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Data visualization software tools | Flow cytometry analysis

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. Source text: Van Gassen et al., 2015.

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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.
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.
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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 library for reading, analyzing, and calibrating flow cytometry data. FlowCal can be run using an intuitive Microsoft Excel interface, or customizable Python scripts. The software accepts Flow Cytometry Standard (FCS) files as inputs and is compatible with different calibration particles, fluorescent probes, and cell types. Additionally, FlowCal automatically gates data, calculates common statistics, and produces publication quality plots. FlowCal should ease the quantitative analysis of flow cytometry data within and across laboratories and facilitate the adoption of standard fluorescence units in synthetic biology and beyond.
Synthesizes low-dimensional visualizations of flow cytometry data. SANJAY is an algorithmic approach for automatically synthesizing 2D and 3D visualizations of high-dimensional flow cytometry data. It employs automated algorithmic synthesis techniques and symbolic decision procedures to create low-dimensional projections of high-dimensional data that can be easily visualized. Results synthesized by SANJAY were better than those produced by the multi-dimensional scaling and random projections approaches in terms of the maximum distortion in the pairwise distances.
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.
Assists users in visualization and analysis of high-throughput flow cytometry data. FlowCytometryTools provides an interface that allows users to directly work with collections of flow cytometry measurements. It includes different features such as the transformations of hyperlog or truncated log, the plotting of 1D, 2D histograms for both single samples and collections, the subsampling to examine only part of a measurement and randomize event order. It also offers a graphical interface to draw gates.
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