Data transformation software tools | Flow cytometry analysis
Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific.
A data management system for flow cytometry data analysis. flowCore allows to handle flow cytometry high content screening data and encourages open development of tools for their coherent analysis. A key component of this package is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. Flowcore constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, statisticians, biologists and clinicians.
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.
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 package that provides graphical diagnostics and quality assessment applications. flowViz adapts principles of Trellis graphics to FCM data. It provides useful visualizations that can aid automated analysis of flow cytometry data.
A package that provides profile maximum likelihood estimation of parameters for flow cytometry data transformations. Parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.