RESUMEN
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.
Asunto(s)
Biología Computacional/métodos , Regulación Neoplásica de la Expresión Génica/genética , Expresión Génica/genética , Neoplasias/patología , Sitios de Carácter Cuantitativo/genética , Proteína BRCA1/genética , Biomarcadores de Tumor/genética , Membrana Celular/genética , Membrana Celular/fisiología , Matriz Extracelular/genética , Matriz Extracelular/fisiología , Humanos , Procesamiento de Imagen Asistido por Computador , Neoplasias/genética , Polimorfismo de Nucleótido Simple/genéticaRESUMEN
Cellular mechanical metamaterials are a special class of materials whose mechanical properties are primarily determined by their geometry. However, capturing the nonlinear mechanical behavior of these materials, especially those with complex geometries and under large deformation, can be challenging due to inherent computational complexity. In this work, we propose a data-driven multiscale computational scheme as a possible route to resolve this challenge. We use a neural network to approximate the effective strain energy density as a function of cellular geometry and overall deformation. The network is constructed by "learning" from the data generated by finite element calculation of a set of representative volume elements at cellular scales. This effective strain energy density is then used to predict the mechanical responses of cellular materials at larger scales. Compared with direct finite element simulation, the proposed scheme can reduce the computational time up to two orders of magnitude. Potentially, this scheme can facilitate new optimization algorithms for designing cellular materials of highly specific mechanical properties.
Asunto(s)
Algoritmos , Simulación por Computador , Análisis de Elementos Finitos , Estrés MecánicoRESUMEN
Cryo-electron microscopy is an increasingly popular tool for studying the structure and dynamics of biological macromolecules at high resolution. A crucial step in automating single-particle reconstruction of a biological sample is the selection of particle images from a micrograph. We present a novel algorithm for selecting particle images in low-contrast conditions; it proves more effective than the human eye on close-to-focus micrographs, yielding improved or comparable resolution in reconstructions of two macromolecular complexes.
