January 31, 2013  |  Education

Transparency of Data Analysis: Insights from the Institute of Medicine

The trend in flow cytometry is a push toward the development of technologies and methods that will enable researchers to mine increasingly more data from each experiment. These efforts will save time, money, and effort and likewise maximize information yield from each valuable sample. As we continue to grow and mine more data from experiments, a need emerges to ensure that analysis tools are built to support these high dimensional datasets.

The Institute of Medicine of the National Academies recently convened a committee that put forth recommendations surrounding the analysis of high dimensional datasets. The committee was convened following the publication and retraction of several large scale studies suffering from mismanagement of the analysis process.

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October 29, 2012  |  Education

The Power of Figure Dimensions: Generating Multiple Analyses

Click on any of the images in this post to enlarge them.

In Cytobank, we use the phrase Figure Dimensions to describe the parameters that define experiment files and contribute to how your plot layouts are organized [1]. Timepoints, Conditions, Channels, and Populations are all examples of Figure Dimensions. When you upload data to Cytobank, you are presented with the opportunity to annotate your Figure Dimensions — to tell Cytobank what exactly is in each file. This process goes the most quickly if you’ve done some annotation at the cytometer during collection — when you create tags within the Setup pages for the various Figure Dimensions, your files will be automatically sorted to match the keywords entered during collection. (You can still tag files with their descriptors even if you haven’t annotated at the cytometer.) You can learn more about annotating your files on our support site.
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September 24, 2012  |  Education

Experiment Quality Checklist for Flow Cytometry

We’ve posted previously on elements that are important to a successful flow cytometry experiment, including these three themes:

Analysis Consistency in Flow Cytometry” — How to use Cytobank functionalities to achieve consistency in gating, display, analysis iterations, and data communication

Making Beautiful Plots: Data Display Basics” — Choosing appropriate plot types, labeling, compensation, and how to properly set scale settings in flow cytometry experiments

Future Proofing Your Experiments and Files: The Importance of Annotation” — An article detailing the importance and power of annotating your datasets and ensuring annotations remained linked to the raw data

This time around, we’ll delve into another round of issues to consider when designing and running a flow cytometry experiment. These themes have emerged out of our personal benchwork experience, our experiences assisting Cytobank users with their analyses, and insights we gained from analyzing large clinical datasets. In this post, we’ll do a brief overview, so stay tuned for future posts that expand on each of these issues.
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March 30, 2012  |  Education

Making Beautiful Plots: Data Display Basics

You’ve labored at the bench and generated data that you’re about to meticulously analyze before preparing the results of your hypothesis-testing for presentation. In this post, we’ll discuss elements that factor into making beautiful (and consistent) displays of data. View our recent post on Analysis Consistency in Flow Cytometry for a discussion of broader themes relating to analysis consistency.

To summarize what will follow in short: make sure all of your data are on scale, accurately compensated, and make sure all your plots are well-labeled.

Choosing plot types, appropriate statistics, and telling the full story

There are a number of plot types that can help you tell your story in different, visually pleasing ways when used appropriately. Among the flashier ways to display data are heatmaps, histograms, and histogram overlays. These one-dimensional representations owe their appeal largely to their ability to convey an easy-to-understand message: “This population changed in X amount in Y condition.” Where this gets tricky is if you’re trying to describe a heterogeneous population. When deciding on a plot type to use to convey your story, you’ll want to make sure you’re telling the whole story, and not omitting important information about the behavior of subsets in the course of eliminating a dimension of data display. In Cytobank, you can mouse over a heatmap square to display the underlying dot plot, which will reveal another dimension of information of your data.

Figure 1. Example of a well-labeled figure using one- and two-dimensional representations.
Excerpted from Irish JM et al (2010) PNAS, 107(29):12747-54, Figure 1B.
(Click on the image for higher resolution)

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February 29, 2012  |  Education

Analysis Consistency in Flow Cytometry

When collecting and analyzing flow cytometry data, analysis consistency and quality control are essential in ensuring the validity of data within an experiment and among experiments carried out over time.

Quality control issues arise when there is variability in how experiments are carried out at the bench. We will tackle issues relating to data acquisition in a future post. In this post, we’ll discuss analysis-related quality concerns and introduce you to several Cytobank functionalities that are geared towards addressing these.

Where do issues surrounding quality control and analysis consistency arise?

– Multi-center endeavors to collect and analyze data
– Heads of labs who want to maintain consistency in analysis and presentation as scientists flux in and out of the lab
– Companies interested in a unified analysis and presentation style
– All scientists aiming to achieve reproducibility

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August 23, 2011  |  Education, Flow Cytometry

Future Proofing Your Experiments and Files: The Importance of Annotation

Ever find yourself staring at a folder of FCS files and thinking, “Wait, now which tubes did I add PMA to, how much did I add, and which samples were these again?”

Jonathan from Cytobank/Stanford recommends what he calls “future proofing” in order to avoid this problem. He explained this approach during a CYTO 2011 Pre-Congress course in his talk titled “Flood Cytometry: Embracing Single Cell Systems Biology (and coping with large cytometry experiments).” In that talk, he outlined four easy steps that are useful for experiments of all sizes.

When collecting on the cytometer:

  1. Tag your FCS files with key experiment details (e.g. “Patient-J01 IL-2 15m”)
  2. Label the channels you are measuring (before collecting data)
  3. Make sure scales and compensations work (before collecting data)
  4. Encode clinical sample IDs (don’t use HIPAA sensitive information)
Click the image to download as a PPT slide

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April 9, 2011  |  Education, Flow Cytometry

Teaching Phospho-Flow… in France

Dataset #5002: Timecourse LACI 2011

This January, Jonathan and Chris from Cytobank traveled to Marseilles, France to help lead a course as part of the Luminy Advanced Course in Immunology (LACI). LACI is organized as a satellite meeting to the Immunology and Metabolism meeting and organized by the Centre d’Immunlogie de Marseille-Luminy (CIML) and the European Molecular Biology Organization (EMBO).

The ‘Cell Signaling’ course at LACI was taught by local instructors Nathalie Auphan-Anezin and Pierre Grenot, both of CIML, and Jonathan and Chris. The course led course participants through staining, collection, upload, and analysis of a phospho-flow experiment. We’ve briefly described the experiment here, made a version of the dataset public along with the original course protocol, and prepared a tutorial (part 1 and part 2) to lead you through Cytobank analysis of the course data.

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February 25, 2011  |  Education, Flow Cytometry

Immunophenotyping Tumor-Infiltrating T cells

Dataset #4659: Testing Set – T Cell Immunophenotype (trimmed)

Quantifying the percentage of cells expressing a protein of interest is a frequent goal in both basic research and clinical studies. Paired with per-cell comparisons of the level of protein expression, this approach provides a powerful way to track and immunophenotype populations of cells present in a particular sample.

One widely recognized application of flow cytometric immunophenotyping is determining the percentage of CD4+ cells in a gated lymphocyte population in order to determine prognosis for an HIV patient. Other applications include measuring a series of markers in order to distinguish between different forms of leukemia.

In Cytobank, you can use the “percent in gate” statistic to measure and display the percentage of cells in a selected gate as compared to each active population in your figure. To illustrate with a simple example, let’s examine a sample dataset looking at the percentage of CD25+ cells in a CD3+ T cell population.

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