CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10415

LoadImages:[module_num:1|svn_version:\'10372\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
    File type to be loaded:tif,tiff,flex movies
    File selection method:Text-Regular expressions
    Number of images in each group?:3
    Type the text that the excluded images have in common:Do not use
    Analyze all subfolders within the selected folder?:No
    Input image file location:Default Input Folder\x7CNone
    Check image sets for missing or duplicate files?:Yes
    Group images by metadata?:No
    Exclude certain files?:No
    Specify metadata fields to group by:
    Image count:1
    Text that these images have in common (case-sensitive):00\x5B56789\x5D0..003.tif
    Position of this image in each group:1
    Extract metadata from where?:None
    Regular expression that finds metadata in the file name:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)
    Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P<Date>.*)\x5B\\\\/\x5D(?P<Run>.*)$
    Channel count:3
    Group the movie frames?:Yes
    Grouping method:Interleaved
    Number of channels per group:3
    Name this loaded image:0_Raw_protein_GFP
    Channel number:1
    Name this loaded image:0_Raw_nuclear_RFP
    Channel number:3
    Name this loaded image:0_Raw_Media
    Channel number:2

Smooth:[module_num:2|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Select the input image:0_Raw_Media
    Name the output image:01_smoothed_Media
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter, in  pixels:6.0
    Edge intensity difference:0.1

Smooth:[module_num:3|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Select the input image:0_Raw_nuclear_RFP
    Name the output image:01_smoothed_nuclear_RFP
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter, in  pixels:5.0
    Edge intensity difference:0.1

Smooth:[module_num:4|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Select the input image:0_Raw_protein_GFP
    Name the output image:01_smoothed_protein_GFP
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter, in  pixels:2.0
    Edge intensity difference:0.1

ImageMath:[module_num:5|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Operation:Invert
    Raise the power of the result by:1
    Multiply the result by:1
    Add to result:0
    Set values less than 0 equal to 0?:Yes
    Set values greater than 1 equal to 1?:Yes
    Name the output image:02_smoothed_Inverted_Media
    Select the first image:01_smoothed_Media
    Multiply the first image by:1
    Select the second image:
    Multiply the second image by:1

CorrectIlluminationCalculate:[module_num:6|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
    Select the input image:02_smoothed_Inverted_Media
    Name the output image:001_Media_correction
    Select how the illumination function is calculated:Regular
    Dilate objects in the final averaged image?:No
    Dilation radius:1
    Block size:60
    Rescale the illumination function?:No
    Calculate function for each image individually, or based on all images?:Each
    Smoothing method:Fit Polynomial
    Method to calculate smoothing filter size:Automatic
    Approximate object size:10
    Smoothing filter size:10
    Retain the averaged image for use later in the pipeline (for example, in SaveImages)?:No
    Name the averaged image:IllumBlueAvg
    Retain the dilated image for use later in the pipeline (for example, in SaveImages)?:No
    Name the dilated image:IllumBlueDilated
    Automatically calculate spline parameters?:Yes
    Background mode:auto
    Number of spline points:5
    Background threshold:2
    Image resampling factor:2
    Maximum number of iterations:40
    Residual value for convergence:0.001

CorrectIlluminationApply:[module_num:7|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
    Select the input image:02_smoothed_Inverted_Media
    Name the output image:03_smoothed_inverted_corrected_media
    Select the illumination function:001_Media_correction
    Select how the illumination function is applied:Subtract

RescaleIntensity:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
    Select the input image:03_smoothed_inverted_corrected_media
    Name the output image:04_Rescaled_Corrected_Inverted_Smoothed_Media
    Select rescaling method:Stretch each image to use the full intensity range
    How do you want to calculate the minimum intensity?:Custom
    How do you want to calculate the maximum intensity?:Custom
    Enter the lower limit for the intensity range for the input image:0
    Enter the upper limit for the intensity range for the input image:1
    Enter the intensity range for the input image:0.000000,1.000000
    Enter the desired intensity range for the final, rescaled image:0.000000,1.000000
    Select method for rescaling pixels below the lower limit:Mask pixels
    Enter custom value for pixels below lower limit:0
    Select method for rescaling pixels above the upper limit:Mask pixels
    Enter custom value for pixels below upper limit:0
    Select image to match in maximum intensity:None
    Enter the divisor:1
    Select the measurement to use as a divisor:None

RescaleIntensity:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
    Select the input image:01_smoothed_nuclear_RFP
    Name the output image:02_Rescaled_nuclear_RFP
    Select rescaling method:Stretch each image to use the full intensity range
    How do you want to calculate the minimum intensity?:Custom
    How do you want to calculate the maximum intensity?:Custom
    Enter the lower limit for the intensity range for the input image:0
    Enter the upper limit for the intensity range for the input image:1
    Enter the intensity range for the input image:0.000000,1.000000
    Enter the desired intensity range for the final, rescaled image:0.000000,1.000000
    Select method for rescaling pixels below the lower limit:Mask pixels
    Enter custom value for pixels below lower limit:0
    Select method for rescaling pixels above the upper limit:Mask pixels
    Enter custom value for pixels below upper limit:0
    Select image to match in maximum intensity:None
    Enter the divisor:1
    Select the measurement to use as a divisor:None

ImageMath:[module_num:10|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Operation:Add
    Raise the power of the result by:1
    Multiply the result by:1
    Add to result:0
    Set values less than 0 equal to 0?:Yes
    Set values greater than 1 equal to 1?:Yes
    Name the output image:05_withrfp_rescaled_corrected_inverted_smoothed_Media
    Select the first image:04_Rescaled_Corrected_Inverted_Smoothed_Media
    Multiply the first image by:1
    Select the second image:02_Rescaled_nuclear_RFP
    Multiply the second image by:0.3

IdentifyPrimaryObjects:[module_num:11|svn_version:\'10372\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
    Select the input image:02_Rescaled_nuclear_RFP
    Name the primary objects to be identified:1_Nuclei
    Typical diameter of objects, in pixel units (Min,Max):7,35
    Discard objects outside the diameter range?:Yes
    Try to merge too small objects with nearby larger objects?:No
    Discard objects touching the border of the image?:Yes
    Select the thresholding method:Otsu Adaptive
    Threshold correction factor:1
    Lower and upper bounds on threshold:0.05,1.0
    Approximate fraction of image covered by objects?:0.01
    Method to distinguish clumped objects:Intensity
    Method to draw dividing lines between clumped objects:Intensity
    Size of smoothing filter:10
    Suppress local maxima that are closer than this minimum allowed distance:7
    Speed up by using lower-resolution image to find local maxima?:Yes
    Name the outline image:1_NucleusOutlines
    Fill holes in identified objects?:Yes
    Automatically calculate size of smoothing filter?:Yes
    Automatically calculate minimum allowed distance between local maxima?:Yes
    Manual threshold:0.0
    Select binary image:None
    Retain outlines of the identified objects?:Yes
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    Two-class or three-class thresholding?:Two classes
    Minimize the weighted variance or the entropy?:Weighted variance
    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
    Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
    Enter LoG filter diameter:5
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Select the measurement to threshold with:None

ExpandOrShrinkObjects:[module_num:12|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Select the input objects:1_Nuclei
    Name the output objects:2_ShrunkenNuclei
    Select the operation:Shrink objects by a specified number of pixels
    Number of pixels by which to expand or shrink:3
    Fill holes in objects so that all objects shrink to a single point?:Yes
    Retain the outlines of the identified objects for use later in the pipeline (for example, in SaveImages)?:No
    Name the outline image:ShrunkenNucleiOutlines

ExpandOrShrinkObjects:[module_num:13|svn_version:\'10300\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Select the input objects:1_Nuclei
    Name the output objects:2_PointNuclei
    Select the operation:Shrink objects to a point
    Number of pixels by which to expand or shrink:3
    Fill holes in objects so that all objects shrink to a single point?:Yes
    Retain the outlines of the identified objects for use later in the pipeline (for example, in SaveImages)?:No
    Name the outline image:ShrunkenNucleiOutlines

IdentifySecondaryObjects:[module_num:14|svn_version:\'10300\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
    Select the input objects:2_ShrunkenNuclei
    Name the objects to be identified:2_CellsFromNuclei
    Select the method to identify the secondary objects:Watershed - Image
    Select the input image:05_withrfp_rescaled_corrected_inverted_smoothed_Media
    Select the thresholding method:Otsu Adaptive
    Threshold correction factor:7.00
    Lower and upper bounds on threshold:0.000000,1.000000
    Approximate fraction of image covered by objects?:0.2
    Number of pixels by which to expand the primary objects:30
    Regularization factor:0.0
    Name the outline image:2_CellOutlinesFromNuclei
    Manual threshold:0.0
    Select binary image:None
    Retain outlines of the identified secondary objects?:Yes
    Two-class or three-class thresholding?:Three classes
    Minimize the weighted variance or the entropy?:Weighted variance
    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
    Discard secondary objects that touch the edge of the image?:No
    Discard the associated primary objects?:No
    Name the new primary objects:FilteredNuclei
    Retain outlines of the new primary objects?:No
    Name the new primary object outlines:FilteredNucleiOutlines
    Select the measurement to threshold with:None
    Fill holes in identified objects?:Yes

PauseCellProfiler:[module_num:15|svn_version:\'9434\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Pause here, skip subsequent modules or continue without prompting?:Continue

MeasureObjectSizeShape:[module_num:16|svn_version:\'1\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
    Select objects to measure:2_CellsFromNuclei
    Select objects to measure:1_Nuclei
    Calculate the Zernike features?:No

MeasureObjectIntensity:[module_num:17|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
    Hidden:1
    Select an image to measure:0_Raw_protein_GFP
    Select objects to measure:2_CellsFromNuclei

MeasureObjectIntensity:[module_num:18|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
    Hidden:1
    Select an image to measure:02_Rescaled_nuclear_RFP
    Select objects to measure:1_Nuclei

ExportToSpreadsheet:[module_num:19|svn_version:\'10251\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
    Select or enter the column delimiter:Comma (",")
    Prepend the output file name to the data file names?:Yes
    Add image metadata columns to your object data file?:No
    Limit output to a size that is allowed in Excel?:No
    Select the columns of measurements to export?:No
    Calculate the per-image mean values for object measurements?:No
    Calculate the per-image median values for object measurements?:No
    Calculate the per-image standard deviation values for object measurements?:No
    Output file location:Default Output Folder\x7CNone
    Create a GenePattern GCT file?:No
    Select source of sample row name:Metadata
    Select the image to use as the identifier:None
    Select the metadata to use as the identifier:None
    Export all measurements?:Yes
    Press button to select measurements to export:
    Data to export:Do not use
    Combine these object measurements with those of the previous object?:No
    File name:DATA.csv
    Use the object name for the file name?:Yes