Residential College | false |
Status | 已發表Published |
An Alternating Direction Minimization based denoising method for extracted ion chromatogram | |
Li,Tianjun1; Chen,Long1; Lu,Xiliang2 | |
2020-11 | |
Source Publication | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS |
ISSN | 0169-7439 |
Volume | 206Pages:104138 |
Abstract | Accurate extracted ion chromatograms (XIC or EIC) is of great importance for Liquid chromatography - mass spectrometry (LC-MS) based quantitative proteomics. However, current preprocessing methods for XIC mainly focus on removing the peaks of low quality. Such operations are helpful for quantitation, but also contain two potential disadvantages: one is that some valuable information may be lost after the peak removal, and the other is that the retained peak lists are still contain noise. Both of the potential disadvantages may bias the final quantitative results. To solve these problems, we proposed an Alternating Direction Minimization (ADM) based denoising framework for XIC. This framework splits the observed XIC signal into baseline (background), noise and true signal, and the true signal is extracted for further analysis. The advantage of this framework is that the inner relationships over each XIC are considered, for which means that the XIC data is handled in a global way. Also, this framework is not sensitive to the noise type, so that it can be applied to wider applications. We adopted the framework for some sample data for quantitation. The quantitative results are employed to show the performance of XIC denoising. Experimental results confirm that the proposed method provides better and more reliable quantitations. |
Keyword | Denoise Extracted Ion Chromatogram Mass Spectra Noise Reduction Proteomics |
DOI | 10.1016/j.chemolab.2020.104138 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Automation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentationmathematics |
WOS Subject | Automation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligenceinstruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability |
WOS ID | WOS:000595160800018 |
Publisher | ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS |
Scopus ID | 2-s2.0-85090732820 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Chen,Long |
Affiliation | 1.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau 2.School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China |
First Author Affilication | Faculty of Science and Technology |
Corresponding Author Affilication | Faculty of Science and Technology |
Recommended Citation GB/T 7714 | Li,Tianjun,Chen,Long,Lu,Xiliang. An Alternating Direction Minimization based denoising method for extracted ion chromatogram[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2020, 206, 104138. |
APA | Li,Tianjun., Chen,Long., & Lu,Xiliang (2020). An Alternating Direction Minimization based denoising method for extracted ion chromatogram. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 206, 104138. |
MLA | Li,Tianjun,et al."An Alternating Direction Minimization based denoising method for extracted ion chromatogram".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 206(2020):104138. |
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