using cellprofiler for biological image analysiscyto+u+webinar...user-friendly interface ... •...
TRANSCRIPT
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Mark-Anthony Bray, Ph.D
Imaging Platform, Broad Institute
Cambridge, Massachusetts, [email protected] 0.4233
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Using CellProfiler for Biological Image Analysis
Quantitative Analysis of Large-Scale Biological Image Data
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Summary
• Background on image-based screening
• Introduction to CellProfiler considerations in image analysis
• Construction and use of a pipeline for analyzing typical image data
• Measurement export and preparation for additional analysis
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Images Contain A Wealth Of Information
http://www.microscopyu.com Image: Javier Irazoqui
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Visual Appearance Indicates Biological State
• Automatic image analysis is– Objective
– Quantitative, with statistics
– Can measure multiple properties at once for every cell
– Distinguishes subtle changes, even those undetectable by eye
– Faster, less tedious
• Images contain a wealth of biological information
• That information can be quantified
Localization
… + hundreds of other features
mRNA or
protein levels
morphology
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Cells or organisms in multiwell plates, each well treated with a gene or chemical perturbant
Automated microscopy
(any manufacturer)
High-Content Screening
Data exploration
& machine learning
Anne
Carpenter
Ray
Jones
Cell measurements
(size, shape, intensity, texture, etc.)
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Software Overview
• Available from www.cellprofiler.org• Free, open source (Python)• Software available for Windows, Mac and Linux
Image Analysis &
Quantification
Image-centric
Data Analysis
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CellProfiler: Overview
• Process large sets of images
• Identifies and measures objects
• Export data for further analysis
• Goal: Provide powerful image analysis methods with a user-friendly interface
• Philosophy: Measure everything, ask questions later...
• Support data analysis based on individual cells
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Typical CellProfiler Pipeline Workflow
• For image-based assays, the basic objective is always to – Identify cells/organisms
– Measure feature(s) of interest
• The uniqueness of each assay comes in– Deciding what compartments
to identify and how to identify them
– Determining which measure(s) are most useful to identify interesting samples
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Typical CellProfiler Pipeline Workflow
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The CellProfiler Interface
• Pipeline panel: Displays modules in pipeline– Modules executed in order from top to bottom
Change module position
Add or remove modules
Module help
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Load pipeline by double-clicking on it
View images by double-clicking on the filename
The CellProfiler Interface
• File panel: Displays files in default image folder
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The CellProfiler Interface
• The figure window has additional menu options
• Toolbar menu: Pan, zoom in/out
• CellProfiler Image Tools– Image Tool (also
displayed by clicking on image)
– Interactive zoom
– Show pixel data (location, intensity)
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The CellProfiler Interface
• Folder panel: Change default input and output directories– Usually these should be separate folders
Input folder: Contains images to be analyzed
Output folder: Contains the output file plus exported data and images
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The CellProfiler Interface
• Settings panel: View and change settings for each module– Clicking on a different module updates the settings view
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Module Categories
• File processing: Image input, file output
• Image processing: Often used for pre-processing prior to object identification
• Object processing:Identification, modification of objects of interest
• Measurement: Collection of measurements from objects of interest
• Data Tools: Measurement exploration, measurement output
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The First Module: LoadImages
• Related how? Depending on the imaging device, one file may represent– One channel at one imaging location
– Multiple channels at one imaging location
– Multiple channels at multiple locations
– Etc…
• Loads an image set
A group of related images to be processed
DNA GFP
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The First Module: LoadImages
• Can use text matching to define the difference between images in a set
All images stained for GFP have the text Channel1- in the name
Same for DNA images (Channel2-)
Assign each image a meaningful name for downstream reference
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Object Identification
• Once the images are loaded, how do you find objects of interest?
• Step 1: Distinguish the foreground from the background by picking a good threshold
• Step 2: Identify objects as regions brighter than the threshold
• Step 3: Cut and join objects to “improve” their shape
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Primary Object Identification
• Many options for thresholding, cut and join methods, etc.
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Thresholding
• Definition: Division of the image into background and foreground
• Method: Pick the method that provides the best results– Otsu: Default - Good for readily identifiable foreground / background
– Background, RobustBackground: Good for images in which most of the image is comprised of background
• What is the best threshold value for dividing the intensity into foreground and background pixels?
Pixel values
Fre
qu
en
cy
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Thresholding
• Correction factor
– Multiplication factor applied to threshold
– Adjusts threshold stringency/leniency
– Setting this factor is empirical
• Upper/lower bounds
– Set safety limits on automatic threshold to guards against false positives
– Helpful for unexpected images: Empty wells, images with dramatic artifacts, etc
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Object Separation
• We need to distinguish multiple objects contained in the same “clump”
Images from Carolina Wahlby
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• Once the foreground objects have been identified, what next?
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Object Separation
• Two step process in “de-clumping”1. Identification of the objects in a clump2. Drawing boundaries between the clumped objects
Adjust settings to “de-clump” objects
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Object Separation
– Intensity: Works best if objects are brighter at center, dimmer at edges
– Shape: Works best if objects have indentations where clumps touch (esp. if objects are round)
Peaks
2
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Indentations
• Clump identification: Two options
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Object Separation
– Distance: Draws boundary lines midway between object centers
– Intensity: Draws boundary lines at dimmest line between objects
• Test Mode allows users to view results of all setting combinations
• Drawing boundaries: Two options
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Object Separation
• Additional separation settings: Adjust these settings if objects are being incorrectly split into pieces or merged together
Original image Smoothing filter
size = 4
Smoothing filter
size = 8
• Smoothing: Increase to reduce intensity irregularities which produce over-segmentation of objects
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Object Separation
• Suppress Local Maxima– Smallest distance allowed between object intensity
peaks to be considered one object rather than a clump
– Decrease to reduce improper merging of objects in clumps
Original image Maxima
distance = 4
Maxima
distance = 8
Maxima
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Object Separation
• Adjusting can produce more improper segmentation than it solves
• The proper settings are usually a matter of trial and error– The automatic settings are a good starting point, though
• However….
Original image Smoothing filter
size = 4
Smoothing filter
size = 8
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Filtering Invalid Objects
• See FilterObjects module for more advanced filtering options
Discard objects that fail size criterion or touch the image border
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Primary Object Identification
• Segmented objects are colored– Shows if each object has
been identified and separated properly
• Outlines: Valid objects– Green: Valid
– Yellow: Invalid – Touching border
– Red: Invalid – Size criterion
• Also outputs object count
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Secondary Object Identification
• Goal: Identify cell boundaries by “growing” primary objects– Nuclei typically more uniform in shape, more easily separated than cells
• Approach: Segment nuclei → Seeds for cell segmentation by using a cell stain channel
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Secondary Object Identification
• Methods– Distance-N: Ignores image
information• Useful in cases where no cell
stain is present
– Watershed, propagate, Distance-B: Uses image information
• Finds dividing lines between objects and background / neighbors
• Test mode allows user to view results of all methods
Propagation
Distance-N
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Tertiary Object Identification
• Goal: Identify tertiary objects by removing the primary objects from secondary objects
– “Subtract” the nuclei objects from cell objects to obtain cytoplasm
Cells Nuclei Cytoplasm— ═
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Pixel-Based Image Classification
• For images where a threshold cannot be found…
• CellProfiler is packaged with ilastik, a pixel-based classification tool– User manually labels regions of image– ilastik uses features to distinguish regions and create a classifier– Classifier used as input into ClassifyPixels module– Currently, Windows only
DIC ilastik Foreground/background mask
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Measurement Modules: Object Morphology
Select the objects to measure
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Module: MeasureObjectAreaShape
• Goal: Measure morphological features such as – Area
– Perimeter
– Eccentricity
– MajorAxisLength
– MinorAxisLength
– Orientation
– FormFactor: Compactness measure, circle = 1, line = 0
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Measurement Modules: Object Intensity
Select the image to measure from
Select the objects to measure
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Module: MeasureObjectIntensity
• Goal: Measure object intensity features such as
– Integrated intensity: Sum of the pixel intensities within an object
– Mean, median, standard deviation intensities
– Maximal and minimal pixel intensities
– Lower/Upper quartile
• The object intensity may be obtained from any image, not just the image used to identify the object
– Example: Ph3 intensity may be measured using the nuclei objects
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Measurement Modules: Object Texture
Select the image to measure from
Select the objects to measure
Select the spatial scale
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MeasureObjectTexture
• Goal: Determine whether the staining pattern is smooth on a particular scale
• Selection of the appropriate texture scale is essentially empirical
– A higher number measures larger patterns of texture
– Smaller numbers measure more localized (finer) patterns of texture
• Can also add several texture modules to the pipeline, each measuring a different texture scale
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Other Measurement Modules
• CalculateMath: Arithmetic operations for measurements
• CalculateStatistics: Assay quality (V and Z' factors) and dose response data (EC50) for all measurements
• Image-based measures– MeasureImageAreaOccupied
– MeasureImageGranularity
– MessureImageIntensity
• Object-based measures– MeasureCorrelation
– MeasureObjectNeighbors
– MeasureRadialDistribution
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Data Export Modules
• User may output images or image measurements
Select the objects to export
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Measurement Display
• The averagemeasurements for all objects in the image are displayed in the figure window
• However, the individualmeasurements for each object are stored in the output file
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Data Export Modules
• Goal: Retain images of intermediate image processing steps for quality control or save measurements for later analysis and exploration
• SaveImages: Writes an image to a file– Intermediate images in the pipeline are not saved unless
requested
– Choice of many image formats to write → module can be used as an image format converter
• ExportToSpreadsheet: Export measurements as a comma-separated file readable by spreadsheet programs
• ExportToDatabase: Export measurements as a per-object and per-table plus configuration file for a MySQL or SQLite database
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Cluster Computing
• If processing time is too great on a single computer, then run the pipeline on a cluster– Install CellProfiler on a computing cluster
– Add the ExportToDatabase module
– Add/configure the CreateBatchFiles module to the end of the pipeline
– Run the pipeline to create a batch file
– Submit the batches to your cluster for processing
– Check the progress of processing
• For really big screens, it is necessary to process images in batches on a computing cluster.
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Megakaryocyte Polyploidization: Leukemia
DNA stain, with
outlines identifying the nuclei
Martha Vokes
Mark
Bray
SU6656
(positive control)
Project in progress
per-cell DNA content (log2)
pro
po
rtio
n o
f ce
lls
SU6656DMSO
DMSO
(negative control)
John Crispino,
Northwestern
University
Jeremy Wen,
postdoc
Status: Identified 206 polyploidization
regulators from 10k compound screen
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Images from BioImage SBS image analysis comparison. Thanks to Ilya Ravkin
Carpenter, et al., Genome Biology, 2006
Measuring Morphology
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Upcoming: CellProfiler 2.1
• Major changes
– Streamlined loading of images and associated data
– Takes advantage of multiple CPU cores, so very large sets of images can now be processed on a regular desktop computer
• Release scheduled for early 2014
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Final Notes
• Where to get help
– Access help from the CellProfiler main window
– Ask for help on the CellProfiler.org forum
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Image assay developmentApply image analysis methods to biological questions
Mark
Bray
Anne
Carpenter David
Logan
Algorithm development & software engineeringDevelop & test new image analysis and data mining methods
and create open-source software tools
IT/Administration
Matthew
Veneskey
Vebjørn
Ljoså
Carolina
Wählby
Carpenter Lab / Broad Institute Imaging Platform
Lee
Kamentsky
Shantanu
Singh
Director
Holger
Hennig
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Acknowledgments
S.D.G.
Free, at www.cellprofiler.org:
Recent funding for this work provided by:
NIH NIGMS (Carpenter: R01 GM089652 and Wahlby: R01 GM095672)
The Broad Institute of Harvard and MIT
Many thanks to our many biology collaborators who
provide images
Contact:[email protected]