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EV_​Flow_​Analysis_​training

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Primary Analysis 5. Re-examination of select cluster(s) Select Files For Analysis 1. Select Files for analysis2. Select scale of data for analysis3. Define parameters for transformation4. Set parameters for dimensional redutions5. Review scatterplot of project data6. Review plot from individual samples7. Write results 1. 2. 3. 4. 6. 7. 8. Select scale of data for re-examination9. Select clusters to be re-examined10. Define parameters for transformation11. Set parameters for dimensional redutions12. Review scatterplot of project data13. Review plot from individual samples14. Write results 8. 9. 10. 11. 12. 13. 14. Notes- The workflow implements UMAP and HDBSCAN as python packages and requires separate installation of these packages using Conda (https://umap-learn.readthedocs.io/en/latest/#) and (https://hdbscan.readthedocs.io/en/latest/index.html). - For installation of pythong support for KNIME please follow instruction from KNIME (https://docs.knime.com/latest/python_installation_guide/#_introduction).For best results, start the analysis with approximatly 50,000 events and increase the sample size once familiar with the workflow.Re-examination of the data is optional. 1419356428101113127Node 3103 write_results List Files/Folders plot_clusters XGBoost Gating transform Path to String 2D/3D Scatterplot dim_reduce_cluster Image Viewer set_parameters partial_input read_from_fcs partial_input read_from_fcs transform set_parameters dim_reduce_cluster Image Viewer plot_clusters 2D/3D Scatterplot write_results Domain Calculator Primary Analysis 5. Re-examination of select cluster(s) Select Files For Analysis 1. Select Files for analysis2. Select scale of data for analysis3. Define parameters for transformation4. Set parameters for dimensional redutions5. Review scatterplot of project data6. Review plot from individual samples7. Write results 1. 2. 3. 4. 6. 7. 8. Select scale of data for re-examination9. Select clusters to be re-examined10. Define parameters for transformation11. Set parameters for dimensional redutions12. Review scatterplot of project data13. Review plot from individual samples14. Write results 8. 9. 10. 11. 12. 13. 14. Notes- The workflow implements UMAP and HDBSCAN as python packages and requires separate installation of these packages using Conda (https://umap-learn.readthedocs.io/en/latest/#) and (https://hdbscan.readthedocs.io/en/latest/index.html). - For installation of pythong support for KNIME please follow instruction from KNIME (https://docs.knime.com/latest/python_installation_guide/#_introduction).For best results, start the analysis with approximatly 50,000 events and increase the sample size once familiar with the workflow.Re-examination of the data is optional. 1419356428101113127Node 3103 write_results List Files/Folders plot_clusters XGBoost Gating transform Path to String 2D/3D Scatterplot dim_reduce_cluster Image Viewer set_parameters partial_input read_from_fcs partial_input read_from_fcs transform set_parameters dim_reduce_cluster Image Viewer plot_clusters 2D/3D Scatterplot write_results Domain Calculator

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Created by: franzek
Created at: 2021-10-14
On NodePit since: 2024-04-02
Last update: 2024-11-21
Created with KNIME version: v5.1.0

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