Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Image Analysis on cDNA Image Analysis on cDNA Microarray Data Microarray Data Demo of Spot Demo of Spot Jean Yang October 24, 2000 Genetics & Bioinformatics Meetings
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Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Image Analysis on cDNA Image Analysis on cDNA Microarray DataMicroarray Data
Demo of SpotDemo of Spot
Jean Yang
October 24, 2000Genetics & Bioinformatics
Meetings
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
cDNA clones(probes)
PCR product amplificationpurification
printing
microarray Hybridise target to microarray
mRNA target)
excitation
laser 1laser 2
emission
scanning
analysis
overlay images and normalise
0.1nl/spot
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
ScannerScanner
Laser
PMT
Dye
Glass Slide
Objective Lens
Detector lens
Pinhole
Beam-splitter
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
After normalisationAfter normalisation
In addition, the ranking of the genes stays pretty much the same.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Practical Problems 1Practical Problems 1• Comet Tails• Likely caused by
insufficiently rapid immersion of the slides in the succinic anhydride blocking solution.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Practical Problems 2 Practical Problems 2
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Practical Problems 3Practical Problems 3
High Background• 2 likely causes:
– Insufficient blocking.– Precipitation of the
labeled probe.
Weak Signals
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Practical Problems 4Practical Problems 4
Spot overlap:Likely cause: toomuch rehydrationduring post -processing.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Steps in Images ProcessingSteps in Images Processing
1. Addressing: locate centers2. Segmentation: classification of pixels either as signal or background. using seeded region growing).3. Information extraction: for each spot of the array, calculates signal intensity pairs, background and quality measures.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
AddressingAddressing
This is the process of assigning coordinates to each of the spots.
Automating this part of
the procedure permits high throughput analysis.
4 by 4 grids19 by 21 spots per grid
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
AddressingAddressing
4 by 4 grids
Within the same batch of print runs. Estimate the translation of grids
Other problems:-- Mis-registration-- Rotation-- Skew in the array
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Segmentation Segmentation methodsmethods
• Fixed circles• Adaptive Circle• Adaptive Shape
– Edge detection.– Seeded Region Growing. (R. Adams and
L. Bishof (1994) :Regions grow outwards from the seed points preferentially according to the difference between a pixel’s value and the running mean of values in an adjoining region.
• Histogram Methods– Adaptive threshold.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
SeedsSeeds
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Limitation of circular segmentationLimitation of circular segmentation
—Small spot—Not circular
Results from SRG
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Local BackgroundsLocal Backgrounds
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Statistical Software - RStatistical Software - R
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
How are we comparing?How are we comparing?
• Foreground and Background Intensities
• M vs A plot• Within slide variability• Between slide variability• Ability to differentiate important
genes from noise
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Foreground and Background Foreground and Background comparisoncomparison
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Does the image analysis matter?Does the image analysis matter?
Spot.nbgSpot.nbg Spot.morphSpot.morph
Spot.valleySpot.valley ScanAlyzeScanAlyze
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Background makes a differenceBackground makes a difference
Local surrounding SA 12 14QA.fix 18 23QA.hist 9 8QA.adp 27 26
Others S.morph 9 9S.const 14 14
Medians of the SD of log2(R/G) for 8 replicated spots multiplied by 100and rounded to the nearest integer.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Between slide variabilityBetween slide variability
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
TT
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
AcknowledgmentsAcknowledgments
Terry SpeedTerry SpeedMichael BuckleyMichael Buckley
Ryan LagerstormRichard BeareHugues TalbotKevin Cheong
Matt Callow (LBL)
Percy Luu (USB)Dave Lin (USB)Vivian Pang (USB)Elva Diaz (USB)
WEHI Bioinformatics groupWEHI Bioinformatics group
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Steps in Images ProcessingSteps in Images Processing
1. Addressing: locate centers2. Segmentation: classification of pixels either as signal or background. using seeded region growing).
3. Information extraction: for each spot of the array, calculates signal intensity pairs, background and quality measures.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Steps in Image Processing Steps in Image Processing
• Spot Intensities– mean (pixel intensities).– median (pixel intensities).– Pixel variation ( IQR of log
(pixel intensities).• Background values
– Local – Morphological opening– Constant (global)– None
• Quality Information
Signal
Background
3. Information Extraction
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
AddressingAddressing
Registration
Registration
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,
Quality MeasurementsQuality Measurements• Array
– Correlation between spot intensities.– Percentage of spots with no signals.– Distribution of spot signal area.
• Spot– Signal / Noise ratio.– Variation in pixel intensities.– Identification of “bad spot” (spots with no signal).
• Ratio (2 spots combined)– Circularity
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,