Crystallization Image Analysis on the World Community Grid Christian A. Cumbaa and Igor Jurisica Jurisica Lab, Division of Signaling Biology Ontario Cancer Institute, Toronto, Ontario

Why automate classification of protein crystallization trial images? clear phase separation precipitate skin crystal X garbage unsure Hauptman-Woodward has 65,000,000 images. They want 65,000,000 outcomes.

Why automate classification of protein crystallization trial images? Assist or replace human screening Speed the search phase in protein crystallization Improve throughput, consistency, objectivity Enables data mining and statistical optimization of the crystallization process clear precipitate crystal

Image classification clear phase separation precipitate skin crystal feature extraction classification clear phase separation precipitate skin crystal X garbage unsure feature 1 feature 2 … feature k 100000s of numbers 7 numbers 10s of numbers

Truth data 96 study NESG & SGPP 50% unanimously-scored images 96 proteins X 1536 images hand-scored by 3 experts Presence/absence of 7 independent outcomes NESG & SGPP 15000 images Hand-scored by 1 expert, same scoring system 50% unanimously-scored images 10 most interesting compound categories 96-study NESG (crystals) SGPP (crystals)

Feature set 12375 features computed per image A few basic statistics 50 microcrystal features Euler number features, two variations 11 Blur levels 11 Blur levels X 4 thresholds Image “energy” 11 blur levels 2925 Grey-Level Co-occurrence Matrix features 3 different grey-level quantizations 13 basic functions 25 sample distances ~100 directions Computable from every point in the image Distilled to max range, max mean, min mean ~9500 image-blob features Radon & edge-detection

Our image analysis problem Computing all 12,375 features takes >5 hours for a single image We have 165,000 images in our training set Features must be evaluated for quality The best features (10s or low 100s) must be computed for the remaining 65,000,000 images Massive computing resources required!

Image analysis on the World Community Grid http://www.worldcommunitygrid.org a global, distributed-computing platform for solving large scientific computing problems with human impact 377,627 volunteers contribute idle CPU time of 960,346 devices. Our project: Help Conquer Cancer* launched November 2007. HCC has two goals: To survey a wide tract of image-feature space and identify image analysis algorithms and parameters (features) that best determine crystallization outcome. To perform the necessary image analysis on Hauptman Woodward’s archive of 65,000,000 crystallization trial images. * fundraising slogan of the Ontario Cancer Institute and its parent organization.

Image analysis on the World Community Grid HCC has two phases Phase I: calculate 12,375 features per image on high-priority images, including 165,441 hand-scored images. November 2007-May 2008 analysis on hand-scored images completed January 2008 Phase II: calculate the best features from Phase I on the backlog of HWI images Grid members have contributed 8,919 CPU-years so far to HCC, an average of 55 CPU-years per day.

Phase I: feature assessment

Measuring feature quality feature entropy Treat as random variables: Image class Feature value Measure the mutual information between them (unit: bits) = entropy(class) + entropy(feature) – entropy(class,feature) class entropy

Measuring feature quality clear precipitate (no crystal) other

Information density: microcrystal counts parameter space Clear Precipitate Crystal

Information density: GLCM maximum range parameter space Clear Precipitate Crystal

Information density: Radon-Sobel soft sum parameter space Clear Precipitate Crystal

Information density: Radon-Sobel blob metrics (means) parameter space Clear Precipitate Crystal

Towards Phase II: image classification

Building classifiers handpicked 74 features from peaks in the clear, precipitate and other mutual information plots two classification schemes three-way: clear, non-crystal precipitate, other ten-way: clear, phase separation, phase + precipitate, skin, phase + crystal, precip, precip + skin, precip + crystal, crystal, garbage naïve Bayes model leave-one-out cross-validation

Measuring classifier accuracy: precision and recall crystals false negatives recall “I think these are crystals” precision true positives false positives

Three-class distribution Clear 24.3% Precipitate AND NOT crystal 52.7% Other 23.0% 17095 5258 5109 15928 45112 1819 617 817 27615 clear non-crystal precipitate other non-crystal precipitate machine says true class Confusion matrix

Recall & precision

10-class distribution Clear 33.83% Phase separation 7.00% Phase separation + precipitate 0.50% Skin 0.79% Phase separation + crystal 2.32% Precipitate 34.25% Precipitate + skin 4.95% Precipitate + crystal 7.53% Crystal 8.34% Garbage 0.55%

Confusion matrix machine says true class clear phase separation 313 20 2 52 1 49 4 28 129 3129 1072 90 219 649 586 56 345 888 8 914 2852 611 1063 562 111 85 222 35 29 305 395 2008 692 328 243 33 205 12 385 512 4088 3440 16907 553 617 494 1972 441 10 551 292 88 75 511 37 268 74 105 5 13 6 372 126 3 31 107 81 97 51 32 24 91 503 139 298 668 281 40 2433 1446 1193 92 815 1135 227 25585 clear phase separation phase and precipitate skin phase and crystal precipitate precipitate and skin precipitate and crystal crystal garbage machine says true class

Recall & precision

Acknowledgements Hauptman-Woodward Medical Research Institute George DeTitta, Joe Luft, Eddie Snell, Mike Malkowski, Angela Lauricella, Max Thayer, Raymond Nagel, Steve Potter, and the 96-study reviewers. World Community Grid Bill Bovermann, Viktors Berstis, Jonathan D. Armstrong, Tedi Hahn, Kevin Reed, Keith J. Uplinger, Nels Wadycki IBM Deep Computing: Jerry Heyman Jurisica Lab: Richard Lu All crystallization images were generated at the High-Throughput Screening lab at The Hauptman-Woodward Institute. Funding from NIH U54 GM074899 Genome Canada IBM NSERC (and earlier work from) NIH P50 GM62413 CITO