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Methodology
Matthias Pietzke
Beatson Institute for Cancer Research
Corresponding Author
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Background Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time consuming. As a consequence of manual data processing the results are often not analysed in full depth, so potential novel findings might get lost.
Methods To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny-application that can be run online in a web-browser on https://mpietzke.shinyapps.io/autoplotter.
Results We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis.
Conclusion With the help of Metabolite AutoPlotter it is now possible to simplify data processing and visualisation for a wide audience. High quality plots from complex data can be generated in a short time with pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further this eliminates potential copy-and paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.
Keywords
metabolites, metabolomics, processing, visualisation, graphs, plots, automatic
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CURRENT STATUS: Published
View the published article at Cancer & Metabolism.
Version 2
Posted 08 Jun, 2020
No community comments so far
Editorial decision: Accept
On 15 Jun, 2020
Editor assigned
On 03 Jun, 2020
Submission checks complete
On 02 Jun, 2020
Editor invited
On 02 Jun, 2020
No comments provided
Editorial decision: Minor revision
On 05 May, 2020
Review #2 received
Received 13 Apr, 2020
Review #1 received
Received 19 Mar, 2020
Reviewer #1 agreed
On 10 Mar, 2020
Reviewer #2 agreed
On 10 Mar, 2020
Reviewers invited
Invitations sent on 07 Mar, 2020
Editor assigned
On 20 Feb, 2020
Submission checks complete
On 19 Feb, 2020
Editor invited
On 19 Feb, 2020
First submitted
On 13 Feb, 2020
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See the published version of this preprint at Cancer & Metabolism.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Methodology
Matthias Pietzke
Beatson Institute for Cancer Research
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Cancer & Metabolism.
Version 2
Posted 08 Jun, 2020
No community comments so far
Editorial decision: Accept
On 15 Jun, 2020
Editor assigned
On 03 Jun, 2020
Submission checks complete
On 02 Jun, 2020
Editor invited
On 02 Jun, 2020
No comments provided
Editorial decision: Minor revision
On 05 May, 2020
Review #2 received
Received 13 Apr, 2020
Review #1 received
Received 19 Mar, 2020
Reviewer #1 agreed
On 10 Mar, 2020
Reviewer #2 agreed
On 10 Mar, 2020
Reviewers invited
Invitations sent on 07 Mar, 2020
Editor assigned
On 20 Feb, 2020
Submission checks complete
On 19 Feb, 2020
Editor invited
On 19 Feb, 2020
First submitted
On 13 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Cancer & Metabolism.
Background Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time consuming. As a consequence of manual data processing the results are often not analysed in full depth, so potential novel findings might get lost.
Methods To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny-application that can be run online in a web-browser on https://mpietzke.shinyapps.io/autoplotter.
Results We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis.
Conclusion With the help of Metabolite AutoPlotter it is now possible to simplify data processing and visualisation for a wide audience. High quality plots from complex data can be generated in a short time with pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further this eliminates potential copy-and paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
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