Mass spectrometry-based proteomics has become increasingly crucial in biological and clinical research. However, technical challenges such as data integration, missing values, and batch effects have posed significant obstacles to accurate analysis and interpretation of proteomic datasets. To address these issues, a groundbreaking resource called MultiPro (Multi-purpose Proteome Resource) has been developed.
MultiPro consists of four comprehensive large-scale proteomics datasets deliberately designed to incorporate batch effects. These datasets utilize the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset features a balanced two-class design based on well-characterized and extensively studied cell lines, including A549 vs K562 or HCC1806 vs HS578T. Moreover, the datasets encompass a total of 48 or 36 biological and technical replicates, enabling a thorough investigation of various technical issues.
The significance of MultiPro lies in its ability to provide researchers with appropriate proteomics datasets for exploration, investigation, and benchmarking. By examining the inter-connections between class and batch factors, researchers can develop approaches to compare and integrate data from DDA and DIA platforms. Additionally, the availability of these comprehensive datasets allows for the development of improved algorithms and data processing techniques, critical for overcoming technical challenges in proteomic research.
Proteomic data, generated through mass spectrometry, plays a crucial role in biomedical research by providing direct information on proteins and their functionalities. Unlike gene expression platforms, proteomics provides valuable insights into mechanisms and phenotypes. However, the diverse nature of proteomic technologies, including different acquisition modes and quantitation strategies, has led to challenges in data integration and missing value imputation. Furthermore, batch effects resulting from instrument throughput issues can confound the true biological signals and reduce the reliability of statistical analysis.
MultiPro aims to bridge these gaps by supplying researchers with benchmark datasets specifically designed for algorithm development, evaluation, and a deeper understanding of technical and biological phenomena. The resource includes datasets suitable for investigating missing value imputation (MVI) and batch effect correction (BEC) using state-of-the-art algorithms. By applying these algorithms to the rich MultiPro datasets, researchers can advance their knowledge and improve the reliability of downstream statistical analysis.
One of the key advantages of MultiPro is its deliberate design to incorporate technical replicates and platform-specific issues. Unlike previous benchmark datasets, MultiPro offers a wider range of missing structures and investigates potential confounders between MVI and BEC. It also includes cross-platform/cross-mode datasets, allowing researchers to compare and integrate data generated through different proteomic platforms. Additionally, MultiPro incorporates new promising technologies such as PASEF (Parallel Accumulation-Serial Fragmentation), which provides significant gains in sensitivity, coverage, and reproducibility.
With MultiPro, researchers now have access to a valuable resource that addresses the challenges of data integration, missing values, and batch effects in proteomic research. By utilizing the comprehensive datasets and advanced algorithms, proteomic research can be conducted with greater accuracy and reliability. This, in turn, will contribute to advancements in phenotype correlation, biomarker and drug discovery, and ultimately, improved therapeutics. MultiPro represents a significant step forward in proteomic research and will undoubtedly pave the way for further breakthroughs in biomedical science.
