May 15, 2013  |  Education

Studying single-cell signaling networks with mass cytometry: A perspective from Professor Bernd Bodenmiller

Recently, Bernd Bodenmiller from the University of Zurich gave a webcast for on using mass cytometry to study single cell signaling networks in biology and disease. The talk consisted of an introduction, an overview of the Fluidigm CyTOF instrument, a summary of Bernd’s recent paper in Nature Biotechnology, and his current work with collaborators in ovarian cancer. If you missed it, the talk is still hosted on Nature’s webcast page and is also summarized for you below.

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March 29, 2013  |  Education, Uncategorized

Data Management Discussion Panel

As you’ve probably heard from us before, the era of Big Data is here and we want to make sure you’re prepared to face the unique challenges it brings. However, we at Cytobank aren’t the only ones thinking about the implications of managing all the data being generated by high dimensional flow and mass cytometry; +Ryan Duggan via the Cytometry Google community recently hosted a Cytometry Hangout on the topic of Data Management and had a guest panel consisting of our very own Nikesh Kotecha, Kevin Krouse from, and Wade Rogers from the University of Pennsylvania. If you didn’t happen to catch it live, below is a quick summary of the panel.

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March 29, 2013  |  Education

New Fluorescence and Mass Cytometry Protocols

One of our goals at Cytobank is to build community resources that facilitate cytometry experiments. This time around, we’ve updated a couple of our Resources with protocol sheets. These protocol sheets contain experimental steps linked to analysis data that you work with yourself in Cytobank. If you’re just venturing out into the realm of mass cytometry, or phospho-flow using mass or fluorescence cytometry, these resources can guide you along the way.
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February 28, 2013  |  Education, Uncategorized

My First CyTOF Experiment

Having recently joined Cytobank and with little practical experience with the CyTOF, I headed over to the Nolan lab to do my very first CyTOF experiment. Every year, the Nolan lab hosts a phospho-flow course where a group of interested researchers fly to Stanford to learn how to perform a phospho-flow experiment. During my day there, I used a protocol in development for this course to generate data on the CyTOF and subsequently used  Premium Cytobank for analysis. This is a record of my experiences that will hopefully be helpful to those of you just starting out in the world of mass cytometry as well.

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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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