Search

Bayesian mixture models are increasingly used for model‐based clustering and the follow‐up analysis on the clusters identified. As such, they are of particular interest for analyzing cytometry data where unsupervised clustering and association studies are often part of the scientific questions. Cytometry data are large quantitative data measured in a multidimensional space that typically ranges from a few dimensions to several dozens, and which keeps increasing due to innovative high‐throughput biotechonologies. We present several recent parametric and nonparametric Bayesian mixture modeling approaches, and describe advantages and limitations of these models under different research context for cytometry data analysis. We also acknowledge current computational challenges associated with the use of Bayesian mixture models for analyzing cytometry data, and we draw attention to recent developments in advanced numerical algorithms for estimating large Bayesian mixture models, which we believe have the potential to make Bayesian mixture model more applicable to new types of single‐cell data with higher dimensions. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge DiscoveryStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory [ABSTRACT FROM AUTHOR]

Adjusted differences black squares and 95% confidence intervals (CIs) (horizontal lines) of observed and predicted retinal -3 PUFAs contents for age-related macular degeneration in donor eyes and humans patients. Interestingly, cholesteryl esters species with -3 PUFAs contributed positively to the estimation of retinal -3 PUFAs content whereas those with -6 PUFAs lowered the prediction index, which is consistent with the well-established competitive metabolism of -3 and -6 PUFAs. Current treatments for age-related macular degeneration (AMD)-the leading cause of blindness in industrialized countries-are restricted to a single form of the disease and cannot always avoid severe visual loss.1 Among emerging preventive strategies, -3 polyunsaturated fatty acids (PUFAs) are enjoying interest as they promote normal retinal structure and function, reduce incidence, and slow progression of AMD, consistently with their abundance in retinal neurons. [Extracted from the article]

Flow cytometry is a powerful technology that allows the high‐throughput quantification of dozens of surface and intracellular proteins at the single‐cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time‐consuming nature. We present a new unsupervised algorithm called "cytometree" to perform automated population identification (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distributions. Node splitting is done according to a model selection procedure based on a normalized difference of Akaike information criteria between two competing models. Post‐processing of the tree structure and derived populations allows us to complete the annotation of the populations. The algorithm is shown to perform better than the state‐of‐the‐art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods project. The algorithm is also applied to a T‐cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open‐source available algorithm while requiring the shortest computation time. © 2018 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Objective: Antibodies against citrullinated fibrinogen (anti-Cit-fibrinogen) have been implicated in rheumatoid arthritis (RA) and associated with cardiovascular risk in RA. The objective of this study was to examine the association between anti-Cit-fibrinogens and coronary artery disease (CAD) outcomes.Methods: We performed the study in an RA cohort based in a large academic institution linked with electronic medical record data containing information on CAD outcomes from medical record review. Using a published bead-based assay method, we measured 10 types of anti-Cit-fibrinogens. We applied a score test to determine the association between the anti-Cit-fibrinogens as a group with CAD outcomes. Principal components analysis (PCA) was performed to assess whether the anti-Cit-fibrinogens clustered into groups. Each group was then additionally tested for association with CAD. Sensitivity analyses were also performed using a published International Classification of Disease, Ninth Revision code group for ischemic heart disease (IHD) as the outcome.Results: We studied 1,006 RA subjects (mean ± SD age 61.0 ± 13.0 years; 72.2% anti-cyclic citrullinated peptide positive). As a group, anti-Cit-fibrinogen was associated with CAD (P = 1.1 × 10-4 ). From the PCA analysis, we observed 3 main groups, of which only 1 group, containing 7 of the 10 anti-Cit-fibrinogens, was significantly associated with CAD outcomes (P = 0.015). In the sensitivity analysis, all anti-Cit-fibrinogens as a group remained significantly associated with IHD (P = 2.9 × 10-4 ).Conclusion: Anti-Cit-fibrinogen antibodies as a group were associated with CAD outcomes in our RA cohort, with the strongest signal for association arising from a subset of the autoantibodies. [ABSTRACT FROM AUTHOR]

Objective Patients with rheumatoid arthritis (RA) develop autoantibodies against a spectrum of antigens, but the clinical significance of these autoantibodies is unclear. Using a phenome-wide association study (PheWAS) approach, we examined the association between autoantibodies and clinical subphenotypes of RA. Methods This study was conducted in a cohort of RA patients identified from the electronic medical records (EMRs) of 2 tertiary care centers. Using a published multiplex bead assay, we measured 36 autoantibodies targeting epitopes implicated in RA. We extracted all International Classification of Diseases, Ninth Revision (ICD-9) codes for each subject and grouped them into disease categories (PheWAS codes), using a published method. We tested for the association of each autoantibody (grouped by the targeted protein) with PheWAS codes. To determine significant associations (at a false discovery rate [FDR] of ≤0.1), we reviewed the medical records of 50 patients with each PheWAS code to determine positive predictive values (PPVs). Results We studied 1,006 RA patients; the mean ± SD age of the patients was 61.0 ± 12.9 years, and 79.0% were female. A total of 3,568 unique ICD-9 codes were grouped into 625 PheWAS codes; the 206 PheWAS codes with a prevalence of ≥3% were studied. Using the PheWAS method, we identified 24 significant associations of autoantibodies to epitopes at an FDR of ≤0.1. The associations that were strongest and had the highest PPV for the PheWAS code were autoantibodies against fibronectin and obesity ( P = 6.1 × 10−4, PPV 100%), and that between fibrinogen and pneumonopathy ( P = 2.7 × 10−4, PPV 96%). Pneumonopathy codes included diagnoses for cryptogenic organizing pneumonia and obliterative bronchiolitis. Conclusion We demonstrated application of a bioinformatics method, the PheWAS, to screen for the clinical significance of RA-related autoantibodies. Using the PheWAS approach, we identified potentially significant links between variations in the levels of autoantibodies and comorbidities of interest in RA. [ABSTRACT FROM AUTHOR]