Scientists Propose a New Statistical Model for Differential Analysis of Quantitative Proteomic Data
A research team led by Dr. SHAO Zhen from CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health of Chinese Academy of Sciences proposed a new model for statistical analysis of quantitative proteomic data. This study entitled "MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes" (https://doi.org/10.1038/s41421-019-0107-9) was published in Cell Discovery on Aug. 13th, 2019.
Mass spectrometry-based proteome profiling experiments now are widely used to quantify protein expression levels across tissues and cell types, providing critical insights into the molecular changes during tissue development and diseases. However, due to many technical issues, precise comparison of quantitative proteomic data remains highly challenging.
In this study, the researchers proposed a new statistical model, termed MAP (Model-based Analysis of Proteomic data), for differential analysis of quantitative proteomic data and detecting proteins showing significant abundance changes across samples. In MAP, a novel step-by-step regression analysis was developed to directly model the impact of technical errors between the proteomic profiles under comparison, which is then used as a reference to infer the statistical significance of the expression change observed for each protein.
To test the performance of this new model, researchers applied it to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells, and found it has clearly superior performance compared to existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided at http://bioinfo.sibs.ac.cn/shaolab/MAP to facilitate its use by researchers in this field.
This work was supported by grants from the Ministry of Science and Technology, the National Science Foundation of China, and Chinese Academy of Sciences.
Workflow of MAP model to compare two iTRAQ quantitative proteomic profiles. (Image by Dr. Shao Zhen's team)
WANG Jin (Ms.)
Shanghai Institute of Nutrition and Health,
Chinese Academy of Sciences