Сравнительный анализ методов сегметнации объектов биомедицинских изображений

Для обработки люминесцентных изображений биологических объектов разработано множество специальных и универсальных алгоритмов, среди которых можно выделить пороговые, градиентные, водораздельные методы и методы, в основе которых лежит вычислительная геометрия Основная проблема разработки методов анализа изображений связана с необходимотью всестороннего тестирования алгоритмов на широком наборе эталонных экспериментальных данных, которые в большинстве случаев технологически трудно получить. Данная задача может быть успешно решена с использованием метода имитационного моделирования . Цели данной – 1) реализация алгоритмов обработки изображений для сегментации ядер клеток, 2) разработка имитационной модели люминесцентных изображений на основе параметров полученных из экспериментов по регистрации биомаркеров цитокератина (CK) в цитоплазме и рецептора эстрогенов (ER) в ядрах клеток

Utilizing microarray spot characteristics to improve cross-species hybridization results

AbstractCross-species hybridization (CSH), i.e., the hybridization of a (target) species RNA to a DNA microarray that represents another (reference) species, is often used to study species diversity. However, filtration of CSH data has to be applied to extract valid information. We present a novel approach to filtering the CSH data, which utilizes spot characteristics (SCs) of image-quantification data from scanned spotted cDNA microarrays. Five SCs that were affected by sequence similarity between probe and target sequences were identified (designated as BS-SCs). Filtration by all five BS-SC thresholds demonstrated improved clustering for two of the three examined experiments, suggesting that BS-SCs may serve for filtration of data obtained by CSH, to improve the validity of the results. This CSH data-filtration approach could become a promising tool for studying a variety of species, especially when no genomic information is available for the target species

Advanced spot quality analysis in two-colour microarray experiments

Background: Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. Findings: We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions. Conclusion: Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions. © 2008 Friederich et al; licensee BioMed Central Ltd

An algorithm for automatic evaluation of the spot quality in two-color DNA microarray experiments

<p>Abstract</p> <p>Background</p> <p>Although DNA microarray technologies are very powerful for the simultaneous quantitative characterization of thousands of genes, the quality of the obtained experimental data is often far from ideal. The measured microarrays images represent a regular collection of spots, and the intensity of light at each spot is proportional to the DNA copy number or to the expression level of the gene whose DNA clone is spotted. Spot quality control is an essential part of microarray image analysis, which must be carried out at the level of individual spot identification. The problem is difficult to formalize due to the diversity of instrumental and biological factors that can influence the result.</p> <p>Results</p> <p>For each spot we estimate the ratio of measured fluorescence intensities revealing differential gene expression or change in DNA copy numbers between the test and control samples. We also define a set of quality characteristics and a model for combining these characteristics into an overall spot quality value. We have developed a training procedure to evaluate the contribution of each individual characteristic in the overall quality. This procedure uses information available from replicated spots, located in the same array or over a set of replicated arrays. It is assumed that unspoiled replicated spots must have very close ratios, whereas poor spots yield greater diversity in the obtained ratio estimates.</p> <p>Conclusion</p> <p>The developed procedure provides an automatic tool to quantify spot quality and to identify different types of spot deficiency occurring in DNA microarray technology. Quality values assigned to each spot can be used either to eliminate spots or to weight contribution of each ratio estimate in follow-up analysis procedures.</p