A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.
BACKGROUND
High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise.


RESULTS
We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A.


BACKGROUND
Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Lin
Yu-Shi
YS
Institute of Information Science, Academia Sinica, Taipei, Taiwan.
Lin
Chung-Chih
CC
Tsai
Yuh-Show
YS
Ku
Tien-Chuan
TC
Huang
Yi-Hung
YH
Hsu
Chun-Nan
CN
D054378:Acetogenins / Q000378:metabolism; D002138:Calibration; D022082:Cellular Structures / Q000648:ultrastructure*; D019295:Computational Biology / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008928:Mitochondria / Q000648:ultrastructure
D054378:Acetogenins