Joint analysis of expression levels and histological images identifies genes associated with tissue morphology.
Joint analysis of expression levels and histological images identifies genes associated with tissue morphology.
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DOI：
10.1038/s41467-021-21727-x
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发表时间：
2021-03-11
影响因子：
16.6
通讯作者：
Engelhardt BE
中科院分区：
文献类型：
--
作者：
Ash JT;Darnell G;Munro D;Engelhardt BE
组织学图像用于表征复杂的表型，如肿瘤分期。我们的目标是将染色组织图像的特征与高维基因组标记相关联。我们使用卷积自动编码器和稀疏典型相关分析（CCA）对成对的组织学图像和批量基因表达，以确定其在组织样本中的表达水平与相应样本图像的形态特征子集相关的基因子集。我们应用我们的方法，ImageCCA，两个TCGA数据集，并找到与细胞外基质和细胞壁基础设施的结构相关的基因集，涉及在细胞外过程中的未表征的基因。我们发现了与特定细胞类型相关的基因组，包括神经元细胞和免疫系统细胞。我们将ImageCCA应用于GTEx v6数据，并找到捕获甲状腺和结肠组织中与遗传变异相关的群体变异的图像特征（图像形态QTL或imQTL），这表明遗传变异调节组织形态性状的群体变异。来自组织学切片的图像特征可以用作组织局部病理学的关联研究中的信息性内表型。在这里，作者开发了ImageCCA，这是一个用于联合分析配对基因表达和组织学数据的框架，这些数据来自自动提取的图像特征。
Histopathological images are used to characterize complex phenotypes such as tumor stage. Our goal is to associate features of stained tissue images with high-dimensional genomic markers. We use convolutional autoencoders and sparse canonical correlation analysis (CCA) on paired histological images and bulk gene expression to identify subsets of genes whose expression levels in a tissue sample correlate with subsets of morphological features from the corresponding sample image. We apply our approach, ImageCCA, to two TCGA data sets, and find gene sets associated with the structure of the extracellular matrix and cell wall infrastructure, implicating uncharacterized genes in extracellular processes. We find sets of genes associated with specific cell types, including neuronal cells and cells of the immune system. We apply ImageCCA to the GTEx v6 data, and find image features that capture population variation in thyroid and in colon tissues associated with genetic variants (image morphology QTLs, or imQTLs), suggesting that genetic variation regulates population variation in tissue morphological traits. Image features from histological slides can be used as informative endophenotypes in association studies for tissue-localized pathologies. Here, the authors develop ImageCCA, a framework for joint analysis of paired gene expression and histology data derived from automatically extracted image features.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
1.8
作者：
Kessy, Agnan;Lewin, Alex;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
影响因子：
4.6
作者：
Hagele, Miriam;Seegerer, Philipp;Binder, Alexander
通讯作者：
Binder, Alexander
通过替代变量分析捕获基因表达研究中的异质性。
影响因子：
4.5
作者：
Leek, Jeffrey T.;Storey, John D.
通讯作者：
Storey, John D.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y