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SPADE / Spanning tree Progression of Density normalized Events
Facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations, based on a versatile method. SPADE helps to organize high-dimensional cytometry data in an unsupervised manner, and to investigate natural and pathogenic cellular heterogeneity for biological insight. The SPADE algorithm consists of four components: (i) density-dependent downsampling, (ii) clustering, (iii) linking clusters with a minimum spanning tree, and (iv) upsampling to restore all cells in the final result. This modularized process allows more efficient sub-algorithms to replace the current components. In this sense, SPADE can be viewed as a framework for cytometric data analysis and visualization that has the capacity to be evolved and adapted.
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.
Detects ‘differentially abundant’ populations between samples and groups in mass cytometry data. Cydar is a computational strategy to perform differentially abundant (DA) analyses of mass cytometry data that does not rely on an initial clustering step. This software allocates cells to hyperspheres, tests for differential abundance of cells between conditions for each hypersphere, and controls the false discovery rate (FDR) across the high-dimensional space. Cydar can be used to robustly detect differentially abundant subpopulations or shifts in marker expression between conditions.
Enables simultaneous illustration of cellular diversity and progression. Cytofkit firstly performs data pre-processing, and enables combined analysis of multiple Flow Cytometry Standard (FCS) files. Users are allowed to customize their data merging strategy to combine them using selectable transformation methods. The remaining three steps address respectively, each of the three following challenges: efficient visualization of these high-dimensional data, identifying cell subpopulations and detecting cellular progression. Cytofkit provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers.
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A package for identifying clusters of cells in multidimensional flow cytometry data associated with an experimental or clinical endpoint of interest. A Citrus analysis generally involves four steps: (I) You must tell Citrus what your experimental endpoint of interest is; (II) cell subsets are identified in all samples using hierarchical clustering. You can think of this step as roughly equivalent to manual gating; (III) properties of every identified cell subset are calculated on a per-sample basis; (IV) descriptive properties from all discovered cellular subsets are evaluated for an association with your experimental endpoint of interest. Citrus reports those cellular subsets that are likely to be predictive or correlated with the experimental endpoint.
ACCENSE / Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding
A standalone application for exploratory analysis of high-dimensional single-cell data such as that generated by Mass Cytometry (CyTOFTM, Fluidigm Corp.). The main functions that ACCENSE provides are: (i) It performs a nonlinear dimensionality reduction on the high-dimensional single-cell data and obtains the inferred low-dimensional representation, (ii) The low dimensional data can be visualized with abundant coloring options, and (iii) It provides clustering methods to automate the classification of cellular sub-populations.
A density-based clustering algorithm, which is both time- and space-efficient and proceeds as follows: densityCut first roughly estimates the densities of data points from a K-nearest neighbour graph and then refines the densities via a random walk. A cluster consists of points falling into the basin of attraction of an estimated mode of the underlining density function. A post-processing step merges clusters and generates a hierarchical cluster tree. The number of clusters is selected from the most stable clustering in the hierarchical cluster tree. densityCut effectively clustered irregular shape synthetic benchmark datasets. We have successfully used densityCut to cluster variant allele frequencies of somatic mutations, single-cell gene expression data, and single-cell CyTOF data. densityCut is based on density estimation on graphs. It could be considered as a variation of the spectral clustering algorithms but is much more time- and space-efficient. Moreover, it automatically selects the number of clusters and works for the datasets with a large number of clusters. In summary, densityCut does not make assumptions about the shape, size, and the number of clusters, and can be broadly applicable for exploratory data analysis.
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.
ACDC / Automated Cell-type Discovery and Classification
Automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. ACDC provides accurate and reliable estimations compared to manual gating results. It automatically classifies previously ambiguous cell types to facilitate discovery. The tool combines profile matching and semi-supervised learning. It takes advantage of biological knowledge to guide learning algorithms and creates a framework for interpreting data from high-dimensional cytometry.
PAC-MAN / Partition-Assisted Clustering and Multiple Alignments of Networks
Identifies automatically cell populations in mass cytometry data closely matching that of expert manual-discovery. PAC-MAN allows the management of very large mass cytometry datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject. It allows to find robust and accurate clusters by using partition-assisted clustering. The tool can be used to make alignments between subpopulations across samples to define dataset-level cellular states.
Analyzes flow or mass cytometry data using a self-organizing map. Using a two-level clustering and star charts, FlowSOM helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. The algorithm consists of four steps: reading the data, building a self-organizing map, building a minimal spanning tree and computing a meta-clustering. We proposed several visualization options: star charts to inspect several markers, pie charts to compare with manual gating results, variable node sizes dependent on the amount of cells assigned to the node and a grid or a tree structure which both give topological information.
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.
mass Cytometry by Time-Of-Flight Linear-Classifier / CyTOF-Linear-Classifier
Permits users to classify cell types in mass cytometry datasets. CyTOF-Linear-Classifier is a linear discriminant analysis (LDA) classifier. It provides a method that enables the analysis of large datasets comprised of millions of cells and to assign labels to cells. It can be used to automatically label cells in mass cytometry data which is a promising step forward to use mass cytometry data in cohort studies.
CATALYST / Cytometry dATa anALYSis Tools
Provides tools for pre-processing and analysis of cytometry data, including compensation and in particular, an improved implementation of the single-cell deconvolution algorithm. CATALYST is a pipeline that including (i) normalization using bead standards, (ii) single-cell deconvolution, and (iii) bead-based compensation. It offers an implementation of bead-based normalization. The identification of bead-singlets (used for normalization), as well as of bead-bead and cell-bead doublets (to be removed) is automated.
one-dimensional Soli-Expression by Nonlinear Stochastic Embedding
Facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm. oneSENSE is a graphical user interface (GUI) for categorical analysis of mass cytometry data. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis. The cellular occupancy of the resulting plots allows direct assessment of the relationships between the categories.
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