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Facilitates the exploration of single-cell analysis of complex systems. VorteX provides a rapid, reliable approach to manage cell subset analysis that maximizes automation. It can process large datasets using fast K-nearest neighbor (KNN) estimation of cell event density and automatically arranges populations by a marker-based classification system. The tool is designed to empower researchers' exploration of biological data by providing and easy-to-use environment for cluster analysis and rich visualization of clustering results.
RchyOptimyx / cellular hieraRCHY OPTIMization
Constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets.
FLAME / FLow analysis with Automated Multivariate Estimation
Uses finite mixture model clustering techniques with novel algorithms and models to define and characterize discrete populations in flow cytometric data. A distinguishing feature of FLAME is its use of skewt distributions, which was motivated by the observation that biologically meaningful data clusters are often skew and heavy-tailed. FLAME includes a metaclustering step during which cell populations are matched across samples.
A package based on a computational approach that automatically reveals all possible cell subsets. By using flowType, from tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. In particular, this computational approach holds significant potential for: (i) detailed exploratory analysis of the immune system (using a high number of markers to parse the cell populations); (ii) analysis of large cohorts of subjects (e.g. clinical studies and vaccine/drug trials); and (iii) screening studies to identify appropriate marker panels for further clinical investigation.
Performs quantitative analysis of cell proliferation in tracking dye-based experiments. flowFit, distributed as an R Bioconductor library, is based on a mathematical model that takes into account the height of each peak, the size and position of the parental population (labeled but not proliferating) and the estimated distance between the brightness of a cell and the brightness of its daughter (in which the dye is assumed to undergo a 2-fold dilution). Although the algorithm does not make any inference on cell types, rates of cell divisions or rates of cell death, it deconvolutes the actual collected data into a set of peaks, whereby each peak corresponds to a subpopulation of cells that have divided N times.
Performs semantic labelling of cell populations based on their surface markers and applied it to labelling of the Federation of Clinical Immunology Societies Human Immunology Project Consortium lyoplate populations as a use case. flowCL enables researchers to unambiguously label their cell populations based on their immunophenotype using the CL and allows for unambiguous and reproducible identification of standardized cell types. It decomposes a query such as ‘CD4+CD8−’ (as provided in the conventional format used by immunologists) into its individual markers (CD4, CD8) and translates their relative abundance into a relation used in the CL, such as + for has plasma membrane part. As the CL development proceeds, flowCL automatically remains up-to-date with the latest scientific knowledge.
Designed for the visualization, analysis and integration of cell clustering results. Clusters having similar cell abundance profiles can be classified, using various methods such as k-means. SPADEVizR can also generate linear, Cox and random forest models to predict biological outcomes, based on the cluster abundances. Representations available in SPADEVizR allow efficient visualizations and comparisons of cluster abundances between different samples and conditions. All pairwise marker co-expressions for selected samples or selected clusters can be visualized using distograms. SPADEVizR constitutes a powerful approach for interpreting clustering results from the SPADE algorithm or other automatic gating algorithms.
Identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. It offers unique advantages in cases where fluorescence activated cell sorting (FACS) gate selection is non-intuitive. CellSort also provides additional functionality to test different gate parameter scenarios and estimate the effects of multiple rounds of sorting.
Identifies flow cytometry (FCM) cell populations. flowLearn works in a semi-supervised mode and requires the gating of one or few characteristic samples by a human expert in the form of thresholds. The software does not have to deal with the problem of noise inherent to such spaces and opens the way towards a quality control of samples which have already been gated. It can be used to spot problems in existing gating hierarchies, offering the possibility to be used to identify problematic gates.
A model-based analysis method for carboxyfluorescein succinimidyl ester (CFSE) time-series data. ShAPE-DALSP uses a flexible description of proliferating cell populations, namely, a division-, age- and label-structured population model. Efficient maximum likelihood and Bayesian estimation algorithms are introduced to infer the model parameters and their uncertainties. These methods exploit the forward sensitivity equations of the underlying partial differential equation model for efficient and accurate gradient calculation, thereby improving computational efficiency and reliability compared with alternative approaches and accelerating uncertainty analysis.
SWIFT / Scalable Weighted Iterative Flow-clustering Technique
Provide tools for automating analysis for massive high-dimensional datasets. SWIFT is based on a multi-stage framework for clustering with features that are motivated in particular by the characteristics of FC data. In particular, SWIFT aims to identify rare populations that are commonly of interest in immunological studies and attempts to honor the modality of the data by ensuring that multi-modal clusters are not grouped together within individual clusters.
Joint clustering and matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in EMMIX-JCM.
A package dedicated to FCM gating analysis, addressing the increasing demand for software capable of processing and analyzing the voluminous amount of FCM data efficiently via an objective, reproducible and automated means. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. It provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. flowClust offers various tools to summarize and visualize a wealth of features of the clustering results. To ensure its convenience of use, it has been adapted for the FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis. flowClust contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.
A package based on a hierarchical algorithm to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. The flowMatch algorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation. Using flowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.
A pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. Modeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable.
FLOCK / FLOw Clustering without K
Uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. FLOCK was used to objectively identify seventeen distinct B-cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal-ImmPort for open use by the immunology research community.
A package based on a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. It uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favourably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans addresses all the issues that prevented the application of K-means to FCM data in the past. This package is a powerful tool for identification of cell populations as part of high throughput and accurate FCM data analysis.
A framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. flowMerge is based on a cluster merging algorithm which improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. It allows the automated selection of the number of distinct cell subpopulations and enables to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. flowMerge provides a good compromise between the flowClustBIC and flowClustICL solutions by combining the good model fitting characteristics of BIC-based model selection with a more modest estimate of the true number of clusters, a characteristic of the ICL-based model selection.
A package for the analysis of flow cytometry data. flowFP provides tools to transform raw flow cytometry data into a form suitable for direct input into conventional statistical analysis and empirical modeling software tools. The approach of flowFP is to generate a description of the multivariate probability distribution function of flow cytometry data in the form of a “fingerprint.” As such, it is independent of a presumptive functional form for the distribution, in contrast with model-based methods such as Gaussian Mixture Modeling. The broad aim of the package is to directly transform raw FC list-mode data into a representation suitable for direct input to other statistical analysis and empirical modeling tools. Thus, it is useful to think of flowFP as an intermediate step between the acquisition of high-throughput FC data on the one hand, and empirical modeling, machine learning, and knowledge discovery on the other.
An automated analysis pipeline for uncompensated fluorescence and mass cytometry data, which consists of two parts. First, cell events of each sample are grouped into individual clusters. Subsequently, a classification algorithm assorts these cell event clusters into populations comparable between different samples. The clustering of cell events is designed for datasets with large event counts in high dimensions as a global unsupervised method, sensitive to identify rare cell types even when next to large populations.
Mixture models are popular as an automated means of gating flow cytometry data, as mixtures of Gaussian densities, or more robust Student-t densities, can cluster flow data in a statistically meaningful way. A major challenge to the use of mixture models is the requirement of a-priori specification of the number of clusters. If the number of clusters is unknown, we can treat determination of the correct number of clusters as a model selection problem. But this approach can be computationally expensive, requiring multiple runs of the software with varying numbers of clusters specified. FlowVB is a computationally cheaper alternative that allows us to fit a Student-t mixture model (SMM) in a single run using a Variational Bayes (VB) inference algorithm.
An integration of Bayesian non-parametric mixture models, massively parallel computing on GPUs and software development in Python to provide an extensible toolkit for automated statistical analysis in high-dimensional flow cytometry (FCM). The use of standard Bayesian non-parametric Dirichlet process mixture models allows the flexible density estimation of the posterior distribution (MCMC) or modes (EM) of high-dimensional FCM data, and provides a coherent statistical framework for data analysis and interpretation.
A statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination.
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