Deep denoising autoencoder-assisted continuous scoring of peak quality in high-resolution LC−MS data
Accurate peak picking is a challenging but fundamental problem in LC-MS-based omics analysis. Previous efforts mainly focused on continuous wavelet transform (CWT) based peak detection, which is highly sensitive while suffers from unsatisfactory precision. Recently proposed deep learning (DL) based...
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| Published in: | Chemometrics and intelligent laboratory systems Vol. 231; p. 104694 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.12.2022
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| ISSN: | 0169-7439, 1873-3239 |
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| Abstract | Accurate peak picking is a challenging but fundamental problem in LC-MS-based omics analysis. Previous efforts mainly focused on continuous wavelet transform (CWT) based peak detection, which is highly sensitive while suffers from unsatisfactory precision. Recently proposed deep learning (DL) based peak classifiers improve the performance significantly. However, their classification strategy loses the continuous criterion for controlling the false positive rate flexibly. Here we put forward AutoMS, which employs a deep learning-based denoising autoencoder to grasp the common characteristics of chromatographic peaks, and predict noise-deducted peaks from the original peak profiles. By comparing the difference before and after processed, it scores the peak quality continuously and precisely. From the evaluating result, AutoMS improved the accuracy for peak picking. AutoMS integrates HPIC for ROI extraction in order to accept raw data directly and output quantitative results. It also supports peak lists obtained from other tools with little adjustment. AutoMS is open source and available at https://github.com/hcji/AutoMS.
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•AutoMS can grasp the common characteristics and predicting noise-deducted signals from raw chromatographic profiles.•AutoMS Evaluates peak qualities based on the difference between the raw chromatographic profiles and the predicted noise-free signals.•AutoMS Provides continuous and objective criterion for estimating good and poor peaks.•AutoMS Accepts raw files and output quantitative peak lists with little manual operation. |
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| AbstractList | Accurate peak picking is a challenging but fundamental problem in LC-MS-based omics analysis. Previous efforts mainly focused on continuous wavelet transform (CWT) based peak detection, which is highly sensitive while suffers from unsatisfactory precision. Recently proposed deep learning (DL) based peak classifiers improve the performance significantly. However, their classification strategy loses the continuous criterion for controlling the false positive rate flexibly. Here we put forward AutoMS, which employs a deep learning-based denoising autoencoder to grasp the common characteristics of chromatographic peaks, and predict noise-deducted peaks from the original peak profiles. By comparing the difference before and after processed, it scores the peak quality continuously and precisely. From the evaluating result, AutoMS improved the accuracy for peak picking. AutoMS integrates HPIC for ROI extraction in order to accept raw data directly and output quantitative results. It also supports peak lists obtained from other tools with little adjustment. AutoMS is open source and available at https://github.com/hcji/AutoMS.
[Display omitted]
•AutoMS can grasp the common characteristics and predicting noise-deducted signals from raw chromatographic profiles.•AutoMS Evaluates peak qualities based on the difference between the raw chromatographic profiles and the predicted noise-free signals.•AutoMS Provides continuous and objective criterion for estimating good and poor peaks.•AutoMS Accepts raw files and output quantitative peak lists with little manual operation. |
| ArticleNumber | 104694 |
| Author | Ji, Hongchao Tian, Jing |
| Author_xml | – sequence: 1 givenname: Hongchao orcidid: 0000-0002-7364-0741 surname: Ji fullname: Ji, Hongchao email: jihongchao@caas.cn – sequence: 2 givenname: Jing surname: Tian fullname: Tian, Jing |
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| CitedBy_id | crossref_primary_10_1084_jem_20250444 crossref_primary_10_3390_molecules28176406 crossref_primary_10_1016_j_trac_2025_118243 crossref_primary_10_3390_ijms252111847 crossref_primary_10_1016_j_fitote_2025_106385 crossref_primary_10_1016_j_sampre_2025_100186 |
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