![]() In a follow-up entry, I will describe in more detail this FILE_UPLOAD Jenkins project. Each build generates a multi-tab report where various properties and annotations of the uploaded file are displayed. As an example, I will use a Jenkins project that assists users to upload and annotate a variety of file types (images, annotations, text, tabular data etc). I will start with an illustration of the reporting requirements we are trying to implement. In either case, it is desirable to be able to display this information to our users after the build for immediate feedback and increased understanding of the underlying data. For example, group statistics such as mean, median and standard deviation describe a data set, but they do not fundamentally transform its data, so they are considered metadata. ![]() These annotations are important metadata about the data and inform us of the data numeric, statistic and biological properties. Annotations that describe but do not alter the original data set.Results representing new values derived from the numerical transformations performed by the analysis (reshaped, normalized etc).When you perform some numerical, statistical or bioinformatic data analysis, the typical result can be assigned to one of two broad result categories. You add a Summary Display post-build action step to parse the XML file generated in Step 2.It uses the configuration file from Step 1. You add a Scriptler build step that uses the writeXMLProperties_scriptlet to generate the XML file that the Summary Display Plugin parses.You write a configuration file for your report (for details see this blog).You need to create a new Scriptler Script from the groovy code for writeXMLProperties_scriptlet, available from my github repository.You need the Summary Display and Scriptler plugins.In this this blog entry, I will describe how I have used the Summary Display and Scriptler plugins to create a simple but effective framework for displaying consistent but dynamic reports following numerical data analysis using the R plugin. Dynamic behavior is required so that you can easily adapt the reports to the underlying data whose format and content will likely change even when the same type of analysis is performed. There are a few different ways that you can create graphical, tabular and/or textual analysis reports, but one thing that becomes clear immediately is that you also require a certain level of dynamic behavior. We also post guided exercises as part of our educational outreach effort.When you use Jenkins for analytics it is important to deliver consistent analysis reports from each build. Simple nuclei identification tutorial ( sample data) (courtesy of the German BioImaging network) Performing a colocalization assay ( relevant example pipeline) Using the Worm Toolbox for image analysis of C. Identifying and measuring cells: Cytoplasm-nucleus translocation assay ( relevant example pipeline)Ĭalculating and applying illumination correction for images ( relevant example pipeline) Identifying, measuring, and classifying yeast colonies ( relevant example pipeline) Using the Input modules in CellProfiler 2.1: Using CellProfiler for Quantitative Image Analysis The NIH has published a introductory chapter of “best practices” for image-based high-content screening (in which CellProfiler is mentioned) as part of the Assay Guidance Manual, and our group has published a more advanced follow-up chapter on image analysis methods. ![]() Our introduction to automated image analysis principles and practicalities is published as an educational article at PLoS. Technical descriptions of CellProfiler and CellProfiler Analyst software can be found in our papers while more written tutorials can be found on the CellProfiler GitHub page. Visit our YouTube playlist for video tutorials on CellProfiler, CellProfiler Analyst, segmentation strategies, how to construct pipelines, and much more.
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