scholarly journals Graph of graphs analysis for multiplexed data with application to imaging mass cytometry

2021 ◽  
Vol 17 (3) ◽  
pp. e1008741
Author(s):  
Ya-Wei Eileen Lin ◽  
Tal Shnitzer ◽  
Ronen Talmon ◽  
Franz Villarroel-Espindola ◽  
Shruti Desai ◽  
...  
Keyword(s):  
Feature Space ◽  
Tissue Microarrays ◽  
Mass Cytometry ◽  
Protein Targets ◽  
Spatially Resolved ◽  
Nonlinear Dimension Reduction ◽  
Average Accuracy ◽  
Map Estimator ◽  
Nonlinear Dimension ◽  
Multiple Samples

Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.

scholarly journals Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

2021 ◽  
Author(s):  
Jovan Tanevski ◽  
Attila Gabor ◽  
Ricardo Ramirez Flores ◽  
Denis Schapiro ◽  
Julio Saez-Rodriguez
Keyword(s):  
Breast Cancer ◽  
Spatial Data ◽  
Spatial Information ◽  
Omics Data ◽  
Intercellular Signaling ◽  
Mass Cytometry ◽  
Learning Framework ◽  
Spatially Resolved ◽  
Scalable Methods ◽  
Improve Patient

Abstract The advancement of technologies to measure highly multiplexed spatial data requires the development of scalable methods that can leverage the spatial information. We present MISTy, a flexible, scalable and explainable machine learning framework for extracting interactions from any spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects, such as those from direct neighbours versus those from distant cells. MISTy can be applied to different spatially resolved omics data with dozens to thousands of markers, without the need to perform cell-type annotation. We evaluate the performance of MISTy on an in silico dataset and demonstrate its applicability on three breast cancer datasets, two measured by imaging mass cytometry and one by Visium spatial transcriptomics. We show how we can estimate interactions coming from different spatial contexts that we can relate to tumor progression and clinical features. Our analysis also reveals that the estimated interactions in triple negative breast cancer are associated with clinical outcomes which could improve patient stratification. Finally, we demonstrate the flexibility of MISTy to integrate different kinds of views by modeling activities of pathways estimated from gene expression in a spatial context to analyse intercellular signaling.

scholarly journals Image Segmentation and Identification of Paired Antibodies in Breast Tissue

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jimmy C. Azar ◽  
Martin Simonsson ◽  
Ewert Bengtsson ◽  
Anders Hast
Keyword(s):  
Breast Tissue ◽  
Principal Component ◽  
Feature Space ◽  
Tissue Microarrays ◽  
Specific Protein ◽  
Blue Dye ◽  
Computationally Efficient ◽  
Automated Quantification ◽  
Human Protein Atlas ◽  
Low Dimensional

Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies’ ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3′-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.

scholarly journals MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yuntao Zhao ◽  
Chunyu Xu ◽  
Bo Bo ◽  
Yongxin Feng
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Feature Space ◽  
Image Texture ◽  
Accuracy Rate ◽  
Texture Representation ◽  
Malware Classification ◽  
Classification Framework ◽  
Average Accuracy ◽  
New Feature

The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.

Tissue microarrays of bone marrow aspirate clot allow assessment of multiple samples

2020 ◽  
Vol 216 (1) ◽  
pp. 152721
Author(s):  
Pollyanna Domeny-Duarte ◽  
Ligia Niero ◽  
Maria Ap. Custodio Domingues
Keyword(s):  
Bone Marrow ◽  
Bone Marrow Aspirate ◽  
Tissue Microarrays ◽  
Multiple Samples

A Comparative Study for Postural Synergy Synthesis Using Linear and Nonlinear Methods

2016 ◽  
Vol 13 (03) ◽  
pp. 1650009 ◽  
Author(s):  
Kai Xu ◽  
Huan Liu ◽  
Yuheng Du ◽  
Xiangyang Zhu
Keyword(s):  
Comparative Study ◽  
Latent Variable ◽  
Robotic Hand ◽  
Nonlinear Method ◽  
Variable Model ◽  
Nonlinear Methods ◽  
Nonlinear Dimension Reduction ◽  
Linear And Nonlinear ◽  
Nonlinear Dimension ◽  
Postural Synergies

Human controls dozens of muscles for different hand postures in a coordinated manner. Such coordination is referred to as a postural synergy. Postural synergy has enabled an anthropomorphic robotic hand with many actuators to be applied as a prosthetic hand and controlled by two to three channels of biological signals. Principle component analysis (PCA) of the hand postures has become a popular way to extract the postural synergies. However, relatively big errors are often produced while the hand postures are reconstructed using these PCA-synthesized synergies due to the linearity nature of this method. This paper presents a comparative study in which the postural synergies are synthesized using both linear and nonlinear methods. Specifically, the Gaussian process latent variable model (GPLVM), as a nonlinear dimension reduction method, is implemented to produce nonlinear postural synergies and the hand postures can then be reconstructed from the two-dimensional synergy plane. Computational and experimental verifications show that the posture reconstruction errors are greatly reduced using this nonlinear method. The results suggest that the use of nonlinear postural synergies should be considered while applying a dexterous robotic hand as prosthesis. Versatile hand postures could be formed via only two channels of bio-signals.

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