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
You can install the development version of glyclean from GitHub with:
# install.packages("pak")
pak::pak("glycoverse/glyclean")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)
#>
#> Attaching package: 'glyclean'
#> The following object is masked from 'package:stats':
#>
#> aggregate
exp <- toy_experiment()
exp <- set_exp_type(exp, "glycomics")
clean_exp <- auto_clean(exp)
#> ℹ Normalizing data (Median Quotient)
#> ✔ Normalizing data (Median Quotient) [32ms]
#>
#> ℹ Removing variables with >50% missing values
#> ✔ Removing variables with >50% missing values [47ms]
#>
#> ℹ Imputing missing values
#> ℹ Sample size <= 30, using sample minimum imputation
#> ℹ Imputing missing values✔ Imputing missing values [9ms]
#>
#> ℹ Normalizing data (Total Area)
#> ℹ Detecting batch effects using ANOVA for 4 variables...
#> ℹ Normalizing data (Total Area)✔ Batch effect detection completed. 0 out of 4 variables show significant batch effects (p < 0.05).
#> ℹ Normalizing data (Total Area)ℹ Batch effects detected in 0.0% of variables (<=10%). Skipping batch correction.
#> ℹ Normalizing data (Total Area)✔ Normalizing data (Total Area) [52ms]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.