Statistics and Its Interface

Volume 13 (2020)

Number 4

Meta-analysis of peptides to detect protein significance

Pages: 465 – 474

DOI: https://dx.doi.org/10.4310/SII.2020.v13.n4.a4

Authors

Yuping Zhang (Department of Statistics, University of Connecticut, Storrs, Ct., U.S.A.)

Zhengqing Ouyang (Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, Ma., U.S.A.)

Wei-Jun Qian (Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, U.S.A.)

Richard D. Smith (Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, U.S.A.)

Wing Hung Wong (Department of Statistics, Stanford University, Stanford, California, U.S.A.)

Ronald W. Davis (Stanford Genome Technology Center, Stanford University, Palo Alto, California, U.S.A.)

Abstract

Shotgun assays are widely used in biotechnologies to characterize large molecules, which are hard to be measured as a whole directly. For instance, in Liquid Chromatography–Mass Spectrometry (LC–MS) shotgun experiments, proteins in biological samples are digested into peptides, and then peptides are separated and measured. However, in proteomics study, investigators are usually interested in the performance of the whole proteins instead of those peptide fragments. In light of meta-analysis, we propose an adaptive thresholding method to select informative peptides, and combine peptide-level models to protein-level analysis. The meta-analysis procedure and modeling rationale can be adapted to data analysis of other types of shotgun assays.

Keywords

neta-analysis, adaptive thresholding, shotgun technology

Received 28 April 2019

Accepted 1 April 2020

Published 31 July 2020