Quality assessment software tools | Flow cytometry data analysis
Effective quality assessment is an important part of any high-throughput flow cytometry data analysis pipeline, especially when considering the complex designs of the typical flow experiments applied in clinical trials. Technical issues like instrument variation, problematic antibody staining, or reagent lot changes can lead to biases in the extracted cell subpopulation statistics. These biases can manifest themselves in non-obvious ways that can be difficult to detect without leveraging information about the study design or other experimental metadata.
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.
Manages data and investigation in flow cytometry derived from the NovoCyte® Flow Cytometer. NovoExpress includes a wide range of features including sample acquisition, data analysis, the producing of quality check (QC) reports and graphs that can be exported for further analysis or publication as well as functions for management of both data and groups of users within a given organization.
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 for the analysis of flow cytometry data. flowFP provides tools to transform raw flow cytometry data into a form suitable for direct input into conventional statistical analysis and empirical modeling software tools. The approach of flowFP is to generate a description of the multivariate probability distribution function of flow cytometry data in the form of a “fingerprint.” As such, it is independent of a presumptive functional form for the distribution, in contrast with model-based methods such as Gaussian Mixture Modeling. The broad aim of the package is to directly transform raw FC list-mode data into a representation suitable for direct input to other statistical analysis and empirical modeling tools. Thus, it is useful to think of flowFP as an intermediate step between the acquisition of high-throughput FC data on the one hand, and empirical modeling, machine learning, and knowledge discovery on the other.
Takes advantage of the manual gates to perform an extensive series of statistical quality assessment checks on the gated cell sub–populations while taking into account the structure of the data and the study design to monitor the consistency of population statistics across staining panels, subject, aliquots, channels, or other experimental variables. QUAliFiER implements SVG–based interactive visualization methods, allowing investigators to examine quality assessment results across different views of the data, and has a flexible interface allowing users to tailor quality checks and outlier detection routines to suit their data analysis needs. The QUAliFiER tool objectively, efficiently, and reproducibly identifies outlier samples in an automated manner by monitoring cell population statistics from gated or ungated flow data conditioned on experiment–level metadata.
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.
Provides quality control and quality assessment tools for flow cytometry data. flowQ creates concise reports of quality checks on single and multi-panel experiments to highlight issues that can be encountered in data acquisition. The reports indicate the number of cells, percentage of boundary events and anomalies on the fluidics and signal acquisition over time.