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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.

Source text: (Finak et al., 2012) QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data. BMC Bioinformatics.

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An R package containing two methods to clean flow cytometry files from unwanted events: A) an automatic method that adopts algorithms for the detection of anomalies, B) an interactive method with a graphical user interface implemented into an R shiny application. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from 1) abrupt changes in the flow rate, 2) instability of signal acquisition, 3) outliers in the lower limit and margin events in the upper limit of the dynamic range. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on flow cytometry data. We recommend the usage of flowAI as a first preprocessing step of the data right after they are obtained from the flow cytometry instrument so that all the downstream analyses, from compensation to detection or rare cells, will benefit from it.
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.
Identifies flow cytometry (FCM) cell populations. flowLearn works in a semi-supervised mode and requires the gating of one or few characteristic samples by a human expert in the form of thresholds. The software does not have to deal with the problem of noise inherent to such spaces and opens the way towards a quality control of samples which have already been gated. It can be used to spot problems in existing gating hierarchies, offering the possibility to be used to identify problematic gates.
A package that makes manually gated data accessible to BioConductor’s computational flow tools by importing pre–processed and gated data from the widely used manual gating tool, FlowJo. flowWorkspace makes manually gated data from large, arbitrarily complex FCM studies accessible in the R environment. It imports compensation matrices, data transformations, manual gates, and FCS files from analyses described in FlowJo workspaces, and reproduces them using the BioConductor flow toolset, thus making manually gated data accessible to the computational flow community. The tool has methods implemented for visualizing, summarizing, extracting and exporting population statistics for gated cell populations. Importantly, it can handle large FCM data sets through support of NetCDF via the ncdfFlow package. flowWorkspace can also be used to export data to the LabKey tool, allowing one to use R as the engine for flow data analysis with a LabKey front end and data repository.
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.
Evaluates backgrounds and statistical photoelectron (Spe) scales on fluorescence cytometers. flowQB is an automated analysis method that allows users to calculate detector efficiency, optical background and intrinsic CV of the beads. An alternative method, included in the flowQB package, re-evaluates the weights after initial fitting and refits until the results reach convergence. This software was applied to a series of instruments using both LED signals and multilevel, multidye bead sets.
An algorithm to track subset frequency changes within a sample during acquisition, and flag time periods with fluorescence perturbations leading to the emergence of false populations. Aberrant time periods are reported as a new parameter and added to a revised data file, allowing users to easily review and exclude those events from further analysis. We apply this method to proof-of-concept datasets and also to a subset of data from a recent vaccine trial. The algorithm flags events that are suspicious by visual inspection, as well as those showing more subtle effects that might not be consistently flagged by investigators reviewing the data manually, and out-performs the current state-of-the-art.
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.
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