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.
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.
Enables flow and image cytometry data analysis. FCS Express RUO is able to analyze flow cytometry data, with flow cytometry tools, plots, and gates. The software allows export to Powerpoint, pdf, Excel, Word, and Microsoft office.
Offers a platform dedicated to cytometry analysis. WinList is an application provindingg a wide range of features including: (i) the production of ratio parameters, equations, alerts as well as reusable report templates and individual histograms; (ii) multiple analysis dealing with rare-event or leukemia-lymphomaanalysis; (iii) the highlighting of events and possible relationships of markers.
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.