Stephen Meehan

Automatic Clustering of Flow Cytometry Data with Density-Based Merging

The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

AutoGate: automating analysis of flow cytometry data

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.

Earth Mover’s Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations

Changes in the frequencies of cell subsets that (co)express characteristic biomarkers, or levels of the biomarkers on the subsets, are widely used as indices of drug response, disease prognosis, stem cell reconstitution, etc. However, although the currently available computational “gating” tools accurately reveal subset frequencies and marker expression levels, they fail to enable statistically reliable judgements as to whether these frequencies and expression levels differ significantly between/among subject groups. Here we introduce flow cytometry data analysis pipeline which includes the Earth Mover’s Distance (EMD) metric as solution to this problem. Well known as an informative quantitative measure of differences between distributions, we present three exemplary studies showing that EMD 1) reveals clinically-relevant shifts in two markers on blood basophils responding to an offending allergen; 2) shows that ablative tumor radiation induces significant changes in the murine colon cancer tumor microenvironment; and, 3) ranks immunological differences in mouse peritoneal cavity cells harvested from three genetically distinct mouse strains.

A Quantitative Method for Comparing the Brightness of Antibody-dye Reagents and Estimating Antibodies Bound per Cell.

We present a quantitative method for comparing the brightness of antibody‐dye reagents and estimating antibodies bound per cell. The method is based on complementary binding of test and fill reagents to antibody capture microspheres. Several aliquots of antibody capture beads are stained with varying amounts of the test conjugate. The remaining binding sites on the beads are then filled with a second conjugate containing a different fluorophore. Finally, the fluorescence of the test conjugate compared to the fill conjugate is used to measure the relative brightness of the test conjugate. The fundamental assumption of the test‐fill method is that if it takes X molecules of one test antibody to lower the fill signal by Y units, it will take the same X molecules of any other test antibody to give the same effect. We apply a quadratic fit to evaluate the test‐fill signal relationship across different amounts of test reagent. If the fit is close to linear, we consider the test reagent to be suitable for quantitative evaluation of antibody binding. To calibrate the antibodies bound per bead, a PE conjugate with 1 PE molecule per antibody is used as a test reagent and the fluorescence scale is calibrated with Quantibrite PE beads. When the fluorescence per antibody molecule has been determined for a particular conjugate, that conjugate can be used for measurement of antibodies bound per cell. This provides comparisons of the brightness of different conjugates when conducted on an instrument whose statistical photoelectron (Spe) scales are known.

QFMatch: multidimensional flow and mass cytometry samples alignment

Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by the curse of dimensionality, or fail when population patterns significantly vary between samples. Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) that is computationally efficient and accommodates cases where population locations differ significantly (or even disappear or appear) from sample to sample. We demonstrate the effectiveness of QFMatch by evaluating sample datasets from immunology studies. The algorithm is based on a novel multivariate extension of the quadratic form distance for the comparison of flow cytometry data sets. We show that this QF distance has attractive computational and statistical properties that make it well suited for analysis tasks that involve the comparison of flow/mass cytometry samples.