 Methodology article
 Open Access
 Published:
The curvHDR method for gating flow cytometry samples
BMC Bioinformatics volume 11, Article number: 44 (2010)
Abstract
Background
Highthroughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is timeconsuming and subjective. Good automatic and semiautomatic gating algorithms are very beneficial to highthroughput flow cytometry.
Results
We develop a statistical procedure, named curvHDR, for automatic and semiautomatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to humanperceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry informatics, is developed.
Conclusion
The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. It offers big savings in human labour when processing highthroughput flow cytometry data whilst retaining a good degree of efficacy.
Background
Flow cytometry is a laserbased biotechnology that produces large multivariate samples. Typically, each member of the sample corresponds to the physical properties of a biological cell  known as forward scatter and side scatter  and antibody binding activity, through fluorescence intensity measurements. The latter measurements arise from the cells being exposed to several fluorescently conjugated antibodies during the flow cytometry procedure. Shapiro [1] provides a detailed summary of flow cytometry technology and its practice.
The last few years have seen a major change in flow cytometry technology, toward what has become known as highthroughput flow cytometry or highcontent flow cytometric screening (FCHCS) (e.g. Le Meur et al. [2]). FCHSC combines robotic fluid handling, flow cytometric instrumentation and bioinformatics software so that relatively large numbers of flow cytometric samples can be processed and analysed in a short period of time. Currently, analysis of such data involves a tremendous amount of manual manipulation. This is costly in time and human energy, and renders the analysis more subjective and errorprone. An early article on FCHCS by Gasparetto et al. [3] closes with: "Further improvements that completely automate the FCHCS procedures and incorporate newly developed advanced data analysis and management features will further improve the efficiency and power of this technique".
An integral component of flow cytometric data analysis is gating, where cells are subsetted according to physical and fluorescence measurements. Recent studies involving high throughput flow cytometric data (e.g. Gasparetto et al. [3]; Brinkman et al. [4]) have involved manual gating of hundreds of flow cytometric samples. Automatic gating methods are becoming more important in contemporary flow cytometry research. If done well, they are more objective, much faster and less expensive. Combined with the automated aspects of new highthroughput flow cytometry technology good automatic gating methods have the potential to open up a wide range of possibilities in biomedical research.
In this article we describe a new method for automatic and semiautomatic gating of multivariate flow cytometry samples. We call the method curvHDR since it makes use of two statistical concepts with regard to the density of the samples: (a) significant high negative curvature corresponding to modal regions and (b) highest density regions (HDR) for data in the vicinity of identified modal regions. The significant curvature phase is useful for identifying regions containing a possibly interesting subset of cells. The HDR phase then aims to improve upon high curvature regions and mimic human perception of what are subsets of interest. The principles underlying curvHDR apply to samples of arbitrary dimension. However, in the present article, we restrict attention to dimensions between one and three.
Often the gate obtained from curvHDR needs to be combined with other simpler gates for effective utilisation. One instance where this applies is when unimportant 'debris' cells near the boundary of the sample exhibit high negative curvature in their density. Rectangular gating, where variables in each direction are restricted to lie within an interval, is often an effective means of eliminating spurious components of a curvHDR gate. Naumann & Wand [5] used curvHDR gates combined with rectangular gates in a flowcytometric application. The Results section provides some illustration of this type of gating.
Our curvHDR methodology is accompanied by software in the R computing environment (R Development Core Team [6]) and, hence, can be integrated into Bioconductor (Gentleman et al. [7]).
The ability to handle trivariate samples is a particularly novel aspect of curvHDR. Traditionally, gating has been limited to two dimensions because of graphical display restrictions. However, recent developments in three dimensional (3D) graphics in the R computing environment allow for routine visualisation of trivariate data and polyhedral gates. The R packages rgl (Adler & Murdoch [8]) and misc3d (Feng & Tierney [9, 10]) are especially useful for work of this kind.
Not surprisingly, other research teams involved in flow cytometric data analysis recently have been developing automatic gating procedures in response to the highthroughput sea change. For example, Lo, Brinkman & Gottardo [11] combine tmixture models and BoxCox transformations to obtain flexible and outlierresistant gates whilst Finak, Bashashati, Brinkman & Gottardo [12] use the Bayesian Information Criterion to approximate optimal merging of such gates. In our view, it is too early for extensive comparison of automatic gating procedures that have been spawned by the demands of highthroughput flow cytometry. At this stage we welcome the development of a variety of approaches. Detailed comparative evaluation would be useful at a later stage; after the 'dust settles'. However, the Results section contains some very brief comparison of curvHDR with the method of Lo et al. [11].
Flow cytometry background
Shapiro [1] provides a comprehensive survey of flow cytometry. Mathematically, typical flow cytometric samples can be thought of as large point clouds in highdimensional space. The dimension is somewhere between about 3 and 15 and the number of points, usually corresponding to cells, is often between tens of thousands and hundreds of thousands. Two of the dimensions usually correspond to the intensity of forward scatter and side scatter which characterise the physical properties of the cell (e.g. size and granularity). The remaining dimensions correspond to the intensity of the cell's fluorescence at a given wavelength (colour). In medical research contexts the colours often correspond to staining of the cells by monoclonal antibodies.
The most important types of gating are (i) bivariate celltype gating (e.g. identification of lymphocytes from scatterplots of forwardscatter versus sidescatter measurements) and (ii) univariate fluorescencechannel gating (e.g. identification of cells that recognise a particular antibody). However, there is no cogent reason for restriction of gating to one and two dimensional projections of flow cytometry point clouds. Roederer & Hardy [13], for example, advocate gating in three and higher dimensions.
Manual gating in practical flow cytometry data analyses usually involves a combination of biological domain knowledge and visual inspection of flow cytometry scatterplots and histograms. But, typically, gates correspond to modal regions in the data. Mathematically, modal regions are those regions where the underlying density function of the data is higher than surrounding regions. The quality of an automatic gating method depends on how well it mimics human perception of what is an appropriate gate. Obviously, this is a difficult goal since perceptions differ from one human to another and there is no single 'right answer'.
An appreciation of humanperceived gates can be obtained from Figure 1. The data are an illustrative subset of the longitudinal flow cytometric data on graftversushost disease described in Brinkman et al. [4] and are available in the Bioconductor package flowViz (Ellis et al. [14]; Sarkar, Le Meur & Gentleman [15]) where it is stored as a flowSet named GvHD. Each panel corresponds to a different day number with respect to blood and marrow transplant of a particular patient. The vertical axis is sinh^{1}(sidescatter) whilst the horizontal axis is sinh^{1}(fluorescence) for the second channel. The gates were drawn by a flow cytometry expert: Dr John Zaunders of the Centre for Immunology, Sydney, Australia. At the time that the gates were drawn, Dr Zaunders had no knowledge of the present article or its content.
Manual gates such as those shown in Figure 1 combine biological domain knowledge with the modal regions apparent from the data. The former is not easily quantified mathematically. Nevertheless, automatic and semiautomatic gating that makes use of the modal region aspects of gating can still be very useful: taking away the human judgement element and permitting faster processing of highthroughput samples. The curvHDR method, described in the next section, aims to fill this niche.
Methods
Let d be the dimension of data in which a gate is sought and let
be a sample in ℛ ^{d}for which gating is desirable. We will assume that gates of interest correspond to modal regions in the sample. This first entails assuming that the x_{ i }s are a sample from a smooth dvariate density function f. Modal regions then correspond to local maxima in f and their surrounds.
The first phase of the curvHDR method employs recently developed feature significance technology (Duong, Cowling, Koch & Wand [16]) to find regions where f has statistically significant high negative curvature. This phase can be thought of as filtering process where aberrant regions of high relative density are ignored and only those regions having statistical evidence of modality are retained. The second phase aims to improve upon the regions obtained in the first phase by modifying them to suit the local density of the data around each high curvature region.
The specific steps of the curvHDR gating method are:

(1)
Remove excessive boundary points and other debris from the data. If the data exhibits heavy skewness then transform the data to reduce skewness. A good 'allpurpose' transformation is the inverse hyperbolic sine transformation .

(2)
Standardise all variables to have zero mean and unit standard deviation.

(3)
Obtain significant high negative curvature regions using the test described in Section 3.2 of Duong et al. [16] over a ddimensional mesh. The regions are stored as intervals for univariate data (d = 1), polygons for bivariate data (d = 2) and polyhedra for trivariate data (d = 3). Let S denote the number of significant curvature regions.

(4)
Replace each of the S significant curvature regions by their convex hulls.

(5)
Grow each convex hull so that its volume is G times larger (for some prespecified growth factor G > 1). This is achieved by 'rolling' a ddimensional sphere around the perimeter of the region.

(6)
For each of the S grown regions, determine the subset of the data lying inside that region.

(7)
For each of the S data subsets, obtain a kernel density estimate, based on a multistage plugin bandwidth selector (Duong & Hazelton [17]), and using only the data in that subset.

(8)
The curvHDR gate is the union of the levelτ HDRs (see definition below) based on the S kernel density estimates. The curvHDR gate will have greater than or equal to S components, where a component is an interval, polygon or polyhedron depending on whether d = 1, d = 2 or d = 3.

(9)
Determine the indices of the data corresponding to the curvHDR gate.

(10)
Transform the gate and gated data back to the original units.
Figure 2 provides graphical illustration of Steps (3)(8) for the case d = 2.
Step (3) requires estimates of the Hessian matrix of f, the d × d matrix with (i, j) entry equal to , with x_{ i }denoting the i th entry of x. Each derivative estimate is obtained via appropriate differentiation of the dvariate kernel density estimator
where K is a dvariate kernel function and H is a d × d bandwidth matrix. Details are given in Duong et al. [16]. In curvHDR we use a single parameter bandwidth matrix for some h_{curv} > 0. This is partially justified by the fact that input data for kernel density estimation is such that each variable has unit standard deviation. Several embellishments are possible, each covered by Wand & Jones [18], but are yet to be entertained for curvHDR. Section 3.2 of Duong et al. [16] describes how the estimated Hessian matrix can be used to determine regions in ℛ^{d}where f has significant high negative curvature. These correspond to local maxima in the underlying density and identify candidate locations for which gating might be appropriate.
The R package feature (Duong & Wand [19]) provides implementation of the significant curvature determination. Efficient computation is achieved using linear binning over a dvariate grid (Wand [20]). This approach leads to a grid of indicators (0/1) for significant high negative curvature. Contouring functions in R such as contourLines() in bivariate case and contour3d() in the trivariate case can then be used to extract and store the regions as polygons (d = 2) or polyhedra (d = 3). The d = 1 case is much simpler and high curvature regions correspond to intervals.
Details on Steps (4)(6) are postponed to upcoming subsections, where the d = 2 and d = 3 cases are treated separately. No such details are necessary for d = 1 since these steps involve elementary manipulations of intervals.
Step (7) involves application of formula (1) to each grown region and the data that it contains. The kernel K is taken to be the dvariate standard normal density function
The bandwidth matrix is chosen using multistage plugin strategies (Duong & Hazelton [17]; Wand & Jones [21]) courtesy of the R package ks (Duong [22]). Further details are given in the parameter choice subsection. In most cases, the Step (6) density estimates are concerned with unimodal structure where plugin bandwidths perform quite well.
For a dvariate density function f and τ ∈ [0, 1] the τ highest density region (HDR) is
(e.g. Hyndman [23]). We can think of the R_{ τ } as corresponding 'meaningful' contours of the density function f. For example, R_{0.9} is the region inside that contour of f for which the probability is 0.1, a relatively small region near the peak of f. The HDR R_{0.1} encompasses 90% of the probability mass of f. In practice, where f is unknown, estimated HDRs can be obtained by replacing f with a density estimate.
In Step (8) we apply the HDR paradigm to each of the density estimates from Step (7). Typically, τ is fixed for all regions although individual τ values could also be specified. We have found that lower τ values are more in keeping with humanbased gating.
Step (9) is similar to Step (6), and details of its execution are discussed in subsections devoted the additional details for bivariate and trivariate samples.
Additional details for bivariate samples
In this section we provide details on aspects of the curvHDR method that are specific to the bivariate case. We begin with Figure 2, which provides a visual overview of curvHDR when d = 2.
We now give some details on Steps (4)(6) in the d = 2 case, as displayed in Panels (b)(d) of Figure 2.
The convex hull of a polygon in ℛ^{2} is a wellknown geometrical construct. A useful physical interpretation involves imagining the vertices of the polygon as nails on a board and stretching an elastic band around outside of the nails. The convex hull then corresponds to the stretched elastic band. In R the convex hull of a polygon can be obtained using the base function chull().
Step (5) involves growing a convex polygon to be G times larger in area via the notion of 'circlerolling'. We first note that the area of a polygon with vertices
and ordered clockwise and such that (x_{1}, y_{1}) = (x_{ N }, y_{ N }) is
Now suppose that we roll a circle of radius r around the perimeter of . A polygonal approximation to the resulting region is obtained by forming normal vectors to each edge of that start from the centre of the edge and radiate outwards a distance of 2r. This approach is illustrated in Panel (c) of Figure 2. Let denote the polygon obtained by joining each of the normal vectors. Step (5) is completed by solving for the r that satisfies A()/A() = G. In our implementation of curvHDR we use a simple bisection search to determine r.
Steps (6) and (9) require the determination of those points that are inside a particular polygon. This is a relatively simple geometric problem and implemented in R by a number of packages. Flow cytometric sample sizes are quite large and speed is important. For this reason, we recommend the function inpolygon() from the Bioconductor package flowCore (Ellis, et al. [24]).
All bivariate kernel density and curvature estimates are obtained via the binned approximation (Wand [20]) over a fine mesh. Choice of the bandwidth matrix is discussed in the parameter choice subsection.
Additional details for trivariate samples
In three dimensions the convex hull corresponds to 'shrink wrapping' a closed polyhedron, and is required for Step (4). Trivariate convex hull computation is facilitated by the function convhulln() in the R package geometry (Grasman & Gramacy [25]).
Steps (3) and (4) make use of the threedimensional contour functionality in the R package misc3d (Feng & Tierney [9, 10]). This package uses triangle mesh objects for storing and displaying polyhedra. The faces of such polyhedra are triangles. For triangularfaced polyhedra, Step (5) is relatively straightforward. A polyhedron is grown by placing a sphere of radius r tangentially to each triangular face, and touching the face at the triangle's centroid. The new polyhedron is the convex hull of the set of antipoles of the touching points. The value of r is chosen so that V()/V() = G, where V () is the volume of an original polyhedron (obtained in Step (4)) and V () is the volume of the grown polyhedron. Note that convhulln() has an option to compute the required volumes.
Steps (6) and (9) require determination of those points in a trivariate sample that lie inside a given polyhedron. This is a nontrivial problem and, to the best of our knowledge, is not supported by any of the current R packages on the Comprehensive R Archive Network. We use an efficient algorithm, specifically designed for largescale problems that involve testing if a large number of points (e.g. hundreds of thousands) lie inside a triangularfaced polyhedron, composed itself of many vertices and faces. The basic idea of the algorithm is that only some faces of the triangular mesh are needed to perform the point containment test; after one of these determining faces is found, testing the given point against this face is sufficient to determine if the point lies inside or outside of the general polyhedron. It should be noted that in principle the algorithm can be extended to higher dimensions. A C++ implementation of this recently developed algorithm is available at the website. http://ptinpoly.pbwiki.com.
Parameter choice
The curvHDR gating method has a suite of parameters that need to be either set to reasonable defaults or chosen by the user. In the interests of making curvHDR as automatic as possible we have, based on extensive experimentation, determined defaults for most of those parameters with the intention that they can remain in the 'background'. Table 1 summarises these default choices.
The bandwidth for the significant curvature phase is the optimal bandwidth for estimation of the dvariate Hessian matrix when f is the standard normal density (Chacon, Duong & Wand [26]). Since the Gaussian density is close to being that with the largest optimal amount of smoothing (Terrell [27]), the table entry corresponds, approximately, to the biggest bandwidth that should be considered for curvature estimation. Note that this formula is only appropriate when the data have first been standardised to have unit standard deviation  as dictated by Step (2).
The curvHDR gate is relatively insensitive to the choice of the significance level for the significant curvature phase and any small value of this parameter is likely to be adequate. Our recommendation of 0.05 matches the most common default for a significance level in statistical procedures.
The growth factor G is defaulted to 2^{d}since it corresponds to an approximate doubling of the size of the original region in each dimension, and has given reasonable answers in examples that we have studied to date. However, there may be circumstances where smaller or larger G values are required for curvHDR to match humanperceived gates.
Recall that Step (7) involves computation of S dvariate kernel density estimates: one for each subset obtained in Step (6). Ideally, these density estimators would use bandwidth matrices tailored for HDR estimation. At the time of this writing, there are no such bandwidth selection algorithms for general d; although Samworth & Wand [28] have recently treated the d = 1 version of the problem. Given its good simulation performance, and because of its availability in R, our current recommendation is to use the multistage plugin bandwidth selector of Duong & Hazelton [17]. This is available in the R package ks (Duong [22]). For d = 1 the relevant function is hpi() while for d = 2, 3 it is Hpi.diag(). For flow cytometric data it is important that the binning flag is set to TRUE since, without binning, the computation is unacceptably slow. Note that ks currently only supports binning for diagonal bandwidth matrices. Finally, for speed reasons again, in the d = 3 case it is recommended that Hpi.diag() uses pilot="samse" and the binning mesh size be kept at a low value such as 21 × 21 × 21.
The only parameter not listed in Table 1 is the level of the highest density region τ . This is because we are uncomfortable about setting a default, given that perception of what is a reasonable gate is somewhat fuzzy, and differs between analysts. Therefore, τ is the main tuning parameter of curvHDR and it is recommended that the user experiments with its choice, perhaps in combination with changes in G. However, if pressed for a default, then τ = 0.1 is a somewhat reasonable answer.
Software
We have written an R function named curvHDRfilter() for implementation of the curvHDR algorithm for input data having dimension between one and three. An accompanying plot() function allows visualization of the gates. For trivariate data, visualization is aided by the RGL graphics device and the packages rgl (Adler & Murdoch [8]) and misc3d (Feng & Tierney [9, 10]). We have commenced work with the developers of flowCore (Ellis et al. [24]) towards making curvHDR usable within that environment. Meanwhile, packaged code and an accompanying vignette is available from the third author (current email address: mwand@uow.edu.au).
Results
We will now provide illustration of curvHDR on the longitudinal flow cytometric data of Figure 1. With space constraints and pedagogy in mind, the illustrations are kept simple and distinct from clinical interpretation and outcomes. See Naumann & Wand [5] for application of curvHDR to cellular signature determination for the graftversushost disease data.
Illustrations of univariate curvHDR
Figure 3 shows two univariate curvHDR gates for some sidescatter data from the GvHD flowSet. The data and its histogram can be obtained using the R commands:
library(flowViz) ; data(GvHD)
inputData < asinh(exprs(GvHD$s9a01)[,2])
hist(inputData,breaks = 100,xlim = c(4,7))
The curvHDR gate in the upper panel has the HDR level set at τ = 0.01, whilst the lower panel has τ = 0.8. The τ = 0.01 gate consists of two intervals; the τ = 0.8 consists of three intervals.
Illustrations of bivariate curvHDR
Figure 4 shows a bivariate curvHDR gate with τ = 0.1. The data are those shown in the upper lefthand panel of Figure 1, corresponding to 6 days before transplant. The data and corresponding scatterplot can be obtained using the R commands:
library(flowViz); data(GvHD)
inputData < asinh(exprs(GvHD$s9a01) [,c(4,2)])
plot(inputData[,1,inputData[,2,xlim = c(5,8.5),ylim = c(4,7))
In Figure 4 we have plotted a subset of these data to enhance visualisation.
Figure 5 shows the result of applying τ = 0.2 gates to all 7 scatterplots. In practice, it is often desirable to restrict attention to a subregion of the data. An effective means of doing this is via intersection with a rectangle. The rectangles in Figure 5 correspond to
Figrure 6 shows the gates after intersection with the rectangle. We call these rectanglecurvHDR gates.
Comparison of Figure 6 with Figure 1 reveals some resemblance between the curvHDR gates and the manual gates that curvHDR is striving to emulate. However, there are also some noticeable differences, as seen by comparing the day = 18 panels. This illustrates limitations of modebased automatic gating methods. A fuller comparison would be interesting, but should involve gates from several experts choosing gates for a larger number of datasets, as well as different choices of the curvHDR tuning parameters.
Comparison with flowClust
The Bioconductor package flowClust (Gottardo and Lo [29]; Lo, Hahne, Brinkman and Gottardo [30]) achieves automated gating through the use of tmixture models and BoxCox transformations. Details of the methodology may be found in Lo, Brinkman & Gottardo [11]. Figure 7 facilitates a cursory visual comparison of curvHDR with flowClust. The data correspond to day = 6 and day = 18 from Figure 5. All gates correspond to the default settings of the tuning parameters. For curvHDR the defaults correspond to Table 1 and τ = 0.1. For flowClust the defaults in its Bioconductor implementation were used. The flowClust method requires specification of the number of clusters K. We set K = 3 for the day = 6 data and K = 1 for the day = 18 to match the number of polygons found by curvHDR, excluding sparse data boundary regions.
One important difference, apparent from Figure 7, is that curvHDR is nonparametric, without any particular shape restrictions, whilst the flowClust gates are parametric  i.e. they correspond to inverse BoxCox transformations of ellipses. The nonparametric aspect of curvHDR allows it to better adapt to the modal structure in the flow cytometry data. The day = 18 data exhibits a pronounced nonconvex modal region, and this is captured by the curvHDR gate. For the day = 6 the curvHDR is more focussed, with nonoverlapping gates for each of the modal regions. The flowClust gates are centred on the same regions, but are considerably larger and overlapping.
Illustrations of trivariate curvHDR
We now provide an illustration of trivariate curvHDR by adding a third variable, forwardscatter, to the longitudinal data of Figure 5. The data and corresponding scatterplot can be obtained using the R commands:
library(flowViz) ; data(GvHD)
inputData < asinh(exprs(GvHD$s9a01) [,c(1,2,4)])
We combined τ = 0.5 curvHDR gating with the rectangular gate:
The resulting rectanglecurvHDR gates are shown in Figure 8. Note that each of the gates consist of between 1 and 3 polyhedra.
Semiautomatic trivariate gating is a novel concept for flow cytometric data analysis. Just as the bivariate gating can offer improvements over univariate gating, we anticipate benefits arising from trivariate gating. With the advent of good threedimensional visualisation software in R/Bioconductor and the emergence of trivariate gating algorithms, such as curvHDR with d = 3, we envisage flow cytometry data analysis breaking away from its current custom of restricting views and gates to two dimensions.
Discussion
The curvHDR method is an intuitive and reasonably simple mechanism for obtaining candidates for celltype gating. The method is intrinsically nonparametric, allowing it to adapt to the data without the restrictions of parametric methods such as those based on the Gaussian density function. Consequently the curvHDR regions are not restricted to be ellipsoidal or to have some other regular shape. With judicious choice of the main tuning parameter τ, possibly in combination with the secondary tuning parameter G, it can mimic human gating quite well. In combination with simple rectangular gating it provides a powerful base with which to build effective automatic gating strategies.
Whilst we have restricted attention and software development to dimensions 13 there is no firm upper limit on the dimensionality in which curvHDR can be applied. Extension of curvHDR beyond three dimensions, in terms of practicable algorithms and software, is an interesting new research problem  and one which could be quite fruitful as flow cytometric data becomes more abundant and complex.
Conclusions
In this study we have proposed an automatic gating method named curvHDR, and worked out the algorithmic details for univariate, bivariate and trivariate data. The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. Naumann & Wand [5] have already used curvHDR in a highthroughput flow cytometry application and demonstrated its efficacy, with big savings in human labour. We are in the process of incorporating the methodology into the R/Bioconductor computing environment with flowCore compatability. This will facilitate the use of curvHDR in future highthroughput flow cytometry analyses.
References
 1.
Shapiro HM: Practical Flow Cytometry. 4th edition. New York: John Wiley & Sons; 2003. full_text
 2.
Le Meur L, Rossini A, Gasparetto M, Smith C, Brinkman RR, Gentleman R: Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry Part A 2007, 71A: 393–403. 10.1002/cyto.a.20396
 3.
Gasparetto M, Gentry T, Sebti S, O'Bryan E, Nimmanapalli R, Blaskovich MA, Bhalla K, Rizzieri D, Haaland P, Dunne J, Smith C: Identification of compounds that enhance the antilymphoma activity of rituximab using flow cytometric highcontent screening. Journal of Immunological Methods 2004, 292: 59–71. 10.1016/j.jim.2004.06.003
 4.
Brinkman RR, Gasparetto M, Lee SJJ, Ribickas AJ, Perkins J, Janssen W, Smiley R, Smith C: Highcontent flow cytometry and temporal data analysis for defining a cellular signature of graftversushost disease. Biology of Blood and Marrow Transplantation 2007, 13: 691–700. 10.1016/j.bbmt.2007.02.002
 5.
Naumann U, Wand MP: Automation in highcontent flow cytometry screening. Cytometry Part A 2009, 75A: 789–797. 10.1002/cyto.a.20754
 6.
R Development Core Team: R: A language and environment for statistical computing.R Foundation for Statistical Computing, Vienna, Austria; [http://www.Rproject.org] ISBN 3900051070 2009
 7.
Gentleman R, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L, Yang YH, Zhang J: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology 2004, 5: R80. 10.1186/gb2004510r80
 8.
Adler D, Murdoch D: rgl 0.71. 3D visualization device system (OpenGL). R package 2008. [http://cran.rproject.org]
 9.
Feng D, Tierney L: Computing and displaying isosurfaces in R. Journal of Statistical Software 2008, 28(1):1–24.
 10.
Feng D, Tierney L: misc3d 0.6. A collection of miscellaneous 3d plots, including isosurfaces. R package 2008. [http://cran.rproject.org]
 11.
Lo K, Brinkman RR, Gottardo R: Automatic gating of flow cytometry data via robust modelbased clustering. Cytometry Part A 2008, 73A: 321–332. 10.1002/cyto.a.20531
 12.
Finak G, Bashashati A, Brinkman RR, Gottardo R: Merging mixture components for cell population identification in flow cytometry. Advances in Bioinformatics 2009, in press. (doi:10.1155/2009/247646). (doi:10.1155/2009/247646).
 13.
Roederer M, Hardy RR: Frequency difference gating: a multivariate method for identifying subsets that differ between samples. Cytometry 2001, 45: 56–64. 10.1002/10970320(20010901)45:1<56::AIDCYTO1144>3.0.CO;29
 14.
Ellis B, Gentleman R, Hahne F, Le Meur N, Sarkar D: flowViz 1.8.0 Visualization for flow cytometry. Bioconductor package 2009. [http://www.bioconductor.org]
 15.
Sarkar D, Le Meur N, Gentleman R:Using flowViz to visualize flow cytometry data. Bioinformatics 2008, 24: 878–879. 10.1093/bioinformatics/btn021
 16.
Duong T, Cowling A, Koch I, Wand MP: Feature significance for multivariate kernel density estimation. Computational Statistics and Data Analysis 2008, 52: 4225–4242. 10.1016/j.csda.2008.02.035
 17.
Duong T, Hazelton ML: Plugin bandwidth matrices for bivariate kernel density estimation. Journal of Nonparametric Statistics 2003, 15: 17–30. 10.1080/10485250306039
 18.
Wand MP, Jones MC: Comparison of smoothing parameterizations in bivariate density estimation. Journal of the American Statistical Association 1993, 88: 520–528. 10.2307/2290332
 19.
Duong T, Wand MP: feature 1.2.0. Feature significance for multivariate kernel density estimation. R package 2009. [http://cran.rproject.org]
 20.
Wand MP: Fast computation of multivariate kernel estimators. Journal of Computational and Graphical Statistics 1994, 3: 433–445. 10.2307/1390904
 21.
Wand MP, Jones MC: Multivariate plugin bandwidth selection. Computational Statistics 1994, 9: 97–116.
 22.
Duong T: ks 1.5.10. Kernel density estimators and kernel discriminant analysis for multivariate data. R package 2009. [http://cran.rproject.org]
 23.
Hyndman RJ: Computing and graphing highest density regions. The American Statistician 1996, 50: 120–126. 10.2307/2684423
 24.
Ellis B, Haaland P, Hahne F, Le Meur F, Gopalakrishnan N: flowCore 1.10.0. Basic structures for flow cytometry data. Bioconductor package 2009. [http://www.bioconductor.org]
 25.
Grasman R, Gramacy RB: geometry 0.1–2. Mesh generation and surface tesselation. R package 2008. [http://cran.rproject.org]
 26.
Chacón JE, Duong T, Wand MP: Asymptotics for general multivariate kernel density derivative estimators. Statistica Sinica 2010, in press.
 27.
Terrell GR: The maximal smoothing principle in density estimation. Journal of the American Statistical Association 1990, 85: 470–477. 10.2307/2289786
 28.
Samworth RJ, Wand MP: Asymptotics and optimal bandwidth selection for highest density region estimation. The Annals of Statistics 2010, in press.
 29.
Gottardo R, Lo K: flowClust 2.4.0. Clustering for flow cytometry. Bioconductor package 2009. [http://www.bioconductor.org]
 30.
Lo K, Hahne F, Brinkman RR, Gottardo R: flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 2009, 10: 145. 10.1186/1471210510145
Acknowledgements
This research was supported by Australian Research Council Discovery Project DP0556518. We are grateful to Florian Hahne for facilitating the incorporation of curvHDR into the flowCore package, Dai Feng and Luke Tierney for assistance with aspects of the misc3d package and to Jianfei Liu and José Maisog for assistance with the computational geometry aspects of largescale point containment testing for general polyhedra. We thank three referees for their reports, leading to improvement of this article.
Author information
Additional information
Authors' contributions
UN contributed to development of the methodology and programmed the univariate and bivariate versions of the algorithm. GL contributed to development of the methodology and programmed the trivariate version of the algorithm. MPW contributed to development of the methodology, coordinated the study and finalized the manuscript. All authors read and approved the final manuscript.
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.
Rights and permissions
About this article
Cite this article
Naumann, U., Luta, G. & Wand, M.P. The curvHDR method for gating flow cytometry samples. BMC Bioinformatics 11, 44 (2010) doi:10.1186/147121051144
Received:
Accepted:
Published:
Keywords
 Kernel Density Estimate
 High Density Region
 Flow Cytometric Data
 Bandwidth Matrix
 High Curvature Region