SpectroPipeR – an R package for the downstream analysis of Spectronaut DIA-MS data

During proteome studies, researchers frequently face various challenges, some of which can be mitigated while others cannot. A prevalent issue is the bottleneck in downstream data analysis, which arises due to a limited number of bioinformaticians, rapid generation of raw data, and variations in data analysis methods or workflows.

To tackle this problem, we developed an R package named SpectroPipeR. This pipeline is designed to i) simplify data analysis tasks, significantly reduce the workload for scientists, ii) be easily expandable, iii) be user-friendly even for those with minimal bioinformatics knowledge, iv) generate standardized analysis, outputs, and reports for each project, and v) produce publication-ready tables, figures, and reports.

SpectroPipeR comprises a set of R functions that facilitate a comprehensive, fully automated, and standardized data analysis of Spectronaut DIA-MS data. These functions include ID rate summary, ON/OFF analysis, normalization, batch or covariate adjustment, iBAQ and maxLFQ quantification, multivariate analysis, peptide-centric statistical analysis (ROPECA or modified t-test), and interactive HTML report generation. The output is presented through a variety of clear graphs and tables in a well-structured folder system. The comprehensive standalone HTML report is extremely useful for existing Electronic Laboratory Notebooks (ELN) or Laboratory Information Management Systems (LIMS) to quickly obtain a project-specific overview.

SpectroPipeR consists of a global parameter setting and four analysis modules and one reporting module that are executed sequentially. This modular approach allows flexibility where specific analyses like ID- and intensity plots can be run independently or as part of the complete pipeline.

After each module execution, dynamic console feedback is provided and written to a log file to help identify errors early on. Upon completion of all modules, a comprehensive set of tables and plots categorized in different folders is generated to summarize the project from various perspectives. Beyond the analysis modules, a XIC plotting module assists the user in examining the raw data in a user-friendly manner.

In conclusion, SpectroPipeR serves as a valuable tool in proteome projects by enhancing data analysis efficiency and providing researchers with detailed insights into their experimental data.