AutoGate: automating analysis of flow cytometry data

Abstract

Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots). Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. Ultimately, this process generates analysis “trees” that can be applied to automatically guide analyses for similar samples. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. When this “early adopter” phase is complete, the authors expect to distribute the software free of charge to .edu, .org and .gov users.

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

“…Instead, the sequence of the markers analyzed is still defined by the operator. OpenCyto [16] and FlowDensity [15] R packages, as well as the standalone executable software AutoGate [17], assist the operator in the definition of mono-and bi-dimensional gates, by using methods for boundary definition based on density estimation techniques. The main advantage of the automated sequential gating approach is represented by the automated identification of the cell population in the bi-dimensional scatter plots, overcoming the limitations linked to the manual drawing of gate boundaries, thus improving reproducibility.…”

Flow cytometry data analysis: Recent tools and algorithms

“…Traditional gating‐based analysis is performed manually, and it is based on visual comparison of one or two‐dimensional plots. A sequence of gates must be used to analyze multidimensional datasets . This method of identification of cell populations currently relies on using software to apply a series of manually drawn gates that select regions in the data plots representing the two parameters along the two axes.…”

“…Flow cytometry technology is widely used in clinical and research laboratories. FCM experiments can provide many information that can help to resolve and analyze deeply many biomedical problems . However, the possibility to take full advantage of this information is prevented by the complexity of the data analysis stage, where the gating step constitutes its major bottleneck.…”

“…A sequence of gates must be used to analyze multidimensional datasets. 16,17 This method of identification of cell populations currently relies on using software to apply a series of manually drawn gates that select regions in the data plots representing the two parameters along the two axes. This process is based on the expertise of the operator rather than standardized statistical inference.…”

Automatically generate two‐dimensional gating hierarchy from clustered cytometry data

“…For example, flowType and RchyOptimyx were designed to enumerate all cell types defined by clustering algorithms, and identify efficient biomarker combinations that correlate with clinical features. AutoGate was designed to automatically draw gates in an unsupervised fashion, producing gating visualizations without requiring users to manually define boundaries of the gates. Implementations in OpenCyto include automated pipelines for generating gating visualizations given a user‐defined gating hierarchy, which can be viewed as a supervised approach.…”