Unlock your biological data


Try: RNA sequencing CRISPR Genomic databases DESeq

Cell population identification software tools | Flow cytometry data analysis

Traditionally, the majority of flow cytometry experiments have been analyzed visually, either by serial manual inspection of one or two dimensions at a time (a process termed “gating”, with boundaries or “gates” defining cell populations of interest). However, these visual approaches are labor intensive and highly subjective, and they neglect information present in the data that are not visible to the human eye, thus representing a major obstacle to the automation and reproducibility of research.

Source text:
(Finak., 2016) Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Scientific Reports.

1 - 26 of 26 results
filter_list Filters
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 26 of 26 results
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.
star_border star_border star_border star_border star_border
star star star star star
A flow cytometry data analysis software.. FlowJo contains a list of loaded samples (experimental data), statistics, gates, and other analyses, as well as tabular and graphical layouts. FlowJo provides features and tools for the creation of histogram and other plot overlays, cell cycle analysis, calcium flux analysis, proliferation analysis, quantitation, cluster identification and backgating display.
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.
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 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.
Provides a series of polygon filters, or “gates”, that discriminate the target cell population from all other cells. GateFinder constructs simple phenotypic signatures for target cell populations identified through high-dimensional single-cell cytometry. The software can address several related challenges in cytometry analysis, such as identifying surrogate phenotypes, designing efficient follow-up experiments, and distilling mechanistic insights from high-dimensional signatures. It can also accelerate the analysis pipeline for high-dimensional single-cell experiments.
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 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.
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 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.
0 - 0 of 0 results
1 - 6 of 6 results
filter_list Filters
computer Job seeker
Disable 2
person Position
thumb_up Fields of Interest
public Country
language Programming Language
1 - 6 of 6 results