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.We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics.Availability and implementation: Freely available at http://www.repexplore.tkContact: enrico.glaab@uni.luSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Technical noise is a common limitation in many high-throughput biological experiments
A common approach to reduce the influence of noise on the statistical analysis of omics data is to use technical replicate measurements, e.g. for mass spectrometry data collecting three technical replicates per biological sample is a typical setting
In order to enable users with limited or no programming experience to benefit from these new techniques to propagate variance information to downstream analyses, we have developed RepExplore, a webservice to analyze proteomics and metabolomics data with technical and biological replicates
Summary
Technical noise is a common limitation in many high-throughput biological experiments Both mass spectrometry devices for proteomics and metabolomics profiling as well as gene and protein microarray platforms can only provide a limited reproducibility (Chen et al, 2007; Albrethsen, 2007). A common approach to reduce the influence of noise on the statistical analysis of omics data is to use technical replicate measurements, e.g. for mass spectrometry data collecting three technical replicates per biological sample is a typical setting. In order to enable users with limited or no programming experience to benefit from these new techniques to propagate variance information to downstream analyses, we have developed RepExplore, a webservice to analyze proteomics and metabolomics data with technical and biological replicates. The software takes advantage of available replicate variance data to derive more robust and informative differential expression and abundance statistics, whisker plot and principal component analysis (PCA) visualizations for omics data interpretation. All results, including interactive ranking tables, 2D and 3D PCA visualizations, bar charts and heat maps are generated automatically within few minutes for a typical dataset
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