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There are 7 different common “artifacts” that may be affecting the quality of your imaging. Before digging into the details, let’s begin by defining an artifact: Essentially, it is any error introduced through sample preparation, the equipment or post-processing methods. This is an important concept to grasp because the effects can cause false positives or negatives, and they can physically distort your data. This is, of course, at odds with your mission to obtain reliable quantitative data. So what can you do to stop these artifacts? The problems can range from dirty objectives to bigger issues like light path aberrations.

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In the flow cytometry community, SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a favored algorithm for dealing with highly multidimensional or otherwise complex datasets. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format. Given the growing popularity of this kind of algorithm for dealing with complex datasets, we decided to test the SPADE algorithm in 5 software packages, including Cytobank, FCS Express, FlowJo, R, and the original, free software made available by the author of SPADE. Which was the fastest?

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