CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:300 GitHash: ModuleCount:14 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?:No 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.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 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\'DNA\x3A DNA stained with DAPI\', \'PH3\x3A An antibody for phosphorylated histone H3 correlated with mitosis\', \'cellbody\x3A \'\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 (file does contain "d0.tif") Name to assign these images:DNA 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 (file does contain "d1.tif") Name to assign these images:PH3 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 (file does contain "d2.tif") Name to assign these images:cellbody Name to assign these objects:Cell 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?:No grouping metadata count:1 Metadata category:None IdentifyPrimaryObjects:[module_num:5|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:DNA Name the primary objects to be identified:Nuclei Typical diameter of objects, in pixel units (Min,Max):8,80 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 declumping only 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 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:PH3 Name the primary objects to be identified:PH3 Typical diameter of objects, in pixel units (Min,Max):8,80 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 declumping only 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 RelateObjects:[module_num:7|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:Nuclei Child objects:PH3 Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None IdentifySecondaryObjects:[module_num:8|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 objects:Nuclei Name the objects to be identified:Cells Select the method to identify the secondary objects:Propagation Select the input image:cellbody Number of pixels by which to expand the primary objects:10 Regularization factor:0.05 Discard secondary objects touching the border of the image?:No Discard the associated primary objects?:No Name the new primary objects:FilteredNuclei Fill holes in identified objects?:Yes Threshold setting version:10 Threshold strategy:Global Thresholding method:Otsu Threshold smoothing scale:0.0 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?:Three 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 IdentifyTertiaryObjects:[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] Select the larger identified objects:Cells Select the smaller identified objects:Nuclei Name the tertiary objects to be identified:Cytoplasm Shrink smaller object prior to subtraction?:Yes MeasureObjectIntensity:[module_num:10|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] Hidden:2 Select an image to measure:DNA Select an image to measure:PH3 Select objects to measure:Nuclei Select objects to measure:Cells Select objects to measure:Cytoplasm MeasureObjectSizeShape:[module_num:11|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:Nuclei Select objects to measure:Cells Select objects to measure:Cytoplasm Calculate the Zernike features?:Yes OverlayOutlines:[module_num:12|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] Display outlines on a blank image?:No Select image on which to display outlines:DNA Name the output image:OrigOverlay Outline display mode:Color Select method to determine brightness of outlines:Max of image How to outline:Thick Select outline color:#0080FF Select objects to display:Cells Select outline color:blue Select objects to display:Nuclei Select outline color:yellow Select objects to display:PH3 SaveImages:[module_num:13|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 type of image to save:Image Select the image to save:OrigOverlay Select method for constructing file names:From image filename Select image name for file prefix:DNA 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:_Overlay Saved file format:png Output file location:Default Output Folder\x7C Image bit depth:8-bit integer Overwrite existing files without warning?:Yes When to save:Every cycle Record the file and path information to the saved image?:Yes Create subfolders in the output folder?:No Base image folder:Elsewhere...\x7C ExportToSpreadsheet:[module_num:14|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?:No 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?:No Filename prefix:MyExpt_ Overwrite existing files without warning?:Yes 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