A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images.
A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images.
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DOI：
10.1186/1471-2105-7-90
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发表时间：
2006-02-23
影响因子：
3
通讯作者：
Murphy RF
中科院分区：
文献类型：
--
作者：
Chen SC;Murphy RF
了解蛋白质的亚细胞位置对于理解蛋白质在细胞中如何工作至关重要。该位置通常通过荧光显微镜图像的解释来确定。近年来，自动化系统已经被开发用于对这些图像进行一致和客观的解释，使得单个细胞中的蛋白质模式可以被分配到已知的位置类别。虽然这些系统对所有主要亚细胞结构的单细胞图像都具有近乎完美的准确性，但它们区分细胞器（如两种高尔基体蛋白）的子模式的能力并不完美。我们在这里描述的工作的目标是提高自动化系统的能力，以决定两个类似的模式是目前在一个领域的细胞通过考虑一个以上的细胞在同一时间。由于显示相同位置图案的细胞通常聚集在一起，因此考虑多个细胞可以预期改善相似图案之间的区分。我们描述了如何利用实验条件的信息，构建一个图形表示的多个细胞在一个领域。假设一个字段是由少量的类，分类精度可以提高，通过允许计算的概率的每个模式的每个单元受到影响，其相邻的细胞在模型中的概率。我们描述了一种新的方式来允许这种影响发生，在这种情况下，我们调整每个类的先验概率，以反映存在的模式。当这种图形模型方法用于合成的多细胞图像，其中每个细胞的真实类别是已知的，我们观察到，区分相似类别的能力得到改善，而不会遭受任何退化的能力，区分不同的类。该方法的计算复杂性是足够低的，改进的类的分配可以获得12个单元的字段在0.04秒以下的1600兆赫的处理器。我们证明，图形模型可以用来提高多细胞荧光显微镜图像中的亚细胞模式分类的准确性。我们还描述了一种新的算法，从图形模型推断类。的性能和速度表明，该方法将是特别有价值的高通量显微镜图像的分析。我们还预计，它将是有用的，用于分析通常存在于组织图像中的细胞类型的混合物。最后，我们预计该方法可以推广到其他问题。
Knowledge of the subcellular location of a protein is critical to understanding how that protein works in a cell. This location is frequently determined by the interpretation of fluorescence microscope images. In recent years, automated systems have been developed for consistent and objective interpretation of such images so that the protein pattern in a single cell can be assigned to a known location category. While these systems perform with nearly perfect accuracy for single cell images of all major subcellular structures, their ability to distinguish subpatterns of an organelle (such as two Golgi proteins) is not perfect. Our goal in the work described here was to improve the ability of an automated system to decide which of two similar patterns is present in a field of cells by considering more than one cell at a time. Since cells displaying the same location pattern are often clustered together, considering multiple cells may be expected to improve discrimination between similar patterns. We describe how to take advantage of information on experimental conditions to construct a graphical representation for multiple cells in a field. Assuming that a field is composed of a small number of classes, the classification accuracy can be improved by allowing the computed probability of each pattern for each cell to be influenced by the probabilities of its neighboring cells in the model. We describe a novel way to allow this influence to occur, in which we adjust the prior probabilities of each class to reflect the patterns that are present. When this graphical model approach is used on synthetic multi-cell images in which the true class of each cell is known, we observe that the ability to distinguish similar classes is improved without suffering any degradation in ability to distinguish dissimilar classes. The computational complexity of the method is sufficiently low that improved assignments of classes can be obtained for fields of twelve cells in under 0.04 second on a 1600 megahertz processor. We demonstrate that graphical models can be used to improve the accuracy of classification of subcellular patterns in multi-cell fluorescence microscope images. We also describe a novel algorithm for inferring classes from a graphical model. The performance and speed suggest that the method will be particularly valuable for analysis of images from high-throughput microscopy. We also anticipate that it will be useful for analyzing the mixtures of cell types typically present in images of tissues. Lastly, we anticipate that the method can be generalized to other problems.
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影响因子：
46.9
作者：
Vazquez, A;Flammini, A;Vespignani, A
通讯作者：
Vespignani, A
影响因子：
7
作者：
Conrad, C;Erfle, H;Eils, R
通讯作者：
Eils, R
影响因子：
5.8
作者：
Boland, MV;Murphy, RF
通讯作者：
Murphy, RF
DOI：
10.1023/b:vlsi.0000003028.71666.44
发表时间：
2003-11-01
期刊：
JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
影响因子：
--
作者：
Murphy, RF;Velliste, M;Porreca, G
通讯作者：
Porreca, G
影响因子：
10.5
作者：
Kumar, A;Agarwal, S;Snyder, M
通讯作者：
Snyder, M