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Evaluation of the power of DeepRTAlign to eliminate missing values

ABSTRACT: Four samples were prepared: 293T digest (73%, 91%, 97% and 99%) offset by varied proportions (27%, 9%, 3% and 1%) of E.coli digest. 10 μg of 293T and E. coli mixtures were loaded each time and the DDA data were collected from six Orbitrap Exploris 480 mass spectrometers with the same settings.

ORGANISM(S): Homo Sapiens

SUBMITTER: Cheng Chang

PROVIDER: PXD046183 | iProX | Tue Oct 17 00:00:00 BST 2023

REPOSITORIES: iProX

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DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.

Liu Yi Y Yang Yun Y Chen Wendong W Shen Feng F Xie Linhai L Zhang Yingying Y Zhai Yuanjun Y He Fuchu F Zhu Yunping Y Chang Cheng C

Nature communications 20231211 1

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has ...[more]

PMID: 38081814

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