Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images
Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images
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DOI：
10.1093/bioinformatics/btp658
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发表时间：
2010-03-15
期刊：
影响因子：
5.8
通讯作者：
Ohler, Uwe
中科院分区：
文献类型：
--
作者：
Mace, Daniel L.;Varnado, Nicole;Ohler, Uwe
动机：高通量成像的最新进步创造了具有数万个基因表达图像的新大型数据集。捕获这些空间和/或时间表达模式的方法包括原位杂交或荧光记者构造或标签，并且结果仍然经常通过主观定性比较来评估。为了处理可用的大型数据集，必须开发全自动分析方法以正确地归一化和模型空间表达模式。分类：我们已经开发了图像分割和注册方法，以识别和提取从RNA中的空间基因表达模式。果蝇胚胎。这些方法使我们能够在六个时间阶段中对3724个基因的78个621图像进行标准化和提取表达信息。基因表达模式之间的相似性是使用四个评分指标计算的：平均平方误差，HAAR小波距离，相互信息和空间共同信息（SMI）。我们还提出了一种策略，通过使用蒙特卡洛交换采样器生成具有相似空间表达模式的替代数据集来计算两个表达图像之间相似性的重要性。在早期开发时间阶段的数据上，我们表明SMI提供了最相关的比较指标，并且我们的显着性测试概括了指标以实现相似的性能。我们举例说明了空间指标在众所周知的果蝇分割网络上的应用。
Motivation: Recent advancements in high-throughput imaging have created new large datasets with tens of thousands of gene expression images. Methods for capturing these spatial and/or temporal expression patterns include in situ hybridization or fluorescent reporter constructs or tags, and results are still frequently assessed by subjective qualitative comparisons. In order to deal with available large datasets, fully automated analysis methods must be developed to properly normalize and model spatial expression patterns.Results: We have developed image segmentation and registration methods to identify and extract spatial gene expression patterns from RNA in situ hybridization experiments of Drosophila embryos. These methods allow us to normalize and extract expression information for 78 621 images from 3724 genes across six time stages. The similarity between gene expression patterns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual information (SMI). We additionally propose a strategy to calculate the significance of the similarity between two expression images, by generating surrogate datasets with similar spatial expression patterns using a Monte Carlo swap sampler. On data from an early development time stage, we show that SMI provides the most biologically relevant metric of comparison, and that our significance testing generalizes metrics to achieve similar performance. We exemplify the application of spatial metrics on the well-known Drosophila segmentation network.