CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:20140723174500 GitHash:6c2d896 ModuleCount:13 HasImagePlaneDetails:False Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Select processed, rotated single-channel 8-bit TIFF files of entire microarrays here.\'\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\'Entering metadata now for each image eases large-scale analysis and enables recognition of each of the channels. Therefore, ensure the naming scheme of the cell microarray image files is as follows\x3A ---.tif. For example, the green channel from the 3rd slide treated with TGF in experiment 2 might be named "e2-s3-tgf-green.tif".\'\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:(?P.+)-(?P.+)-(?P.+)-(?P.+).tif Regular expression:(?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 endwith "tif") 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:5|show_window:False|notes:\x5B\'\'\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:RAW Match metadata:\x5B{u\'blue\'\x3A u\'Experiment\', u\'green\'\x3A u\'Experiment\'}, {u\'blue\'\x3A u\'Condition\', u\'green\'\x3A u\'Condition\'}, {u\'blue\'\x3A u\'Slide\', u\'green\'\x3A u\'Slide\'}\x5D Image set matching method:Metadata Set intensity range from:Image bit-depth Assignments count:2 Single images count:0 Select the rule criteria:or (metadata does Channel "blue") Name to assign these images:blue Name to assign these objects:Cell Select the image type:Grayscale image Set intensity range from:Image bit-depth Retain outlines of loaded objects?:No Name the outline image:LoadedOutlines Select the rule criteria:or (metadata does Channel "green") Name to assign these images:green Name to assign these objects:Cytoplasm Select the image type:Grayscale image Set intensity range from:Image bit-depth Retain outlines of loaded objects?:No Name the outline image:LoadedOutlines Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Do you want to group your images?:Yes grouping metadata count:3 Metadata category:Experiment Metadata category:Slide Metadata category:Condition IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify blue objects (i.e., nuclei).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:blue Name the primary objects to be identified:blue_objects Typical diameter of objects, in pixel units (Min,Max):5,40 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 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:5 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:blue_out 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:.5 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 Threshold setting version:1 Threshold strategy:Automatic Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1 Threshold correction factor:1 Lower and upper bounds on threshold:0.05,1.0 Approximate fraction of image covered by objects?:0.1 Manual threshold:0.0 Select the measurement to threshold with:None Select binary image:MoG Adaptive Masking objects:From image 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 IdentifySecondaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\'Identify green objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input objects:blue_objects Name the objects to be identified:green_objects Select the method to identify the secondary objects:Propagation Select the input image:green Number of pixels by which to expand the primary objects:10 Regularization factor:0.05 Name the outline image:green_out Retain outlines of the identified secondary objects?:Yes Discard secondary objects touching the border 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 Fill holes in identified objects?:Yes Threshold setting version:1 Threshold strategy:Automatic Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing 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:0.0 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 MeasureObjectIntensity:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure intensity features from blue objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Hidden:1 Select an image to measure:blue Select objects to measure:blue_objects MeasureObjectIntensity:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure intensity features from green objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Hidden:1 Select an image to measure:green Select objects to measure:green_objects ExportToSpreadsheet:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Export measurements to a comma-delimited file (CSV). The measurements made for each channel will be saved to separate CSV files in addition to the per-image CSV files.\'\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 Limit output to a size that is allowed in Excel?: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/home/kkaylan/Documents/school/uiuc/underhill/experiments/2014_0715-arrayed_tgf_beta/analysis 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 to export: Representation of Nan/Inf:NaN Add a prefix to file names?:No Filename prefix\x3A:MyExpt_ Overwrite 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 OverlayOutlines:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create outlines of blue objects.\'\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:blue_out Name the output image:blue_out_image Outline display mode:Color Select method to determine brightness of outlines:Max of image Width of outlines:1 Select outlines to display:None Select outline color:yellow Load outlines from an image or objects?:Objects Select objects to display:blue_objects OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create outlines of green objects.\'\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:green_out Name the output image:green_out_image Outline display mode:Color Select method to determine brightness of outlines:Max of image Width of outlines:1 Select outlines to display:None Select outline color:yellow Load outlines from an image or objects?:Objects Select objects to display:green_objects SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images of blue objects.\'\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:blue_out_image 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:blue 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:outlines Saved file format:tif Output file location:Default Output Folder\x7C Image bit depth:8 Overwrite existing files without warning?:Yes When to save:Every cycle Rescale the images? :Yes Save as grayscale or color image?:Grayscale Select colormap:Greys Record the file and path information to the saved image?:No Create subfolders in the output folder?:No Base image folder:Elsewhere...\x7C Saved movie format:avi SaveImages:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images of green objects.\'\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:green_out_image 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:green 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:outlines Saved file format:tif Output file location:Default Output Folder\x7C Image bit depth:8 Overwrite existing files without warning?:Yes When to save:Every cycle Rescale the images? :Yes Save as grayscale or color image?:Grayscale Select colormap:Greys Record the file and path information to the saved image?:No Create subfolders in the output folder?:No Base image folder:Elsewhere...\x7C Saved movie format:avi