and include relevant tests.

Contact

For questions, comments, or suggestions, please contact:

• Email: ra.reijnders@maastrichtuniversity.nl

License and Authors

RRLab is released under the License.

Authors: - RRtK2 – Owner and primary contributor (see AUTHORS.md for complete details)

For a summary of changes in each release, see NEWS.md. If you use RRLab in your research, please review the guidance in the CITATION file.