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#### 3. Calculate the effective confocal volume:
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- Follow the same steps described in bullet point 2 to obtain **dye.res**
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<be><br>
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**or**
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**or**<be><br>
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- If the effective confocal was measured without FA*, the user can specify this volume in fl in the main user input. This volume will be then used to calculate concentrations for the calibration plot. The ways to obtain the effective confocal volume outside the FCSpipelineEMBL_KNIME can be found in [technical details](https://git.embl.de/grp-almf/FCSpipelineEMBL_KNIME/-/wikis/technical-details).
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- If the effective confocal was measured without FA*, the user can specify this volume in fl in the main user input. This volume will be then used to calculate concentrations for the calibration plot. The ways to obtain the effective confocal volume outside the FCSpipelineEMBL_KNIME can be found in technical details.
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* in case there is no dye.res file in your analysis
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*if there is no dye.res file in the analysis
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#### Important notes regarding FA fitting step:
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- There are **different fitting models** used for dye and FP&POI.
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- During FA fitting step, user can use the default parameters of fit as described [here](https://git.embl.de/grp-ellenberg/FCSAnalyze/-/wikis/Fa_fit_fcs). The parameter N can be calculated as an inverse intersection of correlations plot with the ordinate axis. This value of N can be then inserted to the table with all parameters during FA fitting step. This can help to better convergenсe of parameters used in the fitting model.
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- If you calculate the effective volume with our pipeline we recommend optimizing the kappa value during the FA fitting step for dye data. For this strategy, run the fitting procedure several times with different kappa near the default meaning. Besides the default kappa given in the instructions of the FA fitting step, one can determine kappa experimentally.
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- If you calculate the effective volume with our pipeline we recommend optimizing the kappa value during the FA fitting step for dye data. For this strategy, run the fitting procedure several times with different kappa near the default meaning trying to minimize the Chi square parameter. Besides the default kappa given in the instructions of the FA fitting step, one can determine kappa experimentally.
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3. Prepare the following [structure of files](structure of files).
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4. Specify parameters in the main user input. You can change any parameters or leave default values.
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#### 4. Prepare the following [structure of files](structure of files).
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#### 5. Specify parameters in the main user input. <br>
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User can change the parameters or leave default ones. <br><br>
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<br> Users have several options to calculate an effective confocal volume described in [technical details](https://git.embl.de/grp-almf/FCSpipelineEMBL_KNIME/-/wikis/technical-details)
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5. Fill a plot parameters input. The explanation of every parameter can be found in a KMIME description menu of the plot parameters input.
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#### 6. Specify parameters in the plot parameters input.
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6. Specify the channels used for collecting images. The default channel used for extracting intensities from images is the first one. If you want to change the channel, go to FP&POI&WT images metanode, then open Image Reader (Table) nodes, go to Subset Selection and exclude all channels except the channel you need to process.
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#### 7. Specify the channels used for collecting images<br>
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The default channel used for extracting intensities from images is Ch1. If the user needs the different channel to process, go to FP&POI&WT images metanode, then open Image Reader (Table) nodes, go to Subset Selection and exclude all channels except the channel you need to process.
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7. Execute all nodes or particular visualization nodes.
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> For the execution of all nodes at one time, press a shortcut: Shift+F7.
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#### 8. Execute the FCSpipelineEMBL_KNIME*
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For the execution of all nodes at one time, press a shortcut: Shift+F7. Description of all outputs see in the [output section](#output-files)
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In Python View node of calibration plot users have an opportunity to:
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* Pick the point of interest in the calibration plot window to see fluctuation and correlation data from the respective FCS position. The line can be influenced by outliers (see step 8 in the Procedure section)
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> Sensitivity of picking event can be adjusted in the plot parameters input
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* Check the statistics parameters and level of bleaching at the headings of plots.
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* Move and zoom a working space
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The main output node that provides interactive visualization of the calibration plot is the **Python View node**. In the Python View node users have an opportunity to:
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* Pick the point of interest in the calibration plot window to see fluctuation and correlation data from the respective FCS position. The line can be influenced by outliers (see the next step of the procedure). The sensitivity of picking events can be adjusted in the plot parameters input
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* Check the statistics parameters and level of bleaching in the headings of plots
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* Move and zoom a working space
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* Adjust spacing and the view of axes and curves
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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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*Execute all nodes or particular visualization nodes.<be><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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#### 9. Quality Check <br>
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* In the plot parameters input, list the points that haven't passed Quality Check* <br><br>
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**Important note**: The Standart Quality check does **not guarantee** to remove all "bad" fluctuations. Thus, we recommend going through calibration points and remove these points manually. To delete the points in the calibration plot, specify the numbers from the annotations of corresponding points in the plot parameters input (the field: points to delete).
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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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*the points with "bad" fluctuations or poor quality of fitting
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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 customize the plot 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 the pipeline.
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- Users 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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- Users can customize the plot 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 the pipeline.
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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.<br><br>
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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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# Output files
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