Data analysis in flow cytometry: the future just started

Leukemia prediction using sparse logistic regression

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.

GenePattern flow cytometry suite

BACKGROUND

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.

RESULTS

In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.

CONCLUSIONS

GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

An introduction to mass cytometry: fundamentals and applications

Mass cytometry addresses the analytical challenges of polychromatic flow cytometry by using metal atoms as tags rather than fluorophores and atomic mass spectrometry as the detector rather than photon optics. The many available enriched stable isotopes of the transition elements can provide up to 100 distinguishable reporting tags, which can be measured simultaneously because of the essential independence of detection provided by the mass spectrometer. We discuss the adaptation of traditional inductively coupled plasma mass spectrometry to cytometry applications. We focus on the generation of cytometry-compatible data and on approaches to unsupervised multivariate clustering analysis. Finally, we provide a high-level review of some recent benchmark reports that highlight the potential for massively multi-parameter mass cytometry.

Multiparameter flow cytometry: advances in high resolution analysis

Over the past 40 years, flow cytometry has emerged as a leading, application-rich technology that supports high-resolution characterization of individual cells which function in complex cellular networks such as the immune system. This brief overview highlights advances in multiparameter flow cytometric technologies and reagent applications for characterization and functional analysis of cells modulating the immune network. These advances significantly support high-throughput and high-content analyses and enable an integrated understanding of the cellular and molecular interactions that underlie complex biological systems.

Isolation and expansion of endothelial progenitor cells derived from mouse embryonic stem cells

The unlimited differentiation and proliferation capacity of embryonic stem cells represents a great resource for regenerative medicine. Here, we describe a method for differentiating, isolating, and expanding endothelial cells (ECs) from mouse embryonic stem cells (mESCs). First, mESCs are expanded on a mouse embryonic fibroblast (mEF) feeder layer and partially differentiated into embryoid bodies (EBs) by growing the cells in an ultra-low attachment plate for up to 5 days. The EBs are then differentiated along the endothelial lineage using endothelial growth medium supplemented with 40 ng/mL vascular endothelial growth factor (VEGF). The differentiated endothelial population expresses both Fetal Liver Kinase 1 (Flk-1) and VE-Cadherin on the cell surface which can be further purified using a fluorescence-activated cell sorting (FACS) system and subsequently expanded on 0.1 % gelatin-coated plates. The differentiated cells can be analyzed by real-time PCR and flow cytometry to confirm enrichment of EC-specific genes and proteins.

Critical assessment of automated flow cytometry data analysis techniques

In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges.

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Computational analysis of high-dimensional flow cytometric data for diagnosis and discovery

Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.

Evaluation of the reactive T-cell infiltrate in uveitis and intraocular lymphoma with flow cytometry of vitreous fluid (an American Ophthalmological Society thesis)

PURPOSE

To describe the reactive T-cell infiltrate in uveitis and intraocular lymphoma using flow cytometry of clinical intraocular specimens acquired during diagnostic pars plana vitrectomy.

METHODS

This was a retrospective review of diagnostic vitreous specimens (1992-2011) obtained at a university-based, tertiary care center. Seventy-eight patients with uveitis or lymphoma undergoing pars plana vitrectomy were selected for intraocular testing based on clinical diagnostic uncertainty. Pars plana vitrectomy with flow cytometry, gene rearrangement studies, and cytology was performed.

RESULTS

T-cell infiltrates were found in all diagnostic categories with limited power to discriminate between uveitis and T-lymphocyte reactive infiltrates in response to intraocular lymphoma. Statistically significant differences by two-sample test of means between group means were found between 35 uveitis and 35 B-cell lymphoma cases for T-cell markers CD2, 3, 4, 5, and 7, but not for CD8. The CD4:CD8 ratio had a higher mean value in the uveitis group (P=.0113), and 8 T-cell lymphomas had a statistically greater number of CD3+ lymphocytes compared to uveitis (P=.0199) by two-sample test of means. Likelihood ratios were highest for CD2, CD5, CD7, CD4:CD8 ratio, CD20, and CD22.

CONCLUSIONS

Discrimination between uveitis and lymphoma based on cell identification by flow cytometry was limited because of the prevalence of T lymphocytes in all diagnostic categories, emphasizing the importance of a reactive T-cell infiltrate in B-cell lymphomas, which may impede diagnosis. Flow cytometry may allow identification of more cases of T-cell lymphoma than reported when it is combined with gene rearrangement and cytology.

Intraocular lymphoma: a clinical perspective

Primary vitreoretinal lymphoma (PVRL) is a rare malignancy that is speculated to arise extraocularly, and preferentially invade and flourish in the ocular and CNS microenvironments. The eye is involved in about 20% of primary central nervous system lymphomas, but the brain is eventually involved in about 80% of PVRL. Most are B-cell lymphomas with small numbers of T-cell lymphomas metastatic to the vitreous and retina. Metastatic systemic B-cell lymphoma usually involves choroid. Primary choroidal lymphoma is rare. Intraocular lymphoma can usually be distinguished from uveitis clinically, although there are overlaps, which may be pronounced in eyes with a large component of reactive inflammation related to tumor surveillance and control. There are controversies in diagnosis and treatment. Diagnosis through examination of ocular fluid is technically difficult and can utilize cytology, immunohistochemistry, flow cytometry, molecular detection of gene rearrangements, and cytokine profiling. Treatment of intraocular lymphoma without detectable CNS disease could consist of a full course of systemic chemotherapy with ocular adjunctive treatment, or ocular treatment alone depending on the preference of the clinical center. In ocular only cases where the vitreous has been debulked to improve vision and there is no sight-threatening involvement of the RPE, orbital irradiation or intravitreal chemotherapy stabilizes the intraocular process but does not seem to modify the CNS component, which can present symptomatically in an advanced state. This is a highly malignant disease with a poor prognosis. Close collaboration with a pathologist and oncologist, and good communication with patients is essential.

EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols

The EU-supported EuroFlow Consortium aimed at innovation and standardization of immunophenotyping for diagnosis and classification of hematological malignancies by introducing 8-color flow cytometry with fully standardized laboratory procedures and antibody panels in order to achieve maximally comparable results among different laboratories. This required the selection of optimal combinations of compatible fluorochromes and the design and evaluation of adequate standard operating procedures (SOPs) for instrument setup, fluorescence compensation and sample preparation. Additionally, we developed software tools for the evaluation of individual antibody reagents and antibody panels. Each section describes what has been evaluated experimentally versus adopted based on existing data and experience. Multicentric evaluation demonstrated high levels of reproducibility based on strict implementation of the EuroFlow SOPs and antibody panels. Overall, the 6 years of extensive collaborative experiments and the analysis of hundreds of cell samples of patients and healthy controls in the EuroFlow centers have provided for the first time laboratory protocols and software tools for fully standardized 8-color flow cytometric immunophenotyping of normal and malignant leukocytes in bone marrow and blood; this has yielded highly comparable data sets, which can be integrated in a single database.

Toward new insights on the white blood cell differential by flow cytometry: a proof of concept study on the sepsis model

BACKGROUND

We sought to evaluate, on a model of sepsis, the clinical relevance of new parameters obtained on a white blood cell (WBC) differential by flow cytometry, implemented in the routine workflow of our hematology laboratory.

METHODS

A WBC with differential by flow cytometry was done on 459 patients at admission in intensive care unit. They were retrospectively categorized in having (i) infection or not or (ii) a high gravity score (severe sepsis or septic shock) or not. We analyzed by hierarchical clustering, in a multidimensional manner, 50 parameters provided by the flow cytometric platform in place of the standard seven parameters for a standard differential.

RESULTS

Our approach allows to discriminate on the basis of a WBC differential (i) infected patients at admission based on a 16 parameter signature, with a concordance rate of 72.7% and a specificity of 79.9% and (ii) patients with high gravity score (septic shock or severe sepsis) at admission with a signature of eight parameters, with a concordance rate of 74.7% and a specificity of 75.9%.

CONCLUSIONS

This study shows the clinical relevance of an extended WBC differential to obtain by a flow cytometer integrated into a routine hematology laboratory workflow. Development of such approach implicates the redefinition of the WBC differential by integrating new parameters.

A computational framework to emulate the human perspective in flow cytometric data analysis

BACKGROUND

In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation.

RESULTS

To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods.

CONCLUSIONS

The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics.

Multivariate analysis of flow cytometric data using decision trees

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry

Background

When flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.

Results

We designed a hierarchical algorithm, flowMatch, to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. We applied the algorithm on flow cytometry data obtained from human blood cells before and after stimulation with anti-CD3 monoclonal antibody, which is reported to change phosphorylation responses of memory and naive T cells. The flowMatch algorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation.

Conclusions

We concisely represent each class of samples by a template consisting of a collection of meta-clusters (representative abstract populations). Using flowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.

Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays

MOTIVATION

Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.

RESULTS

To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis).

AVAILABILITY

The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

rbrinkman@bccrc.ca.

Polychromatic flow cytometry in the clinical laboratory

Technological advances in flow cytometry include increasingly sophisticated instruments and an expanding range of fluorochromes. These advances are making it possible to detect an increasing number of markers on a single cell. The term polychromatic flow cytometry applies to such systems that detect five or more markers simultaneously. This review provides an overview of the current and future impact of polychromatic flow cytometry in the clinical laboratory. The use of multiple markers has several advantages in the diagnosis and monitoring of haematological malignancies. Cell populations can be analysed more comprehensively and efficiently, and abnormal populations can be distinguished more readily when normal counterparts are present. Polychromatic flow cytometry is particularly useful in the evaluation of plasma cells, and the role of flow cytometry in the assessment of plasma cell disorders is reviewed in depth. There is improved sensitivity in the assessment of small populations, which is critical in the evaluation of minimal residual disease. Flow cytometry can also play a role in assessment of circulating tumour cells in carcinoma. Introduction of polychromatic flow cytometry is a complex process with many challenges including design of antibody panels and instrument compensation. Developments in data analysis are required to realise the full benefits of the other technical advances. Standardisation of protocols may reduce inter-laboratory variation. While the complexity of polychromatic flow cytometry creates challenges, it has substantial potential to improve clinical analysis.

Modulation of the cellular and humoral immune response to pediatric open heart surgery by methylprednisolone

BACKGROUND

With the intention to reduce overshooting immune response, glucocorticoids are frequently administered perioperatively in children undergoing open heart surgery. In a retrospective study we investigated extensively the modulation of the humoral and cellular immune response by methylprednisolone (MP).

METHODS

This study was carried out on blood samples from two groups of children who had undergone surgical correction of atrial or ventricular septal defects, either without (MP⁻, n = 10), or with MP administration (MP+, n = 23, dose median 11 (IQR 10-16) mg kg⁻¹ body weight) before cardiopulmonary bypass (CPB, duration median 42 (IQR 36-65) min). EDTA blood was obtained 24 h preoperatively, after anesthesia, at CPB begin and end, 4, 24, and 48 h after surgery, at discharge and at out-patient follow-up (median 8.2 (IQR 3.3-12.2) months after surgery). Complex blood analysis including clinical chemistry and flow cytometry were performed to monitor humoral immune response, differential blood count, lymphocyte subsets, and the degree of activation of various leukocyte subpopulations.

RESULTS

The patients' postoperative courses and follow-up were uneventful. Release of IL-6 and IL8 was reduced and that of the anti-inflammatory cytokine IL-10 upregulated by MP. Significant increase of circulating neutrophils and monocytes as inflammatory reaction to surgery and CPB contact was detected in both groups. However, invasion of monocytes to the periphery was delayed with MP. CD4+ and CD8+ T-lymphocyte counts were lower with MP treatment. B-lymphocyte count increased significantly after surgery in MP+ but remained constant in MP⁻ group.

CONCLUSIONS

MP treatment partially decreased the pro-inflammatory effect of CPB surgery and induced anti-inflammatory effect on the cellular and humoral level.

SPICE: exploration and analysis of post-cytometric complex multivariate datasets

Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes.

Rapid cell population identification in flow cytometry data

We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.

Introduction A: recent advances in cytometry instrumentation, probes, and methods--review

Cytometric techniques are continually being improved, refined, and adapted to new applications. This chapter briefly outlines recent advances in the field of cytometry with the main focus on new instrumentations in flow and image cytometry as well as new probes suitable for multiparametric analyses. There is a remarkable trend for miniaturizing cytometers, developing label-free and fluorescence-free analytical approaches, and designing "intelligent" probes. Furthermore, new methods for analyzing complex data for extracting relevant information are reviewed.

Quantitative measurement of GLUT4 translocation to the plasma membrane by flow cytometry

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces glucose uptake by insulin-sensitive tissues, most notably the brain, skeletal muscle, and adipocytes. Patients suffering from type-2 diabetes and/or obesity often develop insulin resistance and are unable to control their glucose homeostasis. New insights into the mechanisms of insulin resistance may provide new treatment strategies for type-2 diabetes.

The GLUT family of glucose transporters consists of thirteen members distributed on different tissues throughout the body¹. Glucose transporter type 4 (GLUT4) is the major transporter that mediates glucose uptake by insulin sensitive tissues, such as the skeletal muscle. Upon binding of insulin to its receptor, vesicles containing GLUT4 translocate from the cytoplasm to the plasma membrane, inducing glucose uptake. Reduced GLUT4 translocation is one of the causes of insulin resistance in type-2 diabetes^2,3.

The translocation of GLUT4 from the cytoplasm to the plasma membrane can be visualized by immunocytochemistry, using fluorophore-conjugated GLUT4-specific antibodies.

Here, we describe a technique to quantify total amounts of GLUT4 translocation to the plasma membrane of cells during a chosen duration, using flow cytometry. This protocol is rapid (less than 4 hours, including incubation with insulin) and allows the analysis of as few as 3,000 cells or as many as 1 million cells per condition in a single experiment. It relies on anti-GLUT4 antibodies directed to an external epitope of the transporter that bind to it as soon as it is exposed to the extracellular medium after translocation to the plasma membrane.

Data reduction for spectral clustering to analyze high throughput flow cytometry data

Background

Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.

Results

We tested our algorithm on flow cytometry data as an example of large, multidimensional data containing potentially hundreds of thousands of data points (i.e., "events" in flow cytometry, typically corresponding to cells). Compared to two state of the art model-based flow cytometry clustering methods, SamSPECTRAL demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations.

Conclusions

This work is the first successful attempt to apply spectral methodology on flow cytometry data. An implementation of our algorithm as an R package is freely available through BioConductor.