MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer’s Biomarker Data

In the rapidly moving proteomics field, a diverse patchwork
of
data analysis pipelines and algorithms for data normalization and
differential expression analysis is used by the community. We generated
a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates
both popular and recently developed algorithms for normalization and
statistical analyses. Additional algorithms can be easily added in
the future as plugins. MS-DAP is open-source and facilitates transparent
and reproducible proteome science by generating extensive data visualizations
and quality reporting, provided as standardized PDF reports. Second,
we performed a systematic evaluation of methods for normalization
and statistical analysis on a large variety of data sets, including
additional data generated in this study, which revealed key differences.
Commonly used approaches for differential testing based on moderated
t-statistics were consistently outperformed by more recent statistical
models, all integrated in MS-DAP. Third, we introduced a novel normalization
algorithm that rescues deficiencies observed in commonly used normalization
methods. Finally, we used the MS-DAP platform to reanalyze a recently
published large-scale proteomics data set of CSF from AD patients.
This revealed increased sensitivity, resulting in additional significant
target proteins which improved overlap with results reported in related
studies and includes a large set of new potential AD biomarkers in
addition to previously reported.