NITPicker: selecting time points for follow-up experiments
Abstract Background The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optim...
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| Format: | Article |
| Language: | English |
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BMC
2019-04-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2717-5 |
| _version_ | 1811269779242090496 |
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| author | Daphne Ezer Joseph Keir |
| author_facet | Daphne Ezer Joseph Keir |
| author_sort | Daphne Ezer |
| collection | DOAJ |
| description | Abstract Background The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question. Results In this study, we develop a tool called NITPicker (Next Iteration Time-point Picker) for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making, while maximising information about the shape of the underlying curves. NITPicker uses ideas from the field of functional data analysis. NITPicker is available on the Comprehensive R Archive Network (CRAN) and code for drawing figures is available on Github (https://github.com/ezer/NITPicker). Conclusions NITPicker performs well on diverse real-world datasets that would be relevant for varied biological applications, including designing follow-up experiments for longitudinal gene expression data, weather pattern changes over time, and growth curves. |
| first_indexed | 2024-04-12T21:49:15Z |
| format | Article |
| id | doaj.art-f7bfd152904848b58f0482347a67ddaa |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T21:49:15Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f7bfd152904848b58f0482347a67ddaa2022-12-22T03:15:33ZengBMCBMC Bioinformatics1471-21052019-04-0120111010.1186/s12859-019-2717-5NITPicker: selecting time points for follow-up experimentsDaphne Ezer0Joseph Keir1Department of Statistics, University of WarwickDepartment of Applied Mathematics and Theoretical Physics, University of CambridgeAbstract Background The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question. Results In this study, we develop a tool called NITPicker (Next Iteration Time-point Picker) for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making, while maximising information about the shape of the underlying curves. NITPicker uses ideas from the field of functional data analysis. NITPicker is available on the Comprehensive R Archive Network (CRAN) and code for drawing figures is available on Github (https://github.com/ezer/NITPicker). Conclusions NITPicker performs well on diverse real-world datasets that would be relevant for varied biological applications, including designing follow-up experiments for longitudinal gene expression data, weather pattern changes over time, and growth curves.http://link.springer.com/article/10.1186/s12859-019-2717-5Time seriesLongitudinalExperimental designFunctional data analysisRNA-seqDynamics |
| spellingShingle | Daphne Ezer Joseph Keir NITPicker: selecting time points for follow-up experiments BMC Bioinformatics Time series Longitudinal Experimental design Functional data analysis RNA-seq Dynamics |
| title | NITPicker: selecting time points for follow-up experiments |
| title_full | NITPicker: selecting time points for follow-up experiments |
| title_fullStr | NITPicker: selecting time points for follow-up experiments |
| title_full_unstemmed | NITPicker: selecting time points for follow-up experiments |
| title_short | NITPicker: selecting time points for follow-up experiments |
| title_sort | nitpicker selecting time points for follow up experiments |
| topic | Time series Longitudinal Experimental design Functional data analysis RNA-seq Dynamics |
| url | http://link.springer.com/article/10.1186/s12859-019-2717-5 |
| work_keys_str_mv | AT daphneezer nitpickerselectingtimepointsforfollowupexperiments AT josephkeir nitpickerselectingtimepointsforfollowupexperiments |