DeepSCP: utilizing deep learning to boost single-cell proteome coverage.Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage.  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.
DeepSCP
DeepSCP: utilizing deep learning to boost single-cell proteome coverage.Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage.  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.
DeepSCP
DeepSCP: utilizing deep learning to boost single-cell proteome coverage.Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage.  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.
DeepSCP