TY - JOUR
T1 - statTarget
T2 - A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data
AU - Luan, Hemi
AU - Ji, Fenfen
AU - Chen, Yu
AU - Cai, Zongwei
N1 - Funding Information:
The authors would like to thank the financial supports from Hong Kong Baptist University (IRMC/13-14/03-CHE) and the National Natural Science Foundation of China (NSFC21675176 and NSFC91543202).
Publisher copyright:
© 2018 Elsevier B.V. All rights reserved.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis.
AB - Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis.
UR - http://www.scopus.com/inward/record.url?scp=85051256236&partnerID=8YFLogxK
U2 - 10.1016/j.aca.2018.08.002
DO - 10.1016/j.aca.2018.08.002
M3 - Journal article
C2 - 30253838
AN - SCOPUS:85051256236
SN - 0003-2670
VL - 1036
SP - 66
EP - 72
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -