CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20150805143441
GitHash:aa38cd9
ModuleCount:12
HasImagePlaneDetails:False

LoadData:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:6|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Input data file location:Elsewhere...\x7C\\\\\\\\argon\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\ilastik_CP_co-culture_METHODS_paper\\\\file_lists
    Name of the file:File_list_full.csv
    Load images based on this data?:Yes
    Base image location:Default Input Folder\x7C
    Process just a range of rows?:No
    Rows to process:1,100000
    Group images by metadata?:No
    Select metadata tags for grouping:
    Rescale intensities?:Yes

ClassifyPixels:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'** Make sure these "classes" match your ilastik classes (0,1,2,3,... top to bottom) ** Hep=0, Fb=1, Bkgd=2\', \'There is a bug still that does not translate the path from Windows to Linux in ClassifyPixels, so we specify the path in the UNIX way here\x3A /imaging/analysis/2007_11_07_Hepatoxicity_SPARC/2013_03_27_combinatorialscreen/ilastik\', \'rather than \\\\\\\\iodine\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\2013_03_27_combinatorialscreen\\\\ilastik\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:CorrOrig
    Classifier file location:Elsewhere...\x7C\\\\\\\\argon\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\ilastik_CP_co-culture_METHODS_paper\\\\ilastik\\\\Seeding\\\\ilastik_classifier
    Classfier file name:Seeding_classifier_2015_08_28.h5
    Probability map count:4
    Name the output probability map:HepProb
    Select the class:0
    Name the output probability map:FibProb
    Select the class:1
    Name the output probability map:BkgdProb
    Select the class:2
    Name the output probability map:OtherProb
    Select the class:3

Smooth:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:HepProb
    Name the output image:HepSmooth
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter:7
    Edge intensity difference:0.1
    Clip intensities to 0 and 1?:Yes

Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:FibProb
    Name the output image:FibSmooth
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter:7
    Edge intensity difference:0.1
    Clip intensities to 0 and 1?:Yes

IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:HepSmooth
    Name the primary objects to be identified:HepObj
    Typical diameter of objects, in pixel units (Min,Max):20,60
    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?:No
    Method to distinguish clumped objects:Shape
    Method to draw dividing lines between clumped objects:Shape
    Size of smoothing filter:15
    Suppress local maxima that are closer than this minimum allowed distance:20
    Speed up by using lower-resolution image to find local maxima?:Yes
    Name the outline image:HepPrimaryNucOutline
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:No
    Automatically calculate minimum allowed distance between local maxima?:No
    Retain outlines of the identified objects?:Yes
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
    Enter LoG filter diameter:5.0
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Threshold setting version:1
    Threshold strategy:Manual
    Thresholding method:Otsu
    Select the smoothing method for thresholding:Automatic
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.0,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.5
    Select the measurement to threshold with:None
    Select binary image:None
    Masking objects:None
    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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10
    Use default parameters?:Default
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2.0

IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:FibSmooth
    Name the primary objects to be identified:FibObj
    Typical diameter of objects, in pixel units (Min,Max):25,120
    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?:No
    Method to distinguish clumped objects:Shape
    Method to draw dividing lines between clumped objects:Shape
    Size of smoothing filter:10
    Suppress local maxima that are closer than this minimum allowed distance:7.0
    Speed up by using lower-resolution image to find local maxima?:Yes
    Name the outline image:FibNucOutline
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:Yes
    Retain outlines of the identified objects?:Yes
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
    Enter LoG filter diameter:5.0
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Threshold setting version:1
    Threshold strategy:Manual
    Thresholding method:Otsu
    Select the smoothing method for thresholding:Automatic
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.0,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.5
    Select the measurement to threshold with:None
    Select binary image:None
    Masking objects:None
    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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10
    Use default parameters?:Default
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2.0

RelateObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Sanity check.  Hep and Fib should not overlap.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True]
    Select the input child objects:HepObj
    Select the input parent objects:FibObj
    Calculate child-parent distances?:None
    Calculate per-parent means for all child measurements?:No
    Calculate distances to other parents?:No
    Parent name:None

FlagImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Sanity check.  Hep and Fib should not overlap (probably, depending on smoothing before segmentation).\', \'Fibroblast objects, set aritrarily as "Parents", should have no Children fibroblast objects, i.e. the identified objects should not overlap.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True]
    Hidden:1
    Hidden:1
    Name the flag\'s category:Metadata
    Name the flag:OverlapFlag
    Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail
    Skip image set if flagged?:No
    Flag is based on:Measurements for all objects in each image
    Select the object to be used for flagging:FibObj
    Which measurement?:Children_HepObj_Count
    Flag images based on low values?:No
    Minimum value:0.0
    Flag images based on high values?:Yes
    Maximum value:0
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:

OverlayOutlines:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Display outlines on a blank image?:No
    Select image on which to display outlines:CorrOrig
    Name the output image:HepFibOverlay
    Outline display mode:Color
    Select method to determine brightness of outlines:Max of image
    Width of outlines:1
    Select outlines to display:HepPrimaryNucOutline
    Select outline color:green
    Load outlines from an image or objects?:Image
    Select objects to display:FibGrdTrthLabelMatrix
    Select outlines to display:FibNucOutline
    Select outline color:red
    Load outlines from an image or objects?:Image
    Select objects to display:FibObj

SaveImages:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:HepFibOverlay
    Select the objects to save:None
    Select the module display window to save:None
    Select method for constructing file names:From image filename
    Select image name for file prefix:CorrOrig
    Enter single file name:OrigBlue
    Number of digits:4
    Append a suffix to the image file name?:Yes
    Text to append to the image name:_HepFibOverlay
    Saved file format:tif
    Output file location:Default Output Folder sub-folder\x7COverlays
    Image bit depth:8-bit integer
    Overwrite existing files without warning?:Yes
    When to save:Every cycle
    Rescale the images? :No
    Save as grayscale or color image?:Grayscale
    Select colormap:Default
    Record the file and path information to the saved image?:Yes
    Create subfolders in the output folder?:No
    Base image folder:Elsewhere...\x7C
    Saved movie format:avi

ExportToDatabase:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:26|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True]
    Database type:MySQL
    Database name:2007_11_07_Hepatotoxicity_1
    Add a prefix to table names?:Yes
    Table prefix:AssayDev_ilastik_Plate_Seeding2
    SQL file prefix:SQL_
    Output file location:Default Output Folder\x7C
    Create a CellProfiler Analyst properties file?:Yes
    Database host:imgdb02
    Username:cpuser
    Password:cPus3r
    Name the SQLite database file:DefaultDB.db
    Calculate the per-image mean values of object measurements?:Yes
    Calculate the per-image median values of object measurements?:No
    Calculate the per-image standard deviation values of object measurements?:No
    Calculate the per-well mean values of object measurements?:No
    Calculate the per-well median values of object measurements?:No
    Calculate the per-well standard deviation values of object measurements?:No
    Export measurements for all objects to the database?:All
    Select the objects:
    Maximum # of characters in a column name:64
    Create one table per object, a single object table or a single object view?:One table per object type
    Enter an image url prepend if you plan to access your files via http:http\x3A//imageweb/images/CPALinks
    Write image thumbnails directly to the database?:Yes
    Select the images for which you want to save thumbnails:CorrOrig
    Auto-scale thumbnail pixel intensities?:Yes
    Select the plate type:384
    Select the plate metadata:Plate
    Select the well metadata:Well
    Include information for all images, using default values?:No
    Properties image group count:2
    Properties group field count:2
    Properties filter field count:0
    Workspace measurement count:1
    Experiment name:MyExpt
    Which objects should be used for locations?:HepObj
    Enter a phenotype class table name if using the classifier tool:
    Export object relationships?:Yes
    Overwrite without warning?:Data and schema
    Access CPA images via URL?:Yes
    Select an image to include:CorrOrig
    Use the image name for the display?:Yes
    Image name:Channel1
    Channel color:gray
    Select an image to include:HepFibOverlay
    Use the image name for the display?:Yes
    Image name:Channel2
    Channel color:green
    Do you want to add group fields?:Yes
    Enter the name of the group:PlateWell
    Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well
    Enter the name of the group:PlateName
    Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate
    Do you want to add filter fields?:No
    Automatically create a filter for each plate?:No
    Create a CellProfiler Analyst workspace file?:No
    Select the measurement display tool:ScatterPlot
    Type of measurement to plot on the X-axis:Image
    Enter the object name:None
    Select the X-axis measurement:None
    Select the X-axis index:ImageNumber
    Type of measurement to plot on the Y-axis:Image
    Enter the object name:None
    Select the Y-axis measurement:None
    Select the Y-axis index:ImageNumber

CreateBatchFiles:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:7|show_window:True|notes:\x5B\'\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Store batch files in default output folder?:Yes
    Output folder path:\\\\\\\\iodine\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\2013_03_27_combinatorialscreen\\\\Main_pipeline_output\\\\2014_01_27_CP2p1_TEST
    Are the cluster computers running Windows?:No
    Hidden\x3A in batch mode:No
    Hidden\x3A in distributed mode:No
    Hidden\x3A default input folder at time of save:\\\\\\\\iodine\\\\cbnt_cbimageX\\\\HCS\\\\shanmeghan
    Hidden\x3A revision number:0
    Hidden\x3A from old matlab:No
    Launch BatchProfiler:Yes
    Local root path:\\\\\\\\iodine\\\\cbnt_cbimageX\\\\
    Cluster root path:/cbnt/cbimageX/
    Local root path:\\\\\\\\argon\\\\imaging_analysis\\\\
    Cluster root path:/imaging/analysis/