Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel.
Bioinformatics. 2013 Jul 1;29(13):i36-43. doi: 10.1093/bioinformatics/btt207.
High-spatial resolution imaging datasets of mammalian brains have recently become available in unprecedented amounts. Images now reveal highly complex patterns of gene expression varying on multiple scales. The challenge in analyzing these images is both in extracting the patterns that are most relevant functionally and in providing a meaningful representation that allows neuroscientists to interpret the extracted patterns.
Here, we present FuncISH--a method to learn functional representations of neural in situ hybridization (ISH) images. We represent images using a histogram of local descriptors in several scales, and we use this representation to learn detectors of functional (GO) categories for every image. As a result, each image is represented as a point in a low-dimensional space whose axes correspond to meaningful functional annotations. The resulting representations define similarities between ISH images that can be easily explained by functional categories. We applied our method to the genomic set of mouse neural ISH images available at the Allen Brain Atlas, finding that most neural biological processes can be inferred from spatial expression patterns with high accuracy. Using functional representations, we predict several gene interaction properties, such as protein-protein interactions and cell-type specificity, more accurately than competing methods based on global correlations. We used FuncISH to identify similar expression patterns of GABAergic neuronal markers that were not previously identified and to infer new gene function based on image-image similarities.
Supplementary data are available at Bioinformatics online.
最近，哺乳动物大脑的高空间分辨率成像数据集以前所未有的数量出现。图像现在揭示了高度复杂的基因表达模式，这些模式在多个尺度上变化。分析这些图像的挑战在于提取最相关功能的模式，并提供有意义的表示，使神经科学家能够解释提取的模式。
在这里，我们提出了 FuncISH-一种学习神经原位杂交（ISH）图像功能表示的方法。我们使用几个尺度的局部描述符的直方图来表示图像，并使用此表示为每个图像学习功能（GO）类别的检测器。结果，每个图像都表示为低维空间中的一个点，其轴对应于有意义的功能注释。由此产生的表示定义了 ISH 图像之间的相似性，可以通过功能类别轻松解释。我们将我们的方法应用于 Allen 大脑图谱中可用的鼠标神经 ISH 图像的基因组集，发现大多数神经生物学过程可以从空间表达模式以高精度推断出来。使用功能表示，我们比基于全局相关性的竞争方法更准确地预测了几种基因相互作用特性，例如蛋白质-蛋白质相互作用和细胞类型特异性。我们使用 FuncISH 来识别以前未识别的 GABA 能神经元标记的相似表达模式，并根据图像-图像相似性推断新的基因功能。
补充数据可在“生物信息学”在线获取。
