ROTS: An R package for reproducibility-optimized statistical testing

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample group...

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Vydáno v:	PLoS computational biology Ročník 13; číslo 5; s. e1005562
Hlavní autoři:	Suomi, Tomi, Seyednasrollah, Fatemeh, Jaakkola, Maria K., Faux, Thomas, Elo, Laura L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.05.2017 Public Library of Science (PLoS)
Témata:	Bioinformatics Biology Biology and Life Sciences Biotechnology Case studies Cells, Cultured Chromatography Computational Biology - methods Computer and Information Sciences Consortia Datasets Funding Gene expression Genes Genomes Humans Internet Mass Spectrometry Mass spectroscopy Methods Models, Statistical Packages Physical Sciences Proteins Proteins - chemistry Proteomics Ranking Repositories Reproducibility Reproducibility of Results Research and Analysis Methods Ribonucleic acid RNA Scientific imaging Sequence Analysis, RNA Software State of the art Statistical analysis Statistical models Statistical tests Statistics Transcription Water analysis Finland
ISSN:	1553-7358, 1553-734X, 1553-7358
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceptualization: LLE.Formal analysis: TS FS MKJ TF.Funding acquisition: LLE.Investigation: TS FS MKJ TF LLE.Methodology: TS FS LLE.Project administration: LLE.Software: TS FS LLE.Supervision: LLE.Visualization: TS FS MKJ TF.Writing – original draft: TS.Writing – review & editing: TS FS MKJ TF LLE. The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1005562