Published in

Oxford University Press (OUP), Bioinformatics, 13(31), p. 2235-2237

DOI: 10.1093/bioinformatics/btv127

Links

Tools

Export citation

Search in Google Scholar

RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis

Journal article published in 2015 by Enrico Glaab, Reinhard Schneider ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

Abstract

Summary: High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.