Home
Knime provides powerful and flexible means to mine data. However, as many methods are implemented just for data modeling languages like R or Matlab, it is crucial to integrate these languages into Knime. To some extent this is already possible. However, from our daily work we’ve learned that many users need to use scripts without having any background in scripting. Thus we implemented a new open source scripting integration framework for Knime, which is based on RGG templates [1]. Its main purpose is to hide the script complexity behind a user-friendly graphical interface. Furthermore, our approach provides the sharing of templates over central template repositories.
1 RGG: A general GUI Framework for R scripts; Ilhami Visne, Bioinformatics, 2009, 10:74
The latest stable build is available as Trusted KNIME Community Contribution and can be found at the following Update Site:
The latest version the Scripting tools is available from the Nightly Build of the Knime Community Contributions. To install them, just add the community update site url to Knime and select the scripting extensions from the list.
The R integration uses Rserve as a back-end to communicate with your local R installation or one on a remote server. You can find a detailed description on how setup the back-end at the R-Server installation instructions.
The R integration provides nodes to hand through the whole R workspace. Descriptions on how to use these nodes can be found here: Generic R.
The Python integration supports Python3 and 2.x (recommended version 2.7).
For further information visit the pages of the Python Scripting Integration
https://www.python.org/
Groovy is the dynamic version of Java. It can run locally and uses the same JVM as KNIME itself. In other words it does not need an additional back-end, the nodes alone does the trick.
since the release 2.0.3 the MATLAB scripting integrations needs a MATLAB installation on the local machine.
spaces in the paths that are passed to matlab lead to errors. A quick and dirty workaround is to remove the space in the knime installation folders (and subsequently take care that no other spaces are used in the path).
Our scripting framework is based on a template repository that contains RGG-templates along with description, categorization and optional previews (see screenshot). Templates are stored in plain text files that are supposed to follow a simple 3-fold header-description-templatecode schema. The desired template repository can be specified in the Knime preferences (Independently for each scripting language of course). By default the nodes link to template-files that focus on visualizations and processing-snippets used for HCS screening (Example: R plots, Example: R snippets). These templates supplement the HCS Tools. It is possible to hook in several template repository files at once.
You can explore the current set of figure templates and snippets:
Here there are a couple of example templates.
To use R templates set in KNIME > Preferences > KNIME > R Scripting the following links in their respective field
https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/R/figure-templates.txt https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/R/snippet-templates.txt
To use Python templates set in KNIME > Preferences > KNIME > Python Scripting the following links in their respective field
https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/Python/figure-templates.txt https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/Python/script-templates.txt
To use MATLAB templates set in KNIME > Preferences > KNIME > Matlab Scripting the following links in their respective field
https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/Matlab/figure-templates.txt https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/Matlab/script-templates.txt
To use Groovy templates set in KNIME > Preferences > KNIME > Groovy Scripting the following links in their respective field
https://raw.githubusercontent.com/knime-mpicbg/scripting-templates/master/knime-scripting-templates/Groovy/Groovy-templates.txt
To write your own templates you need to understand the concept the of the RGG-XML. The documentation, example and a "How to..." you can find on the RGG-page.
Flow variables of KNIME can also be accessed within the templates or R code. A short documentation how to used flow variables you can find here here.
The scripting nodes are released under the 3-clause BSD.
Feel welcome to contact us ([email protected]) if you want to contribute to this project, have suggestions, found bugs, or want to tell us about your vision about how scripting languages might integrate into Knime
Please acknowledge or cite this website if you have used this KNIME extension in your work and found it helpful:
https://github.com/knime-mpicbg/knime-scripting/wiki
If you want to mention our institution use this:
High-Throughput Technology Development Studio (TDS) Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
We have published a book chapter (Springer, PubMed) on open source software tools for high-content screening, where we introduced the Scripting Integration and HCS-Tools for KNIME. Feel free to add this as a citation. Here is the citation in BibTeX format:
@incollection{
year={2013},
isbn={978-1-62703-310-7},
booktitle={Target Identification and Validation in Drug Discovery},
volume={986},
series={Methods in Molecular Biology},
editor={Moll, Jurgen and Colombo, Riccardo},
doi={10.1007/978-1-62703-311-4_8},
title={CellProfiler and KNIME: Open Source Tools for High Content Screening},
url={http://dx.doi.org/10.1007/978-1-62703-311-4_8},
publisher={Humana Press},
keywords={High content screening; Image processing; Statistics; Open Source; CellProfiler; KNIME; Distributed computing},
author={Stöter, Martin and Niederlein, Antje and Barsacchi, Rico and Meyenhofer, Felix and Brandl, Holger and Bickle, Marc},
pages={105-122},
language={English}
}