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Liehrmann A, Delannoy E, Launay-Avon A, Gilbault E, Loudet O, Castandet B, Rigaill G. DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection. NAR Genom Bioinform 2023; 5:lqad098. [PMID: 37954572 PMCID: PMC10632193 DOI: 10.1093/nargab/lqad098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3'/5')-degradation of rRNA, mRNA and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html.
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Affiliation(s)
- Arnaud Liehrmann
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
- Laboratoire de Mathématiques et de Modélisation d’Evry (LaMME), Université d’Evry-Val-d’Essonne, UMR CNRS 8071, ENSIIE, USC INRAE, Evry,91037, France
| | - Etienne Delannoy
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
| | - Alexandra Launay-Avon
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
| | - Elodie Gilbault
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Olivier Loudet
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Benoît Castandet
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
| | - Guillem Rigaill
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
- Laboratoire de Mathématiques et de Modélisation d’Evry (LaMME), Université d’Evry-Val-d’Essonne, UMR CNRS 8071, ENSIIE, USC INRAE, Evry,91037, France
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Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe. STATS 2023. [DOI: 10.3390/stats6010007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This work presents the statistical analysis of a monthly average temperatures time series in several European cities using a state space approach, which considers models with a deterministic seasonal component and a stochastic trend. Temperature rise rates in Europe seem to have increased in the last decades when compared with longer periods. Therefore, change point detection methods, both parametric and non-parametric methods, were applied to the standardized residuals of the state space models (or some other related component) in order to identify these possible changes in the monthly temperature rise rates. All of the used methods have identified at least one change point in each of the temperature time series, particularly in the late 1980s or early 1990s. The differences in the average temperature trend are more evident in Eastern European cities than in Western Europe. The smoother-based t-test framework proposed in this work showed an advantage over the other methods, precisely because it considers the time correlation presented in time series. Moreover, this framework focuses the change point detection on the stochastic trend component.
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Chen H, Ren H, Yao F, Zou C. Data-driven selection of the number of change-points via error rate control. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1999820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hui Chen
- School of Statistics and Data Science, Nankai University, China
| | - Haojie Ren
- School of Mathematical Sciences, Shanghai Jiao Tong University, China
| | - Fang Yao
- School of Mathematical Sciences, Peking University, China
| | - Changliang Zou
- School of Statistics and Data Science, Nankai University, China
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Anastasiou A, Fryzlewicz P. Detecting multiple generalized change-points by isolating single ones. METRIKA 2021; 85:141-174. [PMID: 34054146 PMCID: PMC8142888 DOI: 10.1007/s00184-021-00821-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 04/28/2021] [Indexed: 11/12/2022]
Abstract
We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID's accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN. SUPPLEMENTARY INFORMATION The online version supplementary material available at 10.1007/s00184-021-00821-6.
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Affiliation(s)
- Andreas Anastasiou
- Department of Mathematics and Statistics, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus
| | - Piotr Fryzlewicz
- Department of Statistics, The London School of Economics and Political Science, Columbia House, Houghton Street, London, WC2A 2AE UK
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