Journal ArticleDOI
WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis.
Kui Deng,Fan Zhang,Qilong Tan,Yue Huang,Wei Song,Zhiwei Rong,Zheng-Jiang Zhu,Kang Li,Zhenzi Li +8 more
TLDR
A novel algorithm, called WaveICA, which is based on the wavelet transform method with independent component analysis, as the threshold processing method to capture and remove batch effects for large-scale metabolomics data is proposed.About:
This article is published in Analytica Chimica Acta.The article was published on 2019-07-11. It has received 35 citations till now.read more
Citations
More filters
Journal ArticleDOI
MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.
TL;DR: This work introduces MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: efficient parameter optimization for peak picking; automated batch effect correction; and more accurate pathway activity prediction that offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
Journal ArticleDOI
New advances in analytical methods for mass spectrometry-based large-scale metabolomics study
TL;DR: New advances in sample pretreatment methods, nontargeted, targeted and pseudotargeted metabolic data collection techniques, and data correction methods used for MS-based large-scale metabolomics study are focused on.
Journal ArticleDOI
Evaluating and minimizing batch effects in metabolomics.
TL;DR: In this review, the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects are discussed.
Journal ArticleDOI
Lipidomes in health and disease: Analytical strategies and considerations
TL;DR: A large number of the lipids studied have low concentrations at molecular and systems levels, and these low concentrations are associated with endothelial cell death and cell reprograming in animals.
Journal ArticleDOI
NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.
Zhiwei Rong,Qilong Tan,Lei Cao,Liuchao Zhang,Kui Deng,Yue Huang,Zheng-Jiang Zhu,Zhenzi Li,Kang Li +8 more
TL;DR: A novel deep learning model, called Normalization Autoencoder (NormAE), which is based on nonlinear autoencoders (AEs) and adversarial learning, which demonstrated that using NormAE produces the best calibration results.
References
More filters
Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
TL;DR: Three methods of performing normalization at the probe intensity level are presented: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure and the simplest and quickest complete data method is found to perform favorably.
Journal ArticleDOI
Adjusting batch effects in microarray expression data using empirical Bayes methods
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Journal ArticleDOI
The wavelet transform, time-frequency localization and signal analysis
TL;DR: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied and the notion of time-frequency localization is made precise, within this framework, by two localization theorems.
Journal ArticleDOI
XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.
TL;DR: An LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching, and is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples.