single_cell_protein_quantification.cppipe

CellProfiler Pipeline: http://www.cellprofiler.org
Version:5
DateRevision:407
GitHash:
ModuleCount:19
HasImagePlaneDetails:False

Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['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.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    :
    Filter images?:No filtering
    Select the rule criteria:and (extension does istif) (directory doesnot containregexp "[\\\\/]\\.")

Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['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.']|batch_state:array([], 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<Time>.*)_(?P<Well>.*)_(?P<Stack>.*)_ch(?P<ChannelNumber>[0-9]{1,2})
    Regular expression to extract from folder name:(?P<Date>[0-9]{4}_[0-9]{2}_[0-9]{2})$
    Extract metadata from:All images
    Select the filtering criteria:and (file does contain "")
    Metadata file location:Elsewhere...|
    Match file and image metadata:[]
    Use case insensitive matching?:No
    Metadata file name:
    Does cached metadata exist?:No

NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['DNA: DNA stained with DAPI']|batch_state:array([], 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:[]
    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 "ch00")
    Name to assign these images:DAPI
    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 "ch01")
    Name to assign these images:GFP
    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 (file does contain "ch02")
    Name to assign these images:mCherry
    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:['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.']|batch_state:array([], 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:14|show_window:False|notes:['Identify nuclei']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:DAPI
    Name the primary objects to be identified:Nuclei
    Typical diameter of objects, in pixel units (Min,Max):10,100
    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: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
    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
    Display accepted local maxima?:No
    Select maxima color:Blue
    Use advanced settings?:Yes
    Threshold setting version:12
    Threshold strategy:Global
    Thresholding method:Otsu
    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
    Log transform before thresholding?:No
    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
    Size of adaptive window:400
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2.0
    Thresholding method:Otsu

GrayToColor:[module_num:6|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['Merge the 3 channels to RGB']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select a color scheme:RGB
    Rescale intensity:Yes
    Select the image to be colored red:mCherry
    Select the image to be colored green:GFP
    Select the image to be colored blue:DAPI
    Name the output image:Merge
    Relative weight for the red image:1.0
    Relative weight for the green image:1.0
    Relative weight for the blue image:1.0
    Select the image to be colored cyan:Leave this black
    Select the image to be colored magenta:Leave this black
    Select the image to be colored yellow:Leave this black
    Select the image that determines brightness:Leave this black
    Relative weight for the cyan image:1.0
    Relative weight for the magenta image:1.0
    Relative weight for the yellow image:1.0
    Relative weight for the brightness image:1.0
    Hidden:1
    Image name:None
    Color:#ff0000
    Weight:1.0

ColorToGray:[module_num:7|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['Convert merged image from RGB to GrayScale']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Merge
    Conversion method:Combine
    Image type:RGB
    Name the output image:Merge_Gray
    Relative weight of the red channel:1.0
    Relative weight of the green channel:1.0
    Relative weight of the blue channel:1.0
    Convert red to gray?:Yes
    Name the output image:None
    Convert green to gray?:Yes
    Name the output image:polyA
    Convert blue to gray?:Yes
    Name the output image:DNA
    Convert hue to gray?:Yes
    Name the output image:OrigHue
    Convert saturation to gray?:Yes
    Name the output image:OrigSaturation
    Convert value to gray?:Yes
    Name the output image:OrigValue
    Channel count:1
    Channel number:1
    Relative weight of the channel:1.0
    Image name:Channel1

IdentifySecondaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['Identify cells']|batch_state:array([], 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:Merge_Gray
    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:12
    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
    Log transform before thresholding?:Yes
    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

EnhanceOrSuppressFeatures:[module_num:9|svn_version:'Unknown'|variable_revision_number:7|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:mCherry
    Name the output image:Enhanced_mCherry
    Select the operation:Enhance
    Feature size:5
    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
    Rescale result image:No

EnhanceOrSuppressFeatures:[module_num:10|svn_version:'Unknown'|variable_revision_number:7|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:GFP
    Name the output image:Enhanced_GFP
    Select the operation:Enhance
    Feature size:5
    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
    Rescale result image:No

IdentifyPrimaryObjects:[module_num:11|svn_version:'Unknown'|variable_revision_number:14|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:mCherry
    Name the primary objects to be identified:mCherry_dots
    Typical diameter of objects, in pixel units (Min,Max):1,50
    Discard objects outside the diameter range?:No
    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
    Display accepted local maxima?:No
    Select maxima color:Blue
    Use advanced settings?:Yes
    Threshold setting version:12
    Threshold strategy:Global
    Thresholding method:Robust Background
    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
    Log transform before thresholding?:No
    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:Minimum Cross-Entropy

IdentifyPrimaryObjects:[module_num:12|svn_version:'Unknown'|variable_revision_number:14|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:GFP
    Name the primary objects to be identified:GFP_dots
    Typical diameter of objects, in pixel units (Min,Max):1,50
    Discard objects outside the diameter range?:No
    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
    Display accepted local maxima?:No
    Select maxima color:Blue
    Use advanced settings?:Yes
    Threshold setting version:12
    Threshold strategy:Global
    Thresholding method:Robust Background
    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
    Log transform before thresholding?:No
    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:Minimum Cross-Entropy

MaskImage:[module_num:13|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:mCherry
    Name the output image:Masked_mCherry
    Use objects or an image as a mask?:Objects
    Select object for mask:mCherry_dots
    Select image for mask:None
    Invert the mask?:No

MaskImage:[module_num:14|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:GFP
    Name the output image:Masked_GFP
    Use objects or an image as a mask?:Objects
    Select object for mask:GFP_dots
    Select image for mask:None
    Invert the mask?:No

MeasureObjectIntensity:[module_num:15|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['Measure mCherry and GFP intensities in each cell']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select images to measure:Masked_GFP, Masked_mCherry
    Select objects to measure:Cells

MeasureObjectSizeShape:[module_num:16|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Measure the sizes and shapes of the cells to make sure the cells are normal']|batch_state:array([], dtype=uint8)|enabled:False|wants_pause:False]
    Select object sets to measure:Cells
    Calculate the Zernike features?:Yes
    Calculate the advanced features?:No

OverlayOutlines:[module_num:17|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Display outlines on a blank image?:No
    Select image on which to display outlines:Merge
    Name the output image:OrigSegmented
    Outline display mode:Color
    Select method to determine brightness of outlines:Max of image
    How to outline:Thick
    Select outline color:yellow
    Select objects to display:Cells
    Select outline color:blue
    Select objects to display:Nuclei

SaveImages:[module_num:18|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:False|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:OrigSegmented
    Select method for constructing file names:From image filename
    Select image name for file prefix:DAPI
    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:_OrigSegmented
    Saved file format:tiff
    Output file location:Elsewhere...|D:\\MIT\\STARmap\\GFP-mCherry\\20210413_protein\\20210415-72hr-RAW\\Plate2-72hrs-result
    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?:No
    Create subfolders in the output folder?:No
    Base image folder:Elsewhere...|
    How to save the series:T (Time)

ExportToSpreadsheet:[module_num:19|svn_version:'Unknown'|variable_revision_number:13|show_window:False|notes:['Export all kinds of measurements of each cell to Excel']|batch_state:array([], dtype=uint8)|enabled:False|wants_pause:False]
    Select the column delimiter:Comma (",")
    Add image metadata columns to your object data file?:Yes
    Add image file and folder names 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:Elsewhere...|D:\\MIT\\STARmap\\STARmap_computation\\GFP-mCherry\\20210513_Neuron\\2021-05-13-Neuron-24hrs-export
    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?:No
    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:Cells
    Combine these object measurements with those of the previous object?:No
    File name:DATA.csv
    Use the object name for the file name?:Yes