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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Veröffentlicht in:PLoS computational biology Jg. 13; H. 5; S. e1005562
Hauptverfasser: Suomi, Tomi, Seyednasrollah, Fatemeh, Jaakkola, Maria K., Faux, Thomas, Elo, Laura L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.05.2017
Public Library of Science (PLoS)
Schlagworte:
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
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Beschreibung
Zusammenfassung: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).
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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