MetaMSD: meta analysis for mass spectrometry data.

Authors:
So Young Ryu
So Young Ryu
Stanford University
United States
George A Wendt
George A Wendt
University of California
Oakland | United States

PeerJ 2019 10;7:e6699. Epub 2019 Apr 10.

School of Community Health Sciences, University of Nevada - Reno, Reno, NV, United States of America.

Mass spectrometry-based proteomics facilitate disease understanding by providing protein abundance information about disease progression. For the same type of disease studies, multiple mass spectrometry datasets may be generated. Integrating multiple mass spectrometry datasets can provide valuable information that a single dataset analysis cannot provide. In this article, we introduce a meta-analysis software, MetaMSD (Meta Analysis for Mass Spectrometry Data) that is specifically designed for mass spectrometry data. Using Stouffer's or Pearson's test, MetaMSD detects significantly more differential proteins than the analysis based on the single best experiment. We demonstrate the performance of MetaMSD using simulated data, urinary proteomic data of kidney transplant patients, and breast cancer proteomic data. Noting the common practice of performing a pilot study prior to a main study, this software will help proteomics researchers fully utilize the benefit of multiple studies (or datasets), thus optimizing biomarker discovery. MetaMSD is a command line tool that automatically outputs various graphs and differential proteins with confidence scores. It is implemented in R and is freely available for public use at https://github.com/soyoungryu/MetaMSD. The user manual and data are available at the site. The user manual is written in such a way that scientists who are not familiar with R software can use MetaMSD.

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Source
https://peerj.com/articles/6699
Publisher Site
http://dx.doi.org/10.7717/peerj.6699DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462182PMC
April 2019
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References

(Supplied by CrossRef)
Comprehensive molecular portraits of human breast tumours
Cancer Genome Atlas Network et al.
Nature 2012
Statistical methods for meta-analysis
Hedges et al.
1985

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