Raphael Gottardo

Ryan R. Brinkman

George Luta

Matt P. Wand

Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data

 We demonstrate the application and comparative interpretations of 
   three tree-based algorithms for the analysis of data arising from 
   flow cytometry: classification and regression trees (CARTs), random 
   forests (RFs), and logic regression (LR). Specifically, we consider 
   the question of what best predicts CD4 T-cell recovery in HIV-1 
   infected persons starting antiretroviral therapy with CD4 count 
   between 200 and 350 cell/<svg style="vertical-align:-3.56265pt;width:9.5625px;" id="M1" height="12.175" version="1.1" viewBox="0 0 9.5625 12.175" width="9.5625" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,7.675)"><path id="x1D707" d="M538 96q-38 -45 -84 -76.5t-71 -31.5q-44 0 -15 117q12 49 18 89h-2q-99 -128 -163 -178q-36 -28 -70 -28q-26 0 -44 38h-2q-16 -69 -16 -129q0 -43 11 -75.5t24 -42.5l1 -6q-7 -12 -24.5 -23t-37.5 -11q-40 0 -40 76q0 52 35 202l94 407l78 24l5 -5q-32 -122 -72 -310&#xA;q-7 -33 0 -55t25 -22q36 0 105.5 75t107.5 145l32 146l75 26h10q-50 -197 -78 -347q-7 -38 6 -38q7 0 31 17t47 39z" /></g></svg>L. A comparison to a more standard 
   contingency table analysis is provided. While contingency table 
   analysis and RFs provide information on the importance of each 
   potential predictor variable, CART and LR offer additional insight 
   into the combinations of variables that together are predictive of 
   the outcome. In all cases considered, baseline CD3-DR-CD56+CD16+ 
   emerges as an important predictor variable, while the tree-based 
   approaches identify additional variables as potentially informative. 
   Application of tree-based methods to our data suggests that a 
   combination of baseline immune activation states, with emphasis on 
   CD8 T-cell activation, may be a better predictor than any single 
   T-cell/innate cell subset analyzed. Taken together, we show that 
   tree-based methods can be successfully applied to flow cytometry data 
   to better inform and discover associations that may not emerge in 
   the context of a univariate analysis. 

M. Eliot

L. Azzoni

C. Firnhaber

W. Stevens

D. K. Glencross

I. Sanne

L. J. Montaner

A. S. Foulkes

Tree-Based Methods for Discovery of Association between Flow Cytometry Data and Clinical Endpoints

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed
to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell
grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an
inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation,
making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis
approaches using a framework introduced to report each of the components in a data analysis pipeline. Current
challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.

Ali Bashashati

A Survey of Flow Cytometry Data Analysis Methods

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.

Guenther Walther

Noah Zimmerman

Wayne Moore

David Parks

Stephen Meehan

Ilana Belitskaya

Jinhui Pan

Leonore Herzenberg

Automatic Clustering of Flow Cytometry Data with Density-Based Merging

A next step to interpret the findings generated by genome-wide association studies is to associate
molecular quantitative traits with disease-associated alleles. To this end, researchers are linking disease
risk alleles with gene expression quantitative trait loci (eQTL). However, gene expression at the
mRNA level is only an intermediate trait and flow cytometry analysis can provide more downstream
and biologically valuable protein level information in multiple cell subsets simultaneously using freshly
obtained samples. Because the throughput of flow cytometry is currently limited, experiments may
need to span over several weeks or months to obtain a sufficient sample size to demonstrate genetic
association. Therefore, normalisation methods are needed to control for technical variability and compare
flow cytometry data over an extended period of time. We show how the use of normalising
fluorospheres improves the repeatability of a cell surface CD25-APC mean fluorescence intensity phenotype
on <svg     style="vertical-align:-0.20473pt;width:39.6875px;" id="M1" height="14.375" version="1.1" viewBox="0 0 39.6875 14.375" width="39.6875"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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			<g transform="matrix(.012,-0,0,-.012,32.025,5.887)"><path id="x2B" d="M535 230h-212v-233h-58v233h-213v50h213v210h58v-210h212v-50z" /></g>

		
	
</svg> memory T cells. We investigate two types of normalising beads: broad spectrum and
spectrum matched. Lastly, we propose two alternative normalisation procedures that are usable in the
absence of normalising beads.

Calliope A. Dendrou

Erik Fung

Laura Esposito

John A. Todd

Linda S. Wicker

Vincent Plagnol

Fluorescence Intensity Normalisation: Correcting for Time Effects in Large-Scale Flow Cytometric Analysis

We present a framework for the identification of cell subpopulations in 
flow cytometry data based on merging mixture components using the 
flowClust methodology. We show that the cluster merging algorithm 
under our framework improves model fit and provides a better 
estimate of the number of distinct cell subpopulations than 
either Gaussian mixture models or flowClust, especially for 
complicated flow cytometry data distributions. Our framework 
allows the automated selection of the number of distinct cell 
subpopulations and we are able to identify cases where the 
algorithm fails, thus making it suitable for application in a high 
throughput FCM analysis pipeline. Furthermore, we demonstrate a 
method for summarizing complex merged cell subpopulations in a 
simple manner that integrates with the existing flowClust 
framework and enables downstream data analysis. We demonstrate the 
performance of our framework on simulated and real FCM data. The 
software is available in the flowMerge package through the 
Bioconductor project.

Greg Finak

Ryan Brinkman

Raphaël Gottardo

Merging Mixture Components for Cell Population  Identification in Flow Cytometry

Flow cytometry (FCM) has become an important analysis technology in health care and medical research, but the large volume of data produced by modern high-throughput experiments has presented significant new challenges for computational analysis tools. The development of an FCM software suite in Bioconductor represents one approach to overcome these challenges. In the spirit of the R programming language (Tree Star Inc., “FlowJo”), these tools are predominantly console-driven, allowing for programmatic access and rapid development of novel algorithms. Using this software requires a solid understanding of programming concepts and of the R language. However, some of these tools|in particular the statistical graphics and novel analytical methods|are also useful for nonprogrammers. To this end, we have developed an open source, extensible graphical user interface (GUI) <i>iFlow</i>, which sits on top of the Bioconductor backbone, enabling basic analyses by means of convenient graphical menus and wizards. We envision <i>iFlow</i> to be easily extensible in order to quickly integrate novel methodological developments.

Kyongryun Lee

Florian Hahne

Deepayan Sarkar

Robert Gentleman

iFlow: A Graphical User Interface for Flow Cytometry Tools in Bioconductor

Flow cytometry (FCM) software packages from R/Bioconductor, such as flowCore and flowViz, serve as an open platform for development of new analysis tools and methods. We created plateCore, a new package that extends the functionality in these core packages to enable automated negative control-based gating and make the processing and analysis of plate-based data sets from high-throughput FCM screening experiments easier. plateCore was used to analyze data from a BD FACS CAP  screening experiment where five Peripheral Blood Mononucleocyte Cell (PBMC) samples were assayed for 189 different human cell surface markers. This same data set was also manually analyzed by a cytometry expert using the FlowJo data analysis software package (TreeStar, USA). We show that the expression values for markers characterized using the automated approach in plateCore are in good agreement with those from FlowJo, and that using plateCore allows for more reproducible analyses of FCM screening data.

Errol Strain

Perry Haaland

Analysis of High-Throughput Flow Cytometry Data Using plateCore

In flow cytometry, different cell types are usually selected or &#8220;gated&#8221; by a series of 1- or 2-dimensional geometric subsets of the measurements made on each cell. This is easily accomplished in commercial flow cytometry packages but it is difficult to work computationally with the results of this process. The ability to retrieve the results and work with both them and the raw data is critical; our experience points to the importance of bioinformatics tools that will allow us to examine gating robustness, combine manual and automated gating, and perform exploratory data analysis. To provide this capability, we have developed a Bioconductor package called flowFlowJo that can import gates defined by the commercial package FlowJo and work with them in a manner consistent with the other flow packages in Bioconductor. We present this package and illustrate some of the ways in which it can be used.

John J. Gosink

Gary D. Means

William A. Rees

Cheng Su

Hugh A. Rand

Bridging the Divide between Manual Gating and Bioinformatics with the Bioconductor Package flowFlowJo

A new software package called flowFP for the analysis of flow cytometry data is introduced.  The package, which is tightly integrated with other Bioconductor software for analysis of flow cytometry, 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 &#8220;fingerprint.&#8221; As such, it is independent of a presumptive functional form for the distribution, in contrast with model-based methods such as Gaussian Mixture Modeling. FlowFP is computationally efficient and able to handle extremely large flow cytometry data sets of arbitrary dimensionality. Algorithms and software implementation of the package are described. Use of the software is exemplified with applications to data quality control and to the automated classification of Acute Myeloid Leukemia.

Wade T. Rogers

Herbert A. Holyst

FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data

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