Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser
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.
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Posted 08 Jun, 2020
On 10 Jul, 2020
On 15 Jun, 2020
On 03 Jun, 2020
On 02 Jun, 2020
On 02 Jun, 2020
On 05 May, 2020
Received 13 Apr, 2020
Received 19 Mar, 2020
On 10 Mar, 2020
On 10 Mar, 2020
Invitations sent on 07 Mar, 2020
On 20 Feb, 2020
On 19 Feb, 2020
On 19 Feb, 2020
On 13 Feb, 2020
Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser
Posted 08 Jun, 2020
On 10 Jul, 2020
On 15 Jun, 2020
On 03 Jun, 2020
On 02 Jun, 2020
On 02 Jun, 2020
On 05 May, 2020
Received 13 Apr, 2020
Received 19 Mar, 2020
On 10 Mar, 2020
On 10 Mar, 2020
Invitations sent on 07 Mar, 2020
On 20 Feb, 2020
On 19 Feb, 2020
On 19 Feb, 2020
On 13 Feb, 2020
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