2,655 research outputs found
Automated and reproducible cell identification in mass cytometry using neural networks
The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration
Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis
Flow cytometry is often used to characterize the malignant cells in leukemia
and lymphoma patients, traced to the level of the individual cell. Typically,
flow cytometric data analysis is performed through a series of 2-dimensional
projections onto the axes of the data set. Through the years, clinicians have
determined combinations of different fluorescent markers which generate
relatively known expression patterns for specific subtypes of leukemia and
lymphoma -- cancers of the hematopoietic system. By only viewing a series of
2-dimensional projections, the high-dimensional nature of the data is rarely
exploited. In this paper we present a means of determining a low-dimensional
projection which maintains the high-dimensional relationships (i.e.
information) between differing oncological data sets. By using machine learning
techniques, we allow clinicians to visualize data in a low dimension defined by
a linear combination of all of the available markers, rather than just 2 at a
time. This provides an aid in diagnosing similar forms of cancer, as well as a
means for variable selection in exploratory flow cytometric research. We refer
to our method as Information Preserving Component Analysis (IPCA).Comment: 26 page
Data reduction for spectral clustering to analyze high throughput flow cytometry data
Background: Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.Results: We tested our algorithm on flow cytometry data as an example of large, multidimensional data containing potentially hundreds of thousands of data points (i.e., events in flow cytometry, typically corresponding to cells). Compared to two state of the art model-based flow cytometry clustering methods, SamSPECTRAL demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations.Conclusions: This work is the first successful attempt to apply spectral methodology on flow cytometry data. An implementation of our algorithm as an R package is freely available through BioConductor. © 2010 Zare et al; licensee BioMed Central Ltd
Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling
Background: The role of flow cytometry (FCM) in diagnosing myelodysplastic syndromes (MDS) remains controversial, because analysis of myeloid maturation may involve subjective interpretation of sometimes subtle patterns on multiparameter FCM. Methods: Using sixâparameter marker combinations known to be useful in evaluating the myeloid compartment in MDS, we measured objective immunophenotypic differences between nonâneoplastic ( n = 25) and dysplastic ( n = 17) granulopoiesis using a novel method, called Fisher information nonparametric embedding (FINE), that measures information distances among FCM datasets modeled as individual highâdimensional probability density functions, rather than as sets of twoâdimensional histograms. Informationâpreserving component analysis (IPCA) was used to create informationâoptimized ârotatedâ twoâdimensional histograms for visualizing myelopoietic immunophenotypes for each individual sample. Results: There was a consistent trend of segregation of higherâgrade MDS (RAEB and RCMD) from benign by FINE analysis. This difference was accentuated in cases with morphologic dysgranulopoiesis and in cases with clonal cytogenetic abnormalities. However, lower grades of MDS or cases that lacked morphologic dysgranulopoiesis showed much greater overlap with nonâneoplastic cases. Two cases of reactive left shift were consistently embedded within the higherâgrade MDS group. IPCA yielded twoâdimensional histogram projections for each individual case by relative weighting of measured cellular characteristics, optimized for preserving information distances derived through FINE. Conclusions: Objective analysis by information geometry supports the conclusions of previous studies that there are immunophenotypic differences in the maturation patterns of benign granulopoiesis and high grade MDS, but also reinforces the known pitfalls of overlap between lowâgrade MDS and benign granulopoiesis and overlap between reactive granulocytic left shifts and dysplastic granulopoiesis. © 2011 International Clinical Cytometry SocietyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87051/1/20592_ftp.pd
Deep Learning for Embedding and Integrating Multimodal Biomedical Data
Biomedical data is being generated in extremely high throughput and high dimension by technologies in areas ranging from single-cell genomics, proteomics, and transcriptomics (cytometry, single-cell RNA and ATAC sequencing) to neuroscience and cognition (fMRI and PET) to pharmaceuticals (drug perturbations and interactions). These new and emerging technologies and the datasets they create give an unprecedented view into the workings of their respective biological entities. However, there is a large gap between the information contained in these datasets and the insights that current machine learning methods can extract from them. This is especially the case when multiple technologies can measure the same underlying biological entity or system. By separately analyzing the same system but from different views gathered by different data modalities, patterns are left unobserved if they only emerge from the multi-dimensional joint representation of all of the modalities together. Through an interdisciplinary approach that emphasizes active collaboration with data domain experts, my research has developed models for data integration, extracting important insights through the joint analysis of varied data sources. In this thesis, I discuss models that address this task of multi-modal data integration, especially generative adversarial networks (GANs) and autoencoders (AEs). My research has been focused on using both of these models in a generative way for concrete problems in cutting-edge scientific applications rather than the exclusive focus on the generation of high-resolution natural images. The research in this thesis is united around ideas of building models that can extract new knowledge from scientific data inaccessible to currently existing methods
Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets
The goal of this dissertation is to develop unsupervised algorithms for discovering previously unknown subspace trends in massive multivariate biomedical data sets without the benefit of prior information. A subspace trend is a sustained pattern of gradual/progressive changes within an unknown subset of feature dimensions. A fundamental challenge to subspace trend discovery is the presence of irrelevant data dimensions, noise, outliers, and confusion from multiple subspace trends driven by independent factors that are mixed in with each other. These factors can obscure the trends in traditional dimension reduction and projection based data visualizations. To overcome these limitations, we propose a novel graph-theoretic neighborhood similarity measure for sensing concordant progressive changes across data dimensions. Using this measure, we present an unsupervised algorithm for trend-relevant feature selection and visualization. Additionally, we propose to use an efficient online density-based representation to make the algorithm scalable for massive datasets.
The representation not only assists in trend discovery, but also in cluster detection including rare populations. Our method has been successfully applied to diverse synthetic and real-world biomedical datasets, such as gene expression microarray and arbor morphology of neurons and microglia in brain tissue. Derived representations revealed biologically meaningful hidden subspace trend(s) that were obscured by irrelevant features and noise. Although our applications are mostly from the biomedical domain, the proposed algorithm is broadly applicable to exploratory analysis of high-dimensional data including visualization, hypothesis generation, knowledge discovery, and prediction in diverse other applications.Electrical and Computer Engineering, Department o
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.
Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19
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