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### Welcome to a Wiki page of FCSpipelineEMBL_KNIME!
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# Welcome to a Wiki page of FCSpipelineEMBL_KNIME!
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Here we explain how to install and use FCS-calibrated image analysis pipeline. The FCSpipelineEMBL_KNIME is a software for the interactive and automated processing of FCS data.
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#### 1. [Installation](installation)
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#### 2. [Structure of workflow](#structure-of-workflow)
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#### 3. [Procedure](#procedure)
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#### 5. [Output files](#output-files)
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#### Structure of workflow
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# Structure of workflow
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> Explanations of technical details of FCSpipeline you could find [here](technical details)
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> The main nodes users work with are typed in bold.
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- User input:
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- **main user input** (where users specify general parameters)
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- **plot parameters input** (users customize the calibration plot and setting the Quality Check (QC) parameters)
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- WT user input (for calculating the background offset for correction step in Fluctuation Analyzer (FA))
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- **main user input** (general parameters). The explanation of every parameter can be founded in a KMIME description menu of the main user input.
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- **plot parameters input** (calibration plot and Quality Check (QC) parameters)
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- WT user input (for calculating the background offset for the correction step in the Fluctuation Analyzer (FA))
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- Loading the data:
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- FP&POI, dye metanodes (collecting fluctuation data)
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- FP&POI&WT images metanode (loading images and coordinates)
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- Data processing + Quality Check:
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- Python Script (2=>1) (calculating confocal volume and concentrations)
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- intensity calculation metanode (extracting intensities in ROI-s, background substraction)
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- intensity calculation metanode (extracting intensities in ROI-s, background subtraction)
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- joiner metanode with Quality Check (data collection for calibration plot + Quality Check)
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> It will implement a Quality Check by filtering the points for calibration plot according to the bounders of statistics parameters specified by the user in plot parameters input.
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- concentration map metanode (building concentration maps from row images using calibration parameters)
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- Visualisation of data
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- Visualization of data
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- Python Source (POI&FP CPM distribution)
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- **Python View** (calibration plot appeares when you execute this node)
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- **Python View** (calibration plot appears when you execute this node)
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- Table View (concentrations of POI)
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- Two Image Viewers (loaded images with info and concentration maps)
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#### Procedure
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# Procedure
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1. Process a WT data with FA to obtain WT.res. A detailed explanation of FA analysis procedure including [correlations calculation](https://git.embl.de/grp-ellenberg/FCSAnalyze/-/wikis/Fa_Load_and_Correlate) and [fitting](https://git.embl.de/grp-ellenberg/FCSAnalyze/-/wikis/Fa_fit_fcs) can be found in git project of previous FCS-calibrated imaging pipeline developed by Antonio Politi. Only import, correction, and export steps are needed for the WT FA session. Then fill a WT user input in the KNIME workflow of FCSpipelineEMBL_KNIME and execute Python Source node. Use returned offset value for correction steps in further FA sessions for FP and POI.
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2. Perform three separate sessions of FA analysis: for FP, for POI, and for dye as described [here](https://git.embl.de/grp-ellenberg/FCSAnalyze/-/wikis/Fa_fit_fcs). Do **all** intermediate FA steps (correlations calculation, correction, fitting of correlations with ACF) to obtain mFP.res, POI.res, and dye.res.
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**Important:** do not forget to save fluctuation traces with ACF fitting curves (Export all traces button) as .cof, .itr files.
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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. Specify parameters in the main user input. You can change any parameters or leave default values. The explanation of every parameter can be founded in a KMIME description menu of the main user input.
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The explanation of every parameter can be founded in a KMIME description menu of the main user input.
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5. Fill a plot parameters input. The explanation of every parameter can be founded in a KMIME description menu of the plot parameters input.
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> It will implement a Quality Check by filtering the points for calibration plot according to the bounders of statistics parameters specified by user.
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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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| ... | ... | @@ -51,7 +52,7 @@ The explanation of every parameter can be founded in a KMIME description menu of |
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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. Execution of some nodes such as 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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> For the execution of all nodes at one time, press a shortcut: Shift+F7.
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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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| ... | ... | @@ -63,14 +64,21 @@ In Python View node of calibration plot users have an opportunity to: |
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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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Delete points that haven't passed Quality Check (the points with "bad" fluctuations or poor quality of fitting)
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* List the points to delete in plot parameters input. <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 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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> 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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#### 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 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 pipeline.
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# Output files
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> All outputs are saved in the main directory.
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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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