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MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects
He Wang1,2, Kai Peng Lim1,2, Weijia Kong1,2
1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
Scientific Data
|December 2, 2023
Summary
Researchers created MultiPro, a resource of large-scale proteomics datasets, to address technical challenges in mass spectrometry. These datasets aid in developing better algorithms for data integration and batch effect correction in proteomics research.
Area of Science:
- Proteomics
- Mass Spectrometry
- Bioinformatics
Background:
- Mass spectrometry-based proteomics is vital for biological and clinical research.
- Technical challenges like data integration, missing value imputation, and batch effect correction introduce errors but are not well-studied.
- Current proteomic technologies require improved algorithms and data processing knowledge, necessitating appropriate datasets for development and benchmarking.
Purpose of the Study:
- To develop MultiPro, a comprehensive resource of large-scale proteomics datasets.
- To provide datasets with deliberate batch effects for investigating technical issues.
- To enable the exploration of inter-connections between technical factors and the development of new data processing approaches.
Main Methods:
- Developed MultiPro, a resource comprising four large-scale proteomics datasets.
- Utilized parallel accumulation-serial fragmentation in Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes.
- Incorporated balanced two-class designs with well-characterized cell lines and numerous biological/technical replicates.
Main Results:
- Created datasets enabling the investigation of data integration, missing value imputation, and batch effect correction.
- Facilitated the study of inter-connections between class and batch factors in proteomics data.
- Provided a foundation for developing and benchmarking algorithms for DDA and DIA data comparison and integration.
Conclusions:
- MultiPro addresses the need for high-quality, well-characterized proteomics datasets.
- The resource supports the advancement of algorithms for robust data processing and analysis in proteomics.
- Facilitates improved accuracy and reliability in biological and clinical proteomics research.
