- Notifications
You must be signed in to change notification settings - Fork1
Perform preprocessing (normalization, imputation, batch effect correction, etc.) on glycomics and glycoproteomics data
License
Unknown, MIT licenses found
Licenses found
glycoverse/glyclean
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
Omics data cleaning and preprocessing is a critical yet cumbersome step.glyclean helps you perform these tasks with ease, so you can focuson the fun part: downstream analysis!
You can install the latest release of glyclean fromGitHub with:
# install.packages("remotes")remotes::install_github("glycoverse/glyclean@*release")
Or install the development version:
remotes::install_github("glycoverse/glyclean")
As data preprocessing is an essential step in omics data analysis,glyclean plays a central role in theglycoverse ecosystem. It servesas the bridge between raw experimental data (imported viaglyread) anddownstream analysis, enabling other packages likeglystats to workwith clean, analysis-ready data.
library(glyexp)library(glyclean)#>#> Attaching package: 'glyclean'#> The following object is masked from 'package:stats':#>#> aggregateexp<-real_experimentclean_exp<- auto_clean(exp)#>#> ── Normalizing data ──#>#> No QC samples found. Using default normalization method based on experiment#> type.#> Experiment type is "glycoproteomics". Using `normalize_median()`.#>#>#> ── Removing variables with too many missing values ──#>#>#>#> No QC samples found. Using all samples.#> Applying preset "discovery"...#> Total removed: 24 (0.56%) variables.#>#>#> ── Imputing missing values ──#>#>#>#> No QC samples found. Using default imputation method based on sample size.#> Sample size <= 30, using `impute_sample_min()`.#>#>#> ── Aggregating data ──#>#>#>#> Aggregating to "gfs" level#>#>#> ── Normalizing data again ──#>#>#>#> No QC samples found. Using default normalization method based on experiment#> type.#> Experiment type is "glycoproteomics". Using `normalize_median()`.#>#>#> ── Correcting batch effects ──#>#>#>#> ℹ Batch column not found in sample_info. Skipping batch correction.
Yes, that’s it! Calling the magicalauto_clean() function willautomatically perform the following steps, in the most suitable way foryour data:
- Normalization
- Missing value filtering
- Imputation
- Batch effect correction
and other steps that are necessary for downstream analysis.
About
Perform preprocessing (normalization, imputation, batch effect correction, etc.) on glycomics and glycoproteomics data
Resources
License
Unknown, MIT licenses found
Licenses found
Code of conduct
Contributing
Uh oh!
There was an error while loading.Please reload this page.
Stars
Watchers
Forks
Uh oh!
There was an error while loading.Please reload this page.