![]() ![]() Choose the pipeline name and save it to wrmXpress/cp_pipelines/pipelines. Once LoadData has been added, you will no longer be able to test the pipeline.Ĭlick File > Export > Pipeline. In Output Settings (bottom left hand corner of the CP window), change the Default Input and Output folder to /Users/.csv". Start a new project by opening and editing the template CP project that can be found in the wrmXpress repo (in local_env/cp_pipelines/template.cpproj).Ĭreate a folder on your local desktop entitled temp_root/. A few general guidelines will allow for seamless integration of new CellProfiler pipelines into the wrmXpress wrapper. Adding custom CellProfiler pipelinesĪ more immediate option for wrmXpress development that does not require Python expertise is through CellProfiler, which is written in Python but enables pipeline development through a GUI. Development of these modules likely requires a strong working knowledge of Python, including image processing libraries such as opencv, scikit-image, matplotlib, numpy, pillow, and scipy. Modules can be developed in Python and invoked on a well-by-well basis. WrmXpress was designed to be extensible by other users. wrmXpress development Adding custom Python modules Debugging will not begin until an adequate Issue has been filed. ![]() If you run into a bug, please submit an Issue to the wrmXpress repo. So far, not all module combinations have been tested, and there are likely new experimental designs (e.g., wavelength/site combinations) that may result in bugs without further testing and development. The number of jobs submitted corresponds to the number of plates in plate_list.txt. If everything went correctly, you should see a notice that says the jobs have been submitted. Submit the job(s) by running the command condor_submit chtc-submit/imgproc/wrmXpress.sub script=chtc-submit/imgproc/wrmXpress.sh. Do not push these changes back to the remote chtc-submit repo. SSH into the submit2 server and verify that the home directory includes a YAML file for each plate and an updated plate_list.txt file.Ĭlone (or pull) the chtc-submit repo ( git clone ).Įdit chtc-submit/imgproc/wrmXpress.sub to ensure you are requesting the correct number of CPUs and amount of RAM and disc space. Note: If you already have a plate_list.txt file in your home directory, you can also choose to edit this in the terminal using vim or nano. Upload this file to the home directory of the submit2 server. It is probably easiest to use an SFTP client (e.g, Transmit, FileZilla, CyberDuck), but you can also use Globus or the scp command on the command line.Ĭreate a file called plate_list.txt and include the plate name of each plate in the batch on a separate line. Upload the YAML file(s) to your home directory of the submit2 server. ![]() Each plate that you plan to analyze must have its own YAML file (even if the parameters are the same for each plate in the batch). Name each parameters file with the plate name (e.g., 20211013-p04-NJW_932.yml). Edit the YAML file with a text editor (e.g., Sublime, TextEdit, Atom, etc.). A thorough explanation of each parameter can be found at the repo's homepage. Use the template found in the GibHub repo. Once data is staged, follow the protocol below.įor each plate, fill out a YAML file that dictates the job's parameters. To submit a job, the required imaging data and metadata must be uploading to the staging/ directory of the CHTC. For instructions on transferring data and metadata to the CHTC, see the Globus section (1A) of our Server Pipelines page.For generating metadata, see the Metadata heading of the documentation.For full ImageXpress protocols, see our comprehensive documentation.Prerequisites include generating and exporting imaging data to Research Drive, transferring it to the input/ directory of staging/ on the CHTC, as well as uploading experimental metadata to the metadata/ directory of staging/. The following contains a step-by-step protocol that is specific to Zamanian lab analytical pipelines deployed at the Center for High-Throughput Computing (CHTC). More details can be found at the wrmXpress GitHub repository. The software can be deployed on a local machine or distributed on a high-performance or high-throughput computing network. ![]() The suite is composed of multiple bespoke modules that can be mixed and matched with pipelines developed in CellProfiler. We have developed a software suite that analyzes worm imaging data generated on an ImageXpress high-content imager. ![]()
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