Classification of Protein Localization Patterns in 3-D

Meel Velliste

Carnegie Mellon University

Introduction

• Need a Systematics for Protein Localization

• Need Microscope Automation

• Feature based classification of Localization Patterns

• Pioneering work done with 2D images

• Now exploring classification of 3D images

Ten Major Classes of Protein Localization

Features• Derive Numeric

Features based on:– Morphology– Texture– Moments

feature1 feature2 ... featureNImage1 0.3489 0.1294 ... 1.9012Image2 0.4985 0.4823 ... 1.8390... ...ImageM 1.8245 0.8290 ... 0.9018

Classification• Tried:

– Classification Trees– kNN– BPNN

• BPNN was the most successful with 84% correct classification rate

This is acyto-skeletal protein

Results of 2-D ClassificationOutput of Classifier

True Class DN ER Gia GP LA Mit Nuc Act TfR TubDNA 98 1 0 0 0 0 0 0 1 0ER 0 87 2 0 1 5 0 0 1 3

Giantin 0 0 84 12 1 1 1 0 1 0GPP130 0 0 20 72 1 2 3 0 2 0LAMP2 0 0 5 1 74 0 3 0 15 2Mitoch. 0 8 1 0 0 81 0 0 5 5

Nucleolin 0 0 0 1 1 0 98 0 0 0Actin 0 0 0 0 0 1 0 96 1 3TfR 0 2 2 0 18 4 0 2 65 7

Tubulin 0 2 1 0 2 7 0 1 5 84

Overall accuracy = 84%

Motivation for 3-D Classification

• Cells are 3-dimensional objects

• 2-D images take a slice through the cell

• Resultant images are largely dependent on the z-position of the slice

• Losing a lot of 3-D structural information

The Approach

• Acquire a set of 3-D images for the same 10 classes as used in the 2-D work (have 5 now)

• Calculate equivalent features to what was used with the 2-D images

• Compare performance

3-D Classification• Used a subset of the same Morphological

features as used with 2-D patterns:– Number of Objects– Euler Number– Average Object Size– Standard Deviation of Object sizes– Ratio of the Largest to the Smallest Object Size– Average Distance of Objects from COF– Standard Deviation of Object Distances from COF– Ratio of the Largest to Smallest Object Distance

3-D Classification ResultsOutput of Classifier

True Class DN ER Gia GP LA Mit Nuc Act TfR TubDNA 99 0 0 0 0ER

Giantin 0 97 2 0 0GPP130 0 54 45 0 0LAMP2 1 0 0 82 16Mitoch.

NucleolinActin 2 0 0 4 95TfR

Tubulin

Overall accuracy = 84% (95% with GPP=Giantin)

2-D Results — Same 8 FeaturesOutput of Classifier

True Class DN ER Gia GP LA Mit Nuc Act TfR TubDNA 99 0 0 1 0ER

Giantin 0 47 47 5 1GPP130 1 41 57 2 0LAMP2 1 7 1 89 3Mitoch.

NucleolinActin 0 0 0 4 95TfR

Tubulin

Overall accuracy = 84% (95% with GPP=Giantin)

Conclusion

• Further work needed to determine if there is any advantage to using 3D images over 2D images

• Need to design new features to take advantage of extra information in 3D images

Acknowledgements

• Elizabeth Wu - acquired the 3-D image set

• Michael V. Boland & Robert F. Murphy - pioneering work on 2-D images