Advances in data processing and application of imaging transcriptomics
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摘要: 随着全脑转录组数据，即全脑基因表达图谱(如艾伦人类脑图谱)的出现，影像转录组学为理解大脑分子尺度上的空间分布与宏观神经影像表型之间的关系开辟了新的机会。越来越多的研究揭示了基因表达的特定时空模式与大脑结构和功能的不同属性之间的联系。本文介绍了影像转录组学领域广泛使用的基因表达数据集，以及转录组数据处理的基本步骤和常用的工具箱，并概述了将基因表达数据与影像数据相关联的基本工作流程和三大类分析方法。近年来，影像转录组学已被广泛应用于理解脑神经发育和各类神经精神疾病中; 但该领域仍处于新兴阶段，必须克服一些方法学挑战以确保研究结果的稳健性。随着该领域的发展和现有方法的改进，未来的研究可以与越来越全面和精确的将转录组图谱数据相结合，这将会为识别体内观察到的与疾病相关的大脑变化的分子相关性提供一个强大可靠的框架。Abstract: With the advent of brain-wide transcriptomics data, i.e. brain-wide gene expression atlases such as the Allen human brain atlas, imaging transcriptomics has opened new opportunities for understanding the relationship between spatial variations on molecular scale of the brain and macroscopic neuroimaging phenotypes. A growing body of literature is demonstrating relationships between gene expression and different properties of brain structure and function. This article introduces the gene expression dataset widely used in the field of imaging transcriptomics, as well as the basic steps and the commonly used toolbox for transcriptomics data processing, and outlines the basic workflow and three kinds of analysis methods for associating gene expression data with image data. In recent years, imaging transcriptomics has been widely used to understand brain neurodevelopment and various neuropsychiatric disorders. However, the field is still nascent, and several methodological challenges must be overcome to ensure the robustness of the findings. As the filed develops and existing methodologies are refined, future studies can be combined with increasingly more comprehensive and precise transcriptional atlas data, which will offer a powerful and reliable framework for identifying the molecular correlates of disease-related brain changes observed in vivo.
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Key words:
- imaging transcriptomics /
- Allen human brain atlas /
- gene expression /
- neuroimaging /
- brain disorders
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图 1 影像转录组学的一般分析流程
IDP: 影像衍生表型; MNI: 蒙特利尔神经科学研究所; PLS: 偏最小二乘法.
Figure 1. General analysis workflow of imaging transcriptomics.
图 3 考虑空间自相关的影像转录组学分析方法
Figure 3. Imaging transcriptomics analysis methods considering spatial autocorrelation.
表 1 转录组数据处理步骤和方法
Table 1. Transcriptomics data processing steps and methods
处理步骤 目标 可选择的方法 基因注释 使用可用的最新信息将探针重新分配给基因 可使用的软件和工具：Re-annotator[ 27 ]、BioMart (http://www.biomart.org/)、AnnotationDbi (https://bioconductor.org/packages/AnnotationDbi)数据过滤 采用适当的控制来区分表达信号和噪声，提高数据可靠性 在至少k%的样本中排除掉信号强度没有超过背景噪声的探针，阈值k%可自定义 探针选择 当有多个探针注释到同一个基因时，对探针进行选择 (1)计算一个基因的所有可用探针的平均值，(2)选择具有最大强度的探针，(3)选择跨大脑区域方差最大的探针，(4)选择6个供体大脑中差异稳定性最高的探针，(5) 选择与其他探针平均相关性最高的探针 样本分配 将组织样本映射到影像数据中的感兴趣区域 生成供体特异性的分割方案，分别分配左/右脑、皮层/皮下的样本，并设置2 mm的距离阈值，以确保不会分配距离分区超过给定阈值的样本 数据标准化 减少个体间差异 分别对每个供体的基因表达数据进行标准化，可采用z分数标准化、sigmoid标准化等方法 基因过滤 减少供体特异性差异，并根据研究问题选择感兴趣的基因 (1)选择差异稳定性值高的基因，(2)基于GWASs研究选择疾病特异性基因，(3)基于先验假设选择基因 GWASs: 全基因组关联研究. 
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