glyclean

Omics data cleaning and preprocessing is a critical yet cumbersome step. glyclean helps you perform these tasks with ease, so you can focus on the fun part: downstream analysis!

Installation

Install glycoverse

We recommend installing the meta-package glycoverse, which includes this package and other core glycoverse packages.

Install glyclean alone

If you don’t want to install all glycoverse packages, you can only install glyclean.

You can install the latest release of glyclean from r-universe (recommended):

# install.packages("pak")
pak::repo_add(glycoverse = "https://glycoverse.r-universe.dev")
pak::pkg_install("glyclean")

Or from GitHub:

pak::pkg_install("glycoverse/glyclean@*release")

Or install the development version (NOT recommended):

pak::pkg_install("glycoverse/glyclean")

Note: Tips and troubleshooting for the meta-package glycoverse are also applicable here: Installation of glycoverse.

Documentation

• 🚀 Get started: Here
• 📚 Reference: Here

Role in glycoverse

As data preprocessing is an essential step in omics data analysis, glyclean plays a central role in the glycoverse ecosystem. It serves as the bridge between raw experimental data (imported via glyread) and downstream analysis, enabling other packages like glystats to work with clean, analysis-ready data.

Example

library(glyexp)
library(glyclean)

exp <- real_experiment
clean_exp <- auto_clean(exp)

Yes, that’s it! Calling the magical auto_clean() function will automatically perform the following steps, in the most suitable way for your data:

• Normalization
• Missing value filtering
• Imputation
• Batch effect correction

and other steps that are necessary for downstream analysis.