| ... | @@ -63,22 +63,22 @@ In Python View node of calibration plot users have an opportunity to: |
... | @@ -63,22 +63,22 @@ In Python View node of calibration plot users have an opportunity to: |
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* Save the image of the plot
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* Save the image of the plot
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> Users can specify annotations for every point in the calibration plot by adding them to mFP.res (Annotation column). Don't forget to add this option in the plot parameter input.
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> Users can specify annotations for every point in the calibration plot by adding them to mFP.res (Annotation column). Don't forget to add this option in the plot parameter input.
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8. Quality Check <br>
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8. Quality Check <br>
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* In plot parameters input, list the points that haven't passed Quality Check (the points with "bad" fluctuations or poor quality of fitting). **Important note**: The Standart Quality check does **not guarantee** to remove all "bad" fluctuations. Thus, we recommend going through calibration points and remove all "bad" fluctuations manually. To delete the points in the calibration plot, fill the numbers from the annotations of corresponding points into plot parameter input (points to delete).
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* In plot parameters input, list the points that haven't passed Quality Check (the points with "bad" fluctuations or poor quality of fitting). **Important note**: The Standart Quality check does **not guarantee** to remove all "bad" fluctuations. Thus, we recommend going through calibration points and remove all "bad" fluctuations manually. To delete the points in the calibration plot, fill the numbers from the annotations of corresponding points into plot parameter input (points to delete).
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* Reexecute the Python View node with a calibration plot
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* Reexecute the Python View node with a calibration plot
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> Quality Check step could also help to get rid of outliers that can influence the liner parameters of the calibration line.
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> Quality Check step could also help to get rid of outliers that can influence the liner parameters of the calibration line.
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#### Hints and tips for using FCSpipelineEMBL_KNIME
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#### Hints and tips for using FCSpipelineEMBL_KNIME
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- you can use several main user inputs for different datasets to not change all parameters every time you process a new dataset
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- you can use several main user inputs for different datasets to not change all parameters every time you process a new dataset
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- you can customize plot by changing plot settings in Python View metanode.
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- you can customize the tplot by changing plot settings in Python View metanode.
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- the execution of concentration maps metanode could take some time. If you don't need to build concentration maps, you can select all nodes except concentration maps metanode when executing pipeline.
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- the execution of concentration maps metanode could take some time. If you don't need to build concentration maps, you can select all nodes except concentration maps metanode when executing the pipeline.
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# Output files
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# Output files
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> All outputs are saved in the main directory.
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> All outputs are saved in the output folder in the main directory.
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1. **info.csv**
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1. **info.csv**
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info.csv is generated inside the main user input. This is the main output file with all final and intermediate parameters. It includes:
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info.csv is generated inside the main user input. This is the main output file with all final and intermediate parameters. It includes:
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* diffusion coefficient of dye and confocal volume
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* diffusion coefficient of dye and confocal volume
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