Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas.

Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas.
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DOI:
10.1371/journal.pone.0050514
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发表时间:
2012
期刊:
影响因子:
3.7
通讯作者:
Murphy RF
Murphy RF
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Li J;Newberg JY;Uhlén M;Lundberg E;Murphy RF

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人类蛋白质图谱包含免疫荧光图像,显示了数千种蛋白质的亚细胞位置。目前通过目视检查对其进行注释。在本文中,我们描述了自动化的方法来分析图像和使用它们来改善注释。我们首先训练分类器来识别带注释的模式。通过根据分类器的置信度对蛋白质进行排名,我们生成了一个蛋白质列表,这些蛋白质是复检的强有力候选者。与此同时,我们应用层次聚类对蛋白质进行分组,并识别出其注释与其簇中其余蛋白质不一致的蛋白质。这些蛋白质由最初的注释者重新检查,并且很大一部分蛋白质的注释发生了变化。结果表明,自动化的方法可以提供一个重要的补充视觉注释。
The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation.
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