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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| Published in: | PLoS computational biology Vol. 13; no. 5; p. e1005562 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
Public Library of Science
01.05.2017
Public Library of Science (PLoS) |
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| ISSN: | 1553-7358, 1553-734X, 1553-7358 |
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| Abstract | 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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| AbstractList | 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). 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 ( 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).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). |
| Audience | Academic |
| Author | Faux, Thomas Seyednasrollah, Fatemeh Jaakkola, Maria K. Suomi, Tomi Elo, Laura L. |
| AuthorAffiliation | 2 Department of Future Technologies, University of Turku, Turku, Finland Universite de Montreal, CANADA 1 Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland 3 Department of Mathematics and Statistics, University of Turku, Turku, Finland |
| AuthorAffiliation_xml | – name: 3 Department of Mathematics and Statistics, University of Turku, Turku, Finland – name: Universite de Montreal, CANADA – name: 2 Department of Future Technologies, University of Turku, Turku, Finland – name: 1 Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland |
| Author_xml | – sequence: 1 givenname: Tomi orcidid: 0000-0003-3639-979X surname: Suomi fullname: Suomi, Tomi – sequence: 2 givenname: Fatemeh orcidid: 0000-0002-1530-1389 surname: Seyednasrollah fullname: Seyednasrollah, Fatemeh – sequence: 3 givenname: Maria K. surname: Jaakkola fullname: Jaakkola, Maria K. – sequence: 4 givenname: Thomas orcidid: 0000-0002-3878-3368 surname: Faux fullname: Faux, Thomas – sequence: 5 givenname: Laura L. surname: Elo fullname: Elo, Laura L. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28542205$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL (2017) ROTS: An R package for reproducibility-optimized statistical testing. PLoS Comput Biol 13(5): e1005562. https://doi.org/10.1371/journal.pcbi.1005562 2017 Suomi et al 2017 Suomi et al 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL (2017) ROTS: An R package for reproducibility-optimized statistical testing. PLoS Comput Biol 13(5): e1005562. https://doi.org/10.1371/journal.pcbi.1005562 |
| Copyright_xml | – notice: COPYRIGHT 2017 Public Library of Science – notice: 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL (2017) ROTS: An R package for reproducibility-optimized statistical testing. PLoS Comput Biol 13(5): e1005562. https://doi.org/10.1371/journal.pcbi.1005562 – notice: 2017 Suomi et al 2017 Suomi et al – notice: 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL (2017) ROTS: An R package for reproducibility-optimized statistical testing. PLoS Comput Biol 13(5): e1005562. https://doi.org/10.1371/journal.pcbi.1005562 |
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| Title | ROTS: An R package for reproducibility-optimized statistical testing |
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