Date: Thursday, March 13, 2008
Location: Oregon Health & Science University, Portland, OR, USA
Flow Informatics and Computational Cytometry Society (FICCS) connects people sharing interest in new software tools, methods, and standards for flow cytometry. Members of the society represent a cross-disciplinary international collaborative group of bioinformaticists, computational statisticians, software and hardware developers and clinician scientists, from both academia and industry.
and Presentations Download
Thursday, March 13, 2008 – OHSU, Portland, OR, USA
|Ryan Brinkman, firstname.lastname@example.org, http://www.terryfoxlab.ca|
An Analytical Workflow for Investigating Cytokine Profiles
|[download]||Janet Siebert, email@example.com, http://www.cytoanalytics.com|
|Understanding cytokine profiles of disease states has provided researchers with insight into immunologic signaling associated with disease onset and progression, affording opportunities for advancement in diagnostics and therapeutic intervention. Multiparameter flow cytometric assays and bead-based assays provide simultaneous measurement of large numbers of cytokines. These technologies demand appropriate analytical techniques to extract relevant information efficiently. Ms. Siebert will present a data analysis process which reveals significant alterations in T-cell cytokine expression patterns in type 1 diabetes and breast cancer. The process consists of population-level analysis, followed by donor-level analysis, data transformation such as stratification or normalization, and a return to population-level analysis.|
LabKey Flow – Establishing a New Standard for Securely Managing and Distributing Flow Cytometry Data to Investigators
|[download]||Kevin Banks, firstname.lastname@example.org, http://www.labkey.com|
|Flow cytometry core facilities typically provide a breadth of instrumentation and expertise to university researchers, their collaborators and a small number of outside commercial organizations. Today, the majority of cytometry core labs aggregate the data they collect for their customers onto centrally managed FTP servers through a series of time-intensive manual steps. Investigators then retrieve data from the server, provided they are authenticated users. Data backup is usually provided via the exchange of physical media, a process that can lead to errors in copying or transfer.
LabKey Flow enables cytometry core labs to implement a new standard operating procedure that makes managing and distributing data to their customers easier, more secure and more reliable for both investigators and core lab staff. Operators save newly generated data from their cytometers into LabKey Flow’s relational database. Data goes into a “release queue” so that the core lab can rapidly assess the quality of the data prior to distributing it to investigators. This simple but important quality control step helps each lab build and maintain its reputations for quality, an essential attribute for any service-focused core lab.
LabKey Flow does not just help core labs distribute flow cytometer data to their customers. It also provides investigators with a central repository for management of all flow projects, including both raw experimental data and analyzed results. By establishing a secure, shared repository for all their data, investigators eliminate the pain of searching for data hidden away on student desktop systems or, even worse, having to re-run experiments because of data lost during student departures. Investigators can even use LabKey’s web-based interface to share their projects with remotely located collaborators, or to provide full access to the system conveniently from home offices. The ability to save analytical results from applications like FlowJo back into LabKey Flow provides tremendous additional benefits in efficiency and reliability. With LabKey Flow, you simply log into your project file and all the raw experimental data and analyzed results are available for your review – raw data, graphics, tables and charts are all at your fingertips. Investigators can also take advantage of LabKey Flow’s query language, which makes it easy to query across multiple flow data sets or projects. They can even run sophisticated analysis scripts using LabKey’s built-in support for R, import/export data between applications like Microsoft Excel, and interface with their own analytical algorithms. Users of LabKey Flow can make full use of the best tools that are out there today.
This presentation will describe the LabKey system in further detail and provide a hands-on demonstration of its capabilities.
Analysis of High Throughput Flow Cytometry Data using plateCore
|[download]||Errol Strain, Errol_Strain@bd.com, http://www.bd.com|
|plateCore is a Bioconductor packaged created to make processing and analysis of large, complex flow datasets in R easier. High throughput flow studies are often run
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in a 96 or 384-well plate format, with a number of different samples, controls, and antibodies-dye conjugates present on the plate. Analyzing the output from the cytometer requires keeping track of the contents of each well, matching sample wells with control wells, gating each well/channel separately, making the appropriate plots, and summarizing the results. plateCore extends the flowCore and flowViz packages to work on flowPlate objects that represent these large flow datasets. For those familiar with flowCore and flowViz, the gating (filtering), transformation, and other data manipulations for flowPlates are very similar to flowSets.
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study has unstimulated wells that serve as the negative control. In both analyses the goal will be to identify cells that are positively stained relative to the corresponding negative control.
The Bioconductor package flowCore, a shared development platform for flow cytometry data analysis in R
|[download]||Florian Hahne, email@example.com, http://www.fhcrc.org|
|Traditionally, flow cytometry (FCM) has been a tube-based technique limited to small-scale laboratory studies. High throughput methods have recently been developed and are now used in both basic and clinical research. The large amount of information generated by high throughput flow cytometry experiments must be stored, managed, and easily summarized in order to make it accessible to the researcher. The Open Source statistical programming language R in conjunction with the Bioconductor Project can play an important role in this new paradigm by providing a unified research platform which biologists, bioinformaticians, computer scientists, and statisticians can use to analyze data and develop standard or novel methods. Here we present the newly developed Bioconductor package “flowCore” which provides structures to represent flow cytometry data in R. In addition, “flowCore” and a number of associated Bioconductor packages offer functionality to import, pre-process, visualize and assess data from FCM experiments. We will give brief examples of this functionality.|
Automated Gating of Flow Cytometry Data via Robust Model-based Clustering
|[download]||Raphael Gottardo, firstname.lastname@example.org, http://www.stat.ubc.ca/~raph/|
|The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment.
However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full potential.
We propose a flexible statistical model-based clustering approach for identifying cell populations in flow cytometry data based on t mixture
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models with a Box-Cox transformation. This approach generalizes the popular Gaussian mixture models to account for outliers and allow for non- elliptical clusters. We describe an Expectation- Maximization (EM) algorithm to simultaneously handle parameter estimation and transformation selection.
Software development for large-scale flow cytometry data analysis
|[Download id not defined]||Peter Krutzik, email@example.com, http://www.stanford.edu|
|With recent advances in flow cytometry equipment and reagents, researchers are performing larger, more complex datasets than ever before. In particular, analysis of intracellular signaling events is becoming more common, and requires a more quantitative approach to flow cytometric data analysis. Therefore, we have been developing two software packages, Cytobank and Webflow, which provide unique solutions to the problems of data management, data sharing, plate-based annotations, quantitative analysis, heat map generation, and rapid exploratory data analysis. Webflow is tailored more towards rapid analysis of high throughput drug screening experiments, while Cytobank is a more powerful data management system and generates publication-quality figures with control over all aspects of data analysis for both analog and digital data. Both programs are web-based, running on central servers with enough computing power for even the most complex experiment.|
ISAC’s Data Standards Task Force New Flow Cytometry Data Standards Efforts
|[Download id not defined]||James Wood, firstname.lastname@example.org, http://www.isac.net|
|Jim will present an overview of recent developments from ISAC’s Data Standards Task Force in the development of standards for flow cytometry including the Analytical Cytometry Standard (ACS), GatingML, MIFlowCyt and FCS.|
Data Standards Workshop
|Open workshop and attendee-led discussion on opportunities and implications of the proposed flow cytometry data standards for scientists, vendors and third party software developers|
|4:30 – 5:30 pm||
Panel Discussion on Minimum Information Standards
|4:30 – 7:30 pm||
Hors d’oeuvres, poster viewing, and vendor demos
|6:30 – 9:30 pm||
Dinner and Talks
|Amnis-sponsored dinner and talks at the Marriott Riverplace, including Leigh Samsel, National Heart, Lung, and Blood Institute. (The aerial tram connects OHSU and the riverfront.) Registration for this meeting is at http://www.regonline.com/IIGPortland – a separate Amnis-site registration is required.|