CellProfiler Pipeline: http://www.cellprofiler.org Version:2 DateRevision:20121105125254 LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n dtype=\'\x7CS1\')] File type to be loaded:individual images File selection method:Text-Exact match 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?:None Input image file location:Default Input Folder\x7CNone Check image sets for unmatched or duplicate files?:Yes Group images by metadata?:No Exclude certain files?:No Specify metadata fields to group by: Select subfolders to analyze: Image count:1 Text that these images have in common (case-sensitive):kv13 Position of this image in each group:1 Extract metadata from where?:None Regular expression that finds metadata in the file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D) Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P.*)\x5B\\\\\\\\/\x5D(?P.*)$ Channel count:1 Group the movie frames?:No Grouping method:Interleaved Number of channels per group:3 Load the input as images or objects?:Images Name this loaded image:Original Name this loaded object:Nuclei Retain outlines of loaded objects?:No Name the outline image:LoadedImageOutlines Channel number:1 Rescale intensities?:Yes ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n dtype=\'\x7CS1\')] Select the input image:Original Conversion method:Split Image type\x3A:RGB Name the output image:OrigGray Relative weight of the red channel:1 Relative weight of the green channel:1 Relative weight of the blue channel:1 Convert red to gray?:Yes Name the output image:RED Convert green to gray?:Yes Name the output image:GREEN Convert blue to gray?:Yes Name the output image:BLUE Channel count:1 Channel number\x3A:Red\x3A 1 Relative weight of the channel:1 Image name\x3A:Channel1 IdentifyPrimaryObjects:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n dtype=\'\x7CS1\')] Select the input image:BLUE Name the primary objects to be identified:Nuclei Typical diameter of objects, in pixel units (Min,Max):8,45 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:Yes Discard objects touching the border of the image?:Yes Select the thresholding method:Otsu Global Threshold correction factor:1 Lower and upper bounds on threshold:0.10,1.0 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:PrimaryOutlines 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?:No 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 Method to calculate adaptive window size:Image size Size of adaptive window:10 IdentifyPrimaryObjects:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Select the input image:RED Name the primary objects to be identified:CD4 Cells Typical diameter of objects, in pixel units (Min,Max):8,45 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:Yes Discard objects touching the border of the image?:Yes Select the thresholding method:Otsu Global Threshold correction factor:1 Lower and upper bounds on threshold:0.15,1.0 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:PrimaryOutlines 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?:No 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 Method to calculate adaptive window size:Image size Size of adaptive window:10 IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n dtype=\'\x7CS1\')] Select the input image:GREEN Name the primary objects to be identified:kv1.3channels Typical diameter of objects, in pixel units (Min,Max):8,45 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:Yes Discard objects touching the border of the image?:Yes Select the thresholding method:Otsu Global Threshold correction factor:1 Lower and upper bounds on threshold:0.25,1.0 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:PrimaryOutlines 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?:No 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 Method to calculate adaptive window size:Image size Size of adaptive window:10 RelateObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n dtype=\'\x7CS1\')] Select the input child objects:kv1.3channels Select the input parent objects:CD4 Cells Calculate distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Select the input child objects:CD4 Cells Select the input parent objects:Nuclei Calculate distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None ClassifyObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement Hidden:1 Select the object to be classified:CD4 Cells Select the measurement to classify by:Children_kv1.3channels_Count Select bin spacing:Custom-defined bins Number of bins:3 Lower threshold:0 Use a bin for objects below the threshold?:No Upper threshold:1 Use a bin for objects above the threshold?:Yes Enter the custom thresholds separating the values between bins:0,0.1 Give each bin a name?:Yes Enter the bin names separated by commas:noKV1.3, KV1.3positive Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No Name the output image:ClassifiedNuclei Enter the object name:None Select the first measurement:None Method to select the cutoff:Mean Enter the cutoff value:0.5 Select the second measurement:None Method to select the cutoff:Mean Enter the cutoff value:0.5 Use custom names for the bins?:No Enter the low-low bin name:low_low Enter the low-high bin name:low_high Enter the high-low bin name:high_low Enter the high-high bin name:high_high Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No Enter the image name:None ClassifyObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement Hidden:1 Select the object to be classified:Nuclei Select the measurement to classify by:Children_CD4 Cells_Count Select bin spacing:Custom-defined bins Number of bins:3 Lower threshold:0 Use a bin for objects below the threshold?:No Upper threshold:1 Use a bin for objects above the threshold?:Yes Enter the custom thresholds separating the values between bins:0,0.1 Give each bin a name?:Yes Enter the bin names separated by commas:CD4negative, CD4positive Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No Name the output image:ClassifiedNuclei Enter the object name:None Select the first measurement:None Method to select the cutoff:Mean Enter the cutoff value:0.5 Select the second measurement:None Method to select the cutoff:Mean Enter the cutoff value:0.5 Use custom names for the bins?:No Enter the low-low bin name:low_low Enter the low-high bin name:low_high Enter the high-low bin name:high_low Enter the high-high bin name:high_high Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No Enter the image name:None MeasureObjectIntensity:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Hidden:1 Select an image to measure:GREEN Select objects to measure:kv1.3channels DisplayDataOnImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Display object or image measurements?:Object Select the input objects:CD4 Cells Measurement to display:Children_kv1.3channels_Count Select the image on which to display the measurements:RED Text color:m Name the output image that has the measurements displayed:DisplayImage Font size (points):8 Number of decimals:1 Image elements to save:Image Annotation offset (in pixels):0 ClassifyObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement Hidden:1 Select the object to be classified:kv1.3channels Select the measurement to classify by:Intensity_MeanIntensity_GREEN Select bin spacing:Custom-defined bins Number of bins:2 Lower threshold:0.3 Use a bin for objects below the threshold?:No Upper threshold:1 Use a bin for objects above the threshold?:Yes Enter the custom thresholds separating the values between bins:0.30,0.40 Give each bin a name?:Yes Enter the bin names separated by commas:KV1.3low,KV1.3high Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No Name the output image:ClassifiedNuclei Enter the object name:None Select the first measurement:None Method to select the cutoff:Mean Enter the cutoff value:0.5 Select the second measurement:None Method to select the cutoff:Mean Enter the cutoff value:0.5 Use custom names for the bins?:No Enter the low-low bin name:low_low Enter the low-high bin name:low_high Enter the high-low bin name:high_low Enter the high-high bin name:high_high Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No Enter the image name:None FilterObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Name the output objects:KV1.3 positive cells Select the object to filter:CD4 Cells Filter using classifier rules or measurements?:Measurements Select the filtering method:Limits Select the objects that contain the filtered objects:None Retain outlines of the identified objects?:No Name the outline image:FilteredObjects Rules file location:Default Input Folder\x7CNone Rules file name:rules.txt Class number:1 Measurement count:1 Additional object count:0 Select the measurement to filter by:Children_kv1.3channels_Count Filter using a minimum measurement value?:Yes Minimum value:1 Filter using a maximum measurement value?:No Maximum value:1 FilterObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Name the output objects:CD4 positive cells Select the object to filter:Nuclei Filter using classifier rules or measurements?:Measurements Select the filtering method:Limits Select the objects that contain the filtered objects:None Retain outlines of the identified objects?:No Name the outline image:FilteredObjects Rules file location:Default Input Folder\x7CNone Rules file name:rules.txt Class number:1 Measurement count:1 Additional object count:0 Select the measurement to filter by:Children_CD4 Cells_Count Filter using a minimum measurement value?:Yes Minimum value:1 Filter using a maximum measurement value?:No Maximum value:1 CalculateMath:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Name the output measurement:KV1.3+ of CD4 Operation:Divide Select the numerator measurement type:Image Select the numerator objects:KV1.3 positive cells Select the numerator measurement:Count_KV1.3 positive cells Multiply the above operand by:1 Raise the power of above operand by:1 Select the denominator measurement type:Image Select the denominator objects:CD4 Cells Select the denominator measurement:Count_CD4 Cells Multiply the above operand by:1 Raise the power of above operand by:1 Take log10 of result?:No Multiply the result by:100 Raise the power of result by:1 CalculateMath:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] Name the output measurement:CD4+ of Nuclei Operation:Divide Select the numerator measurement type:Image Select the numerator objects:KV1.3 positive cells Select the numerator measurement:Count_CD4 positive cells Multiply the above operand by:1 Raise the power of above operand by:1 Select the denominator measurement type:Image Select the denominator objects:CD4 Cells Select the denominator measurement:Count_Nuclei Multiply the above operand by:1 Raise the power of above operand by:1 Take log10 of result?:No Multiply the result by:100 Raise the power of result by:1 ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Export the per-image measurements to a comma-delimited file (.csv)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)] 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:Elsewhere...\x7C/Users/lkhansen/Desktop 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, using default file names?:No Press button to select measurements to export: Data to export:Image Combine these object measurements with those of the previous object?:No File name:image.csv Use the object name for the file name?:No