Mass cytometry addresses the analytical challenges of polychromatic flow cytometry by using metal atoms as tags rather than fluorophores and atomic mass spectrometry as the detector rather than photon optics.
Aligns single cells from differentiation systems with bifurcating branches. Wishbone pinpoints bifurcation points and labels each cell as pre-bifurcation or as one of two post-bifurcation cell fates to order cells according to their developmental progression. It is generalizable to additional lineages, as it was demonstrated by applying it to mouse myeloid differentiation. The tool outperforms methods developed specifically for single cell RNA-seq data.
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.
A graph-based trajectory detection algorithm that receives multiparameter single-cell events as input and maps them onto a one-dimensional developmental trajectory. Cells are ordered along a trajectory that represents their most likely placement along a developmental continuum.
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.
Adjusts influences of volume and cell-cycle phase on mass cytometry data. CellCycleTRACER is a supervised manifold learning method that utilizes the cell-cycle phase labels to assure the known ordering in inferred embedding. This web application can be applied to any mass cytometry dataset, can detect proteins with a resolution down to a single cell and is able to expose masked signaling relationships and cell heterogeneity.
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.
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.
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.
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.
Identifies a gating strategy optimized for high yield and purity concerning a cell population of interest. Hypergate is a program useful for the detection of Innate lymphoid cells, and spots strategies that allow gating these cells with fewer parameters. It operates by finding a hyperrectangle (or high-dimensional rectangle) that specifically encapsulates the cell cluster of interest, and also replicable results supplies.
Predicts clinical outcomes using single cell data such as flow cytometry data or RNA single cell sequencing data. CytoDx doesn’t require cell gating or clustering to process. It first estimates the association between each single cell and clinical outcome. It then averages cell level associations within samples to obtain predictors for clinical outcome. This software can also predict clinical features even in the presence of batch effects.
Permits user to realize interactive investigation. Cytosplore is a single-cell analysis framework allowing interactive exploration of the hierarchy by a set of embeddings and used for subsequent analysis. It is applicable to mass cytometry datasets and other high-dimensional data like single-cell transcriptomic datasets. It provides distinct advantage of visualizing all cells and intracluster heterogeneity at subsequent levels of detail up to the single-cell level.
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.
Enables automated meta-analysis of cytometry datasets, including both conventional flow and mass cytometry (CyTOF) data. MetaCyto combines clustering methods and a silhouette scanning method. The software employs computational approaches to identify common cell subsets across studies in either of two fully automated pipelines: unsupervised analysis and guided analysis. It also allows to analyze cytometry data from single experiment.
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.
Identifies information rich 2D scatter plots and extracts biological insights from them. CytoBinning is an application that permits to identify which specific regions of the scatter plot exhibits the most notable differences between two groups of donors and to allow subtle shifts in the immune phenotype to be highlighted. It also includes a list of important marker pairs and related important cell sub-regions.
Aligns two manifolds such that related points in each measurement space are aligned together. MAGAN is a generative adversarial network (GAN) that discovers relationships between domains by aligning their manifolds rather than just superimposing them. The algorithm can be used when one system is measured in two different ways and thus forms two different manifolds. It facilitates the integration of datasets from multiple biological modalities.
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.
Retrieves cell populations or states associated with an outcome variable in high-dimensional cytometry data. diffcyt employs high-resolution unsupervised clustering together with supervised statistical analyses. It applies empirical Bayes moderated tests for differential discovery analyses in high-dimensional cytometry data. This tool can account for complex experimental designs, including batch effects, paired designs, and continuous covariates.
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.
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.
Serves for automatic gating. DeepCyTOF is an integrated deep learning domain adaptation framework employing one manually gated reference sample and utilizes it for automated gating of the remaining samples in a study. It allows users to handle missing data and uses multiple distribution matching residual networks to calibrate an arbitrary number of source samples to a fixed reference sample.
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