Removal of batch effects for large-scale untargeted metabolomics data based on wavelet analysis. The WaveICA R package provides a new algorithm to removing batch effects for metabolomics data. The details of this method are in the papers "WaveICA: a novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis" and "WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information."
This fork is a slightly modified WaveICA by RECETOX that includes both original WaveICA and WaveICA 2.0, and contains fewer dependencies.
You can install WaveICA Package Using the following commands:
devtools::install_github("RECETOX/WaveICA",host="https://api.github.com")
To run WaveICA, you can use the following command:
library(recetox.waveica)
features <- waveica(features_table, wf, batch, factorization, group, K, t, t2, alpha)
Alternatively, if your data contains no batch information or the samples have been measured in a single batch you can run:
library(recetox.waveica)
features <- waveica_nonbatchwise(features_table, wf, injection_order, alpha, cutoff, K)
For further information on how to use the tool please refer to our docs in this repository.
When using waveica, please cite as: Deng K, Zhang F, Tan Q, Huang Y, Song W, Rong Z, Zhu ZJ, Li K, Li Z. WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis. Anal Chim Acta. 2019 Jul 11;1061:60-69. doi: 10.1016/j.aca.2019.02.010. Epub 2019 Feb 19. PMID: 30926040.
When using waveica_nonbatchwise, please cite as: Deng K, Zhao F, Rong Z, Cao L, Zhang L, Li K, Hou Y, Zhu ZJ. WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information. Metabolomics. 2021 Sep 20;17(10):87. doi: 10.1007/s11306-021-01839-7. PMID: 34542717.
If you have any questions regarding this fork please contact RECETOX development team at GalaxyToolsDevelopmentandDeployment@space.muni.cz