Dataset Information

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.

ABSTRACT:

Motivation

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.

Availability

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/.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Lin YS

PROVIDER: S-EPMC2881379 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Publications

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.

Lin Yu-Shi YS Lin Chung-Chih CC Tsai Yuh-Show YS Ku Tien-Chuan TC Huang Yi-Hung YH Hsu Chun-Nan CN

Bioinformatics (Oxford, England) 20100601 12

<h4>Motivation</h4>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.<h4>Results</h4>We propose a graph-based approach to cell image analysis. We define gr ...[more]

PMID: 20529919

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Project description:BACKGROUND: Transmembrane ?-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane ?-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane ?-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. RESULTS: We present a novel graph-theoretic model for classification and structure prediction of transmembrane ?-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize ?-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane ?-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject ?-helical bundles with 100% accuracy and ?-barrel lipocalins with 97% accuracy. CONCLUSIONS: We show that it is possible to design models for classification and structure prediction for transmembrane ?-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

Dataset Information

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.

Motivation

Results

Availability

Supplementary information

Publications

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.

Similar Datasets

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