CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:300 GitHash: ModuleCount:20 HasImagePlaneDetails:False Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] : Filter images?:Images only Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Extract metadata?:Yes Metadata data type:Text Metadata types:{} Extraction method count:1 Metadata extraction method:Extract from file/folder names Metadata source:File name Regular expression to extract from file name:^(?P.*)_i(?P\x5B0-9\x5D{1,2}j\x5B0-9\x5D{1,3}).tiColour(?P\x5B0-9\x5D{1,2}).tif Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ Extract metadata from:All images Select the filtering criteria:and (file does contain "") Metadata file location: Match file and image metadata:\x5B\x5D Use case insensitive matching?:No NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Assign a name to:Images matching rules Select the image type:Grayscale image Name to assign these images:DNA Match metadata:\x5B\x5D Image set matching method:Order Set intensity range from:Image metadata Assignments count:3 Single images count:0 Maximum intensity:255.0 Process as 3D?:No Relative pixel spacing in X:1.0 Relative pixel spacing in Y:1.0 Relative pixel spacing in Z:1.0 Select the rule criteria:and (or (metadata does ch "2")) Name to assign these images:RNASignal Name to assign these objects:Cell Select the image type:Grayscale image Set intensity range from:Image metadata Maximum intensity:255.0 Select the rule criteria:and (metadata does ch "3") Name to assign these images:NuceliSignal Name to assign these objects:Nucleus Select the image type:Grayscale image Set intensity range from:Image metadata Maximum intensity:255.0 Select the rule criteria:and (metadata does ch "4") Name to assign these images:glom Name to assign these objects:Cytoplasm Select the image type:Grayscale image Set intensity range from:Image metadata Maximum intensity:255.0 Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Do you want to group your images?:Yes grouping metadata count:2 Metadata category:grid Metadata category:Bear ImageMath:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Operation:Invert Raise the power of the result by:1.0 Multiply the result by:1.0 Add to result:0.0 Set values less than 0 equal to 0?:Yes Set values greater than 1 equal to 1?:Yes Ignore the image masks?:No Name the output image:GlomInverted Image or measurement?:Image Select the first image:glom Multiply the first image by:1.0 Measurement: Image or measurement?:Image Select the second image: Multiply the second image by:1.0 Measurement: IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:GlomInverted Name the primary objects to be identified:Glom Typical diameter of objects, in pixel units (Min,Max):100,4000 Discard objects outside the diameter range?:Yes Discard objects touching the border of the image?:No 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:7.0 Speed up by using lower-resolution image to find local maxima?:Yes 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 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Use advanced settings?:No Threshold setting version:10 Threshold strategy:Global Thresholding method:Minimum cross entropy Threshold smoothing scale:1.3488 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Manual threshold:0.0 Select the measurement to threshold with:None Two-class or three-class thresholding?:Two classes Assign pixels in the middle intensity class to the foreground or the background?:Foreground Size of adaptive window:50 Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 Thresholding method:Otsu ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Operation:Invert Raise the power of the result by:1.0 Multiply the result by:1.0 Add to result:0.0 Set values less than 0 equal to 0?:Yes Set values greater than 1 equal to 1?:Yes Ignore the image masks?:No Name the output image:RNAInveted Image or measurement?:Image Select the first image:RNASignal Multiply the first image by:1.0 Measurement: Image or measurement?:Image Select the second image: Multiply the second image by:1.0 Measurement: EnhanceOrSuppressFeatures:[module_num:8|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] Select the input image:RNAInveted Name the output image:RNASupress Select the operation:Suppress Feature size:6 Feature type:Speckles Range of hole sizes:1,10 Smoothing scale:2.0 Shear angle:0.0 Decay:0.95 Enhancement method:Tubeness Speed and accuracy:Fast IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:RNASupress Name the primary objects to be identified:RNASpots Typical diameter of objects, in pixel units (Min,Max):10,60 Discard objects outside the diameter range?:Yes Discard objects touching the border of the image?:No 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:7.0 Speed up by using lower-resolution image to find local maxima?:Yes 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 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Use advanced settings?:No Threshold setting version:10 Threshold strategy:Global Thresholding method:RobustBackground Threshold smoothing scale:1.3488 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.5,1.0 Manual threshold:0.0 Select the measurement to threshold with:None Two-class or three-class thresholding?:Two classes Assign pixels in the middle intensity class to the foreground or the background?:Foreground Size of adaptive window:50 Lower outlier fraction:0.2 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 Thresholding method:Otsu ImageMath:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Operation:Invert Raise the power of the result by:1.0 Multiply the result by:1.0 Add to result:0.0 Set values less than 0 equal to 0?:Yes Set values greater than 1 equal to 1?:Yes Ignore the image masks?:No Name the output image:NucleiInverted Image or measurement?:Image Select the first image:NuceliSignal Multiply the first image by:1.0 Measurement: Image or measurement?:Image Select the second image: Multiply the second image by:1.0 Measurement: EnhanceOrSuppressFeatures:[module_num:11|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] Select the input image:NucleiInverted Name the output image:EnhanceOrSuppressFeatures Select the operation:Suppress Feature size:10 Feature type:Speckles Range of hole sizes:1,10 Smoothing scale:2.0 Shear angle:0.0 Decay:0.95 Enhancement method:Tubeness Speed and accuracy:Fast IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:NucleiInverted Name the primary objects to be identified:Nucleioutlines Typical diameter of objects, in pixel units (Min,Max):20,60 Discard objects outside the diameter range?:Yes Discard objects touching the border of the image?:Yes 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:7.0 Speed up by using lower-resolution image to find local maxima?:Yes 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 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Use advanced settings?:No Threshold setting version:10 Threshold strategy:Global Thresholding method:Minimum cross entropy Threshold smoothing scale:1.3488 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Manual threshold:0.0 Select the measurement to threshold with:None Two-class or three-class thresholding?:Two classes Assign pixels in the middle intensity class to the foreground or the background?:Foreground Size of adaptive window:50 Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 Thresholding method:Otsu MeasureObjectSizeShape:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select objects to measure:Nucleioutlines Calculate the Zernike features?:Yes FilterObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the objects to filter:Nucleioutlines Name the output objects:FilterNuclei Select the filtering mode:Measurements Select the filtering method:Limits Select the objects that contain the filtered objects:None Select the location of the rules or classifier file:Elsewhere...\x7C Rules or classifier file name:rules.txt Class number:1 Measurement count:1 Additional object count:0 Assign overlapping child to:Both parents Select the measurement to filter by:AreaShape_MajorAxisLength Filter using a minimum measurement value?:No Minimum value:0.0 Filter using a maximum measurement value?:Yes Maximum value:44 RelateObjects:[module_num:15|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] Parent objects:Glom Child objects:FilterNuclei Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:Yes Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:16|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] Parent objects:Glom Child objects:RNASpots Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:Yes Calculate distances to other parents?:No Parent name:None FilterObjects:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the objects to filter:FilterNuclei Name the output objects:Nucingloms Select the filtering mode:Measurements Select the filtering method:Limits Select the objects that contain the filtered objects:None Select the location of the rules or classifier file:Elsewhere...\x7C Rules or classifier file name:rules.txt Class number:1 Measurement count:1 Additional object count:0 Assign overlapping child to:Both parents Select the measurement to filter by:Parent_Glom Filter using a minimum measurement value?:Yes Minimum value:1 Filter using a maximum measurement value?:No Maximum value:1.0 FilterObjects:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the objects to filter:RNASpots Name the output objects:RNAinGloms Select the filtering mode:Measurements Select the filtering method:Limits Select the objects that contain the filtered objects:None Select the location of the rules or classifier file:Elsewhere...\x7C Rules or classifier file name:rules.txt Class number:1 Measurement count:1 Additional object count:0 Assign overlapping child to:Both parents Select the measurement to filter by:Parent_Glom Filter using a minimum measurement value?:Yes Minimum value:1 Filter using a maximum measurement value?:No Maximum value:1.0 RelateObjects:[module_num:19|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] Parent objects:Nucingloms Child objects:RNAinGloms Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:Yes Calculate distances to other parents?:No Parent name:None ExportToSpreadsheet:[module_num:20|svn_version:\'Unknown\'|variable_revision_number:12|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the column delimiter:Comma (",") Add image metadata columns to your object data file?:Yes Select the 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\x7C 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 measurement types?:Yes Press button to select measurements: Representation of Nan/Inf:NaN Add a prefix to file names?:Yes Filename prefix:GlomTest Overwrite existing files without warning?:No 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