Approximated and User Steerable tSNE for Progressive Visual Analytics

Metabolic Survival Adaptations of Plasmodium falciparum Exposed to Sublethal Doses of Fosmidomycin

The malaria parasite Plasmodium falciparum contains the apicoplast organelle that synthesizes isoprenoids, which are metabolites necessary for posttranslational modification of Plasmodium proteins. We used fosmidomycin, an antibiotic that inhibits isoprenoid biosynthesis, to identify mechanisms that underlie the development of the parasite's adaptation to the drug at sublethal concentrations. We first determined a concentration of fosmidomycin that reduced parasite growth by ∼50% over one intraerythrocytic developmental cycle (IDC). At this dose, we maintained synchronous parasite cultures for one full IDC and collected metabolomic and transcriptomic data at multiple time points to capture global and stage-specific alterations. We integrated the data with a genome-scale metabolic model of P. falciparum to characterize the metabolic adaptations of the parasite in response to fosmidomycin treatment. Our simulations showed that, in treated parasites, the synthesis of purine-based nucleotides increased, whereas the synthesis of phosphatidylcholine during the trophozoite and schizont stages decreased. Specifically, the increased polyamine synthesis led to increased nucleotide synthesis, while the reduced methyl-group cycling led to reduced phospholipid synthesis and methyltransferase activities. These results indicate that fosmidomycin-treated parasites compensate for the loss of prenylation modifications by directly altering processes that affect nucleotide synthesis and ribosomal biogenesis to control the rate of RNA translation during the IDC. This also suggests that combination therapies with antibiotics that target the compensatory response of the parasite, such as nucleotide synthesis or ribosomal biogenesis, may be more effective than treating the parasite with fosmidomycin alone.

BIRD: identifying cell doublets via biallelic expression from single cells

Summary

Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power toward doublets’ identification. We present a pipeline called BIallelic Ratio for Doublets (BIRD) that relies on heterologous genetic variations, from single-cell RNA sequencing. For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (±3.7%) accuracy, and an area under the curve of 0.88 (±0.04) for a collection of ∼13 300 single cells. BIRD addresses instances of doublets, which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high-coverage data from Smart-seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage and depth.

Supplementary information

Supplementary data are available at Bioinformatics online.

DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.

Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery

Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms

A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information.

Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

Introduction to Multiparametric Flow Cytometry and Analysis of High-Dimensional Data

Multiparametric flow cytometry is a technique utilized in translational experiments that utilizes fluorescently tagged antibodies and functional fluorescent dyes to measure proteins on the surface or in the cytoplasm of cells and to measure processes occurring within cells themselves. These fluorescent molecules, or fluorophores, can be tagged to antibodies to measure specific biological molecules such as proteins inside or on the surface of cells. Small organic compounds such as the nucleic acid binding dye propidium iodide (PI) can permeate compromised cell membranes when cells are no longer viable or used to measure DNA content of cycling cells. Successful completion of flow cytometry experiments requires expertise in both the preparation of the samples, acquisition of the samples on instruments, and analyses of the results. This chapter describes the principles needed to conduct a successful multiparameter flow cytometry experiment needed for drug development with references to well established internet resources that are useful to those less experienced in the field. In addition, we provide a brief introduction to data analysis including complex analysis of 10+ parameters simultaneously. These high-dimensional datasets require novel methods for analysis due to the volume of data collected, which are also introduced in this chapter.

ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.

SCHNEL: scalable clustering of high dimensional single-cell data

MOTIVATION

Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.

RESULTS

We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.

AVAILABILITY AND IMPLEMENTATION

Implementation is available on GitHub (https://github.com/biovault/SCHNELpy). All datasets used in this study are publicly available.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.

Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network

Purpose

The goal of this study is to explore whether deep learning based embedded models can provide a better visualization solution for large citation networks.

Design/methodology/approach

Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters.

Findings

The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps’ layout has very high stability.

Research limitations

The computational and time costs of training are very high for network embedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested.

Practical implications

This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliometric analysis tasks.

Originality/value

This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer, more stable science map. We also designed a practical evaluation method to investigate and compare maps.

Conditional t-SNE: more informative t-SNE embeddings

Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyond the specifics of t-SNE, there are two substantial limitations of any such approach: (1) not all information can be captured in a single two-dimensional embedding, and (2) to well-informed users, the salient structure of such an embedding is often already known, preventing that any real new insights can be obtained. Currently, it is not known how to extract the remaining information in a similarly effective manner. We introduce conditional t-SNE (ct-SNE), a generalization of t-SNE that discounts prior information in the form of labels. This enables obtaining more informative and more relevant embeddings. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining an elegant method with a single integrated objective. We show how to efficiently optimize the objective and study the effects of the extra parameter that ct-SNE has over t-SNE. Qualitative and quantitative empirical results on synthetic and real data show ct-SNE is scalable, effective, and achieves its goal: it allows complementary structure to be captured in the embedding and provided new insights into real data.

PARMAP: A Pan-Genome-Based Computational Framework for Predicting Antimicrobial Resistance

Antimicrobial resistance (AMR) has emerged as one of the most urgent global threats to public health. Accurate detection of AMR phenotypes is critical for reducing the spread of AMR strains. Here, we developed PARMAP (Prediction of Antimicrobial Resistance by MAPping genetic alterations in pan-genome) to predict AMR phenotypes and to identify AMR-associated genetic alterations based on the pan-genome of bacteria by utilizing machine learning algorithms. When we applied PARMAP to 1,597 Neisseria gonorrhoeae strains, it successfully predicted their AMR phenotypes based on a pan-genome analysis. Furthermore, it identified 328 genetic alterations in 23 known AMR genes and discovered many new AMR-associated genetic alterations in ciprofloxacin-resistant N. gonorrhoeae, and it clearly indicated the genetic heterogeneity of AMR genes in different subtypes of resistant N. gonorrhoeae. Additionally, PARMAP performed well in predicting the AMR phenotypes of Mycobacterium tuberculosis and Escherichia coli, indicating the robustness of the PARMAP framework. In conclusion, PARMAP not only precisely predicts the AMR of a population of strains of a given species but also uses whole-genome sequencing data to prioritize candidate AMR-associated genetic alterations based on their likelihood of contributing to AMR. Thus, we believe that PARMAP will accelerate investigations into AMR mechanisms in other human pathogens.

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this article, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.

CyTOFmerge: integrating mass cytometry data across multiple panels

Motivation

High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel.

Results

To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.

Availability and implementation

Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge).

Supplementary information

Supplementary data are available at Bioinformatics online.

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors

We present PANENE, a progressive algorithm for approximate nearest neighbor indexing and querying. Although the use of k-nearest neighbor (KNN) libraries is common in many data analysis methods, most KNN algorithms can only be queried when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and cannot satisfy the latency requirements of progressive systems. This long latency has significantly limited the use of many machine learning methods, such as t-SNE, in interactive visual analytics. PANENE is a novel algorithm for Progressive Approximate k-NEarest NEighbors, enabling fast KNN queries while continuously indexing new batches of data. Following the progressive computation paradigm, PANENE operations can be bounded in time, allowing analysts to access running results within an interactive latency. PANENE can also incrementally build and maintain a cache data structure, a KNN lookup table, to enable constant-time lookups for KNN queries. Finally, we present three progressive applications of PANENE, such as regression, density estimation, and responsive t-SNE, opening up new opportunities to use complex algorithms in interactive systems.

Immune monitoring using mass cytometry and related high-dimensional imaging approaches

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points. In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications. This paper presents an incremental DR solution. We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data. First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results. Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization. We demonstrate the effectiveness of our design with two case studies using real-world datasets.

A comparison framework and guideline of clustering methods for mass cytometry data

Background

With the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.

Result

To address this issue, we compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.

Conclusion

All the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.

The art of using t-SNE for single-cell transcriptomics

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Single-cell technologies in reproductive immunology

The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.

PD-L1 blockade engages tumor-infiltrating lymphocytes to co-express targetable activating and inhibitory receptors

BACKGROUND

The clinical benefit of immunotherapeutic approaches against cancer has been well established although complete responses are only observed in a minority of patients. Combination immunotherapy offers an attractive avenue to develop more effective cancer therapies by improving the efficacy and duration of the tumor-specific T-cell response. Here, we aimed at deciphering the mechanisms governing the response to PD-1/PD-L1 checkpoint blockade to support the rational design of combination immunotherapy.

METHODS

Mice bearing subcutaneous MC-38 tumors were treated with blocking PD-L1 antibodies. To establish high-dimensional immune signatures of immunotherapy-specific responses, the tumor microenvironment was analyzed by CyTOF mass cytometry using 38 cellular markers. Findings were further examined and validated by flow cytometry and by functional in vivo experiments. Immune profiling was extended to the tumor microenvironment of colorectal cancer patients.

RESULTS

PD-L1 blockade induced selectively the expansion of tumor-infiltrating CD4+ and CD8+ T-cell subsets, co-expressing both activating (ICOS) and inhibitory (LAG-3, PD-1) molecules. By therapeutically co-targeting these molecules on the TAI cell subsets in vivo by agonistic and antagonist antibodies, we were able to enhance PD-L1 blockade therapy as evidenced by an increased number of TAI cells within the tumor micro-environment and improved tumor protection. Moreover, TAI cells were also found in the tumor-microenvironment of colorectal cancer patients.

CONCLUSIONS

This study shows the presence of T cell subsets in the tumor micro-environment expressing both activating and inhibitory receptors. These TAI cells can be targeted by combined immunotherapy leading to improved survival.

Towards an Uncertainty-Aware Visualization in the Digital Humanities

As visualization becomes widespread in a broad range of cross-disciplinary academic domains, such as the digital humanities (DH), critical voices have been raised on the perils of neglecting the uncertain character of data in the visualization design process. Visualizations that, purposely or not, obscure or remove uncertainty in its different forms from the scholars’ vision may negatively affect the manner in which humanities scholars regard computational methods as useful tools in their daily work. In this paper, we address the issue of uncertainty representation in the context of the humanities from a theoretical perspective, in an attempt to provide the foundations of a framework that allows for the construction of ecological interface designs which are able to expose the computational power of the algorithms at play while, at the same time, respecting the particularities and needs of humanistic research. To this end, we review past uncertainty taxonomies in other domains typically related to the humanities and visualization, such as cartography and GIScience. From this review, we select an uncertainty taxonomy related to the humanities that we link to recent research in visualization for the DH. Finally, we bring a novel analytics method developed by other authors (Progressive Visual Analytics) into question, which we argue can be a good candidate to resolve the aforementioned difficulties in DH practice.

Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment

Visual analysis of multidimensional data requires expressive and effective ways to reduce data dimensionality to encode them visually. Multidimensional projections (MDP) figure among the most important visualization techniques in this context, transforming multidimensional data into scatter plots whose visual patterns reflect some notion of similarity in the original data. However, MDP come with distortions that make these visual patterns not trustworthy, hindering users to infer actual data characteristics. Moreover, the patterns present in the scatter plots might not be enough to allow a clear understanding of multidimensional data, motivating the development of layout enrichment methodologies to operate together with MDP. This survey attempts to cover the main aspects of MDP as a visualization and visual analytic tool. It provides detailed analysis and taxonomies as to the organization of MDP techniques according to their main properties and traits, discussing the impact of such properties for visual perception and other human factors. The survey also approaches the different types of distortions that can result from MDP mappings and it overviews existing mechanisms to quantitatively evaluate such distortions. A qualitative analysis of the impact of distortions on the different analytic tasks performed by users when exploring multidimensional data through MDP is also presented. Guidelines for choosing the best MDP for an intended task are also provided as a result of this analysis. Finally, layout enrichment schemes to debunk MDP distortions and/or reveal relevant information not directly inferable from the scatter plot are reviewed and discussed in the light of new taxonomies. We conclude the survey providing future research axes to fill discovered gaps in this domain.

Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.

Selective Wander Join: Fast Progressive Visualizations for Data Joins

Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident results on a subset of the visualization? This can happen when users are in exploratory analysis mode but want to ask some directed questions of the data as well. In a progressive visualization system, the online aggregation algorithm determines the database sampling rate and resulting convergence rate, not the user. In this paper, we extend a recent method in online aggregation, called Wander Join, that is optimized for queries that join tables, one of the most computationally expensive operations. This extension leverages importance sampling to enable user-driven sampling when data joins are in the query. We applied user interaction techniques that allow the user to view and adjust the convergence rate, providing more transparency and control over the online aggregation process. By leveraging importance sampling, our extension of Wander Join also allows for stratified sampling of groups when there is data distribution skew. We also improve the convergence rate of filtering queries, but with additional overhead costs not needed in the original Wander Join algorithm.

Predicting Cell Populations in Single Cell Mass Cytometry Data

Mass cytometry by time‐of‐flight (CyTOF) is a valuable technology for high‐dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify “new” cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell‐populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state‐of‐the‐art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Memory CD4+ T cells are generated in the human fetal intestine

The fetus is thought to be protected from exposure to foreign antigens, yet CD45RO+ T cells reside in the fetal intestine. Here we combined functional assays with mass cytometry, single-cell RNA sequencing and high-throughput T cell antigen receptor (TCR) sequencing to characterize the CD4+ T cell compartment in the human fetal intestine. We identified 22 CD4+ T cell clusters, including naive-like, regulatory-like and memory-like subpopulations, which were confirmed and further characterized at the transcriptional level. Memory-like CD4+ T cells had high expression of Ki-67, indicative of cell division, and CD5, a surrogate marker of TCR avidity, and produced the cytokines IFN-γ and IL-2. Pathway analysis revealed a differentiation trajectory associated with cellular activation and proinflammatory effector functions, and TCR repertoire analysis indicated clonal expansions, distinct repertoire characteristics and interconnections between subpopulations of memory-like CD4+ T cells. Imaging mass cytometry indicated that memory-like CD4+ T cells colocalized with antigen-presenting cells. Collectively, these results provide evidence for the generation of memory-like CD4+ T cells in the human fetal intestine that is consistent with exposure to foreign antigens.

Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.

PD-L1 Mediates Dysfunction in Activated PD-1+ NK Cells in Head and Neck Cancer Patients

Inhibitory immune-checkpoint receptors (ICRs), including programmed death 1 (PD-1), have been characterized as exhaustion markers on T cells that infiltrate the tumor microenvironment (TME) of many cancer types, including head and neck cancer (HNC). However, expression and function of ICRs, including PD-1, on natural killer (NK) cells remains less defined. NK cells are innate immune effector cells that lyse epidermal growth factor receptor-overexpressing HNC cells via cetuximab-mediated antibody-dependent cytotoxicity. Cetuximab is clinically effective but only in 10% to 15% of patients. Therefore, it is necessary to investigate how immunomodulation with cetuximab or PD-1 blockade might enhance NK cell responses in the TME and improve monoclonal antibody therapeutic efficacy. We observed that expression of PD-1 on NK cells marks an activated phenotype, which was suppressed only after binding programmed death ligand-1 (PD-L1). HNC patients who exhibit higher circulating PD-1⁺ NK cells associate with better clinical outcome, and these cells are enriched in the TME. Cetuximab-mediated NK cell activation increased PD-1 expression on NK cells in vitro, which was confirmed in vivo in a prospective neoadjuvant cetuximab trial. In contrast, PD-L1 ligation of PD-1⁺ NK cells diminished their activation status, whereas PD-1 blockade increased cetuximab-mediated NK cell activation and cytotoxicity, but only against HNC targets with high PD-L1 expression. Therefore, blocking the PD-1-PD-L1 axis may be a useful strategy to reverse immune evasion of HNC tumors with high PD-L1 expression during cetuximab therapy by reversing NK cell dysfunction.

Heterogeneity of circulating CD8 T-cells specific to islet, neo-antigen and virus in patients with type 1 diabetes mellitus

Auto-reactive CD8 T-cells play an important role in the destruction of pancreatic β-cells resulting in type 1 diabetes (T1D). However, the phenotype of these auto-reactive cytolytic CD8 T-cells has not yet been extensively described. We used high-dimensional mass cytometry to phenotype autoantigen- (pre-proinsulin), neoantigen- (insulin-DRIP) and virus- (cytomegalovirus) reactive CD8 T-cells in peripheral blood mononuclear cells (PBMCs) of T1D patients. A panel of 33 monoclonal antibodies was designed to further characterise these cells at the single-cell level. HLA-A2 class I tetramers were used for the detection of antigen-specific CD8 T-cells. Using a novel Hierarchical Stochastic Neighbor Embedding (HSNE) tool (implemented in Cytosplore), we identified 42 clusters within the CD8 T-cell compartment of three T1D patients and revealed profound heterogeneity between individuals, as each patient displayed a distinct cluster distribution. Single-cell analysis of pre-proinsulin, insulin-DRIP and cytomegalovirus-specific CD8 T-cells showed that the detected specificities were heterogeneous between and within patients. These findings emphasize the challenge to define the obscure nature of auto-reactive CD8 T-cells.

High-density single cell mRNA sequencing to characterize circulating tumor cells in hepatocellular carcinoma

Patients with hepatocellular carcinoma (HCC) release tumor cells to the bloodstream, which can be detected using cell surface markers. Despite numerous reports suggest a direct correlation between the number of circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough molecular characterization of CTCs. Due to the limited access to tissue samples in patients at advanced stages of HCC, it is crucial to develop new technologies to identify HCC cancer drivers in routine clinical conditions. Here, we describe a method that sequentially combines image flow cytometry and high density single-cell mRNA sequencing to identify CTCs in HCC patients. Genome wide expression profiling of CTCs using this approach demonstrates CTC heterogeneity and helps detect known oncogenic drivers in HCC such as IGF2. This integrated approach provides a novel tool for biomarker development in HCC using liquid biopsy.

Mass cytometry reveals innate lymphoid cell differentiation pathways in the human fetal intestine

Innate lymphoid cells (ILCs) are abundant in mucosal tissues and involved in tissue homeostasis and barrier function. Although several ILC subsets have been identified, it is unknown if additional heterogeneity exists, and their differentiation pathways remain largely unclear. We applied mass cytometry to analyze ILCs in the human fetal intestine and distinguished 34 distinct clusters through a t-SNE-based analysis. A lineage (Lin)-CD7+CD127-CD45RO+CD56+ population clustered between the CD127+ ILC and natural killer (NK) cell subsets, and expressed diverse levels of Eomes, T-bet, GATA3, and RORγt. By visualizing the dynamics of the t-SNE computation, we identified smooth phenotypic transitions from cells within the Lin-CD7+CD127-CD45RO+CD56+ cluster to both the NK cells and CD127+ ILCs, revealing potential differentiation trajectories. In functional differentiation assays, the Lin-CD7+CD127-CD45RO+CD56+CD8a- cells could develop into CD45RA+ NK cells and CD127+RORγt+ ILC3-like cells. Thus, we identified a previously unknown intermediate innate subset that can differentiate into ILC3 and NK cells.

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment.

The Contribution of Cytomegalovirus Infection to Immune Senescence Is Set by the Infectious Dose

The relationship between human cytomegalovirus (HCMV) infections and accelerated immune senescence is controversial. Whereas some studies reported a CMV-associated impaired capacity to control heterologous infections at old age, other studies could not confirm this. We hypothesized that these discrepancies might relate to the variability in the infectious dose of CMV occurring in real life. Here, we investigated the influence of persistent CMV infection on immune perturbations and specifically addressed the role of the infectious dose on the contribution of CMV to accelerated immune senescence. We show in experimental mouse models that the degree of mouse CMV (MCMV)-specific memory CD8⁺ T cell accumulation and the phenotypic T cell profile are directly influenced by the infectious dose, and data on HCMV-specific T cells indicate a similar connection. Detailed cluster analysis of the memory CD8⁺ T cell development showed that high-dose infection causes a differentiation pathway that progresses faster throughout the life span of the host, suggesting a virus–host balance that is influenced by aging and infectious dose. Importantly, short-term MCMV infection in adult mice is not disadvantageous for heterologous superinfection with lymphocytic choriomeningitis virus (LCMV). However, following long-term CMV infection the strength of the CD8⁺ T cell immunity to LCMV superinfection was affected by the initial CMV infectious dose, wherein a high infectious dose was found to be a prerequisite for impaired heterologous immunity. Altogether our results underscore the importance of stratification based on the size and differentiation of the CMV-specific memory T cell pools for the impact on immune senescence, and indicate that reduction of the latent/lytic viral load can be beneficial to diminish CMV-associated immune senescence.

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome

Spatial and temporal brain transcriptomics has recently emerged as an invaluable data source for molecular neuroscience. The complexity of such data poses considerable challenges for analysis and visualization. We present BrainScope: a web portal for fast, interactive visual exploration of the Allen Atlases of the adult and developing human brain transcriptome. Through a novel methodology to explore high-dimensional data (dual t-SNE), BrainScope enables the linked, all-in-one visualization of genes and samples across the whole brain and genome, and across developmental stages. We show that densities in t-SNE scatter plots of the spatial samples coincide with anatomical regions, and that densities in t-SNE scatter plots of the genes represent gene co-expression modules that are significantly enriched for biological functions. We also show that the topography of the gene t-SNE maps reflect brain region-specific gene functions, enabling hypothesis and data driven research. We demonstrate the discovery potential of BrainScope through three examples: (i) analysis of cell type specific gene sets, (ii) analysis of a set of stable gene co-expression modules across the adult human donors and (iii) analysis of the evolution of co-expression of oligodendrocyte specific genes over developmental stages. BrainScope is publicly accessible at www.brainscope.nl.

Defining an inflamed tumor immunophenotype in recurrent, metastatic squamous cell carcinoma of the head and neck

OBJECTIVES

Immune checkpoint inhibitors have demonstrated clinical benefit in recurrent, metastatic (R/M) squamous cell carcinoma of the head and neck (SSCHN), but lacking are biomarkers that predict response. We sought to define an inflamed tumor immunophenotype in this R/M SCCHN population and correlate immune metrics with clinical parameters and survival.

METHODS

Tumor samples were prospectively acquired from 34 patients to perform multiparametric flow cytometry and multidimensional clustering analysis integrated with next-generation sequencing data, clinical parameters and outcomes.

RESULTS

We identified an inflamed subgroup of tumors with prominent CD8+ T cell infiltrates and high PD-1/TIM3 co-expression independent of clinical variables, with improved survival compared with a non-inflamed subgroup (median overall survival 84.0 vs. 13.0months, p=0.004). The non-inflamed subgroup demonstrated low CD8+ T cells, low PD-1/TIM3 co-expression, and higher T_regs. Overall non-synonymous mutational burden did not correlate with response to PD-1 blockade in a subset of patients.

CONCLUSION

R/M SCCHN patients with an inflamed tumor immunophenotype demonstrate improved survival. Further prospective studies are needed to validate these findings and explore the use of immunophenotype to guide patient selection for immunotherapeutic approaches.