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    Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics. © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email:


    Bing Wang, Yue Wang, Yu Chen, Mengmeng Gao, Jie Ren, Yueshuai Guo, Chenghao Situ, Yaling Qi, Hui Zhu, Yan Li, Xuejiang Guo. DeepSCP: utilizing deep learning to boost single-cell proteome coverage. Briefings in bioinformatics. 2022 Jul 18;23(4)

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    PMID: 35656712

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