1 - 8 of 8 results


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.


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.


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.


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.


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.