Testing challengesĭespite its commercial status, FlowJo was one headache after another, starting with getting the SPADE implementation working. SPADE within Cytobank and FCS Express are integrated with the general user interface, and require no plugins or separate installations.īefore we talk results, let’s start by covering certain challenges regarding some of the aforementioned software and how they factored into the speed testing process. Plus, Peng invites you to contact him for help if your data isn’t recognized by his software, which might be worth a try considering that SPADE3 is surprisingly attractive and easy to use – albeit with limited functionality compared to paid-for, full-suite cytometry data analysis packages.Ĭommercial flow cytometry software is naturally held to a higher standard, especially if it touts the ability to run SPADE out of the box on your files. Hiccups like these are to be expected of non-commercial software, and in any case, it’s hard to argue with free software of this caliber. However, he notes outright that it cannot necessarily handle the plethora of different formats produced by all available cytometers. Peng Qiu has generously made his original program, SPADE3, freely available to the flow community. Of the five software packages we tested (Cytobank, FCS Express, FlowJo, R, and the original, free software made available by the author of SPADE), only Cytobank and FCS Express were able to reliably return results from various FCS files – which is to say, they reliably returned some results while the others returned nothing at all! It was assumed this would be easy enough to do – we were wrong. Having already argued for the need for speed, and given the growing popularity of these kinds of algorithms for dealing with complex datasets, we figured it would be of interest to the flow cytometry community to undertake a similar test across popular software packages. įigure 1 SPADE trees with 100 clusters colored on the same parameter (TCR-beta) in five different software. If you’d like to read more on the theory and development of the SPADE algorithm, see the original literature. 1).Īs with a phylogenetic tree, similar clusters are grouped closer together, and dissimilar clusters are located more distally on the tree. The result is quite different from the two-dimensional plot of tSNE, and rather resembles a phylogenetic tree in its branching structure (Fig. Unlike tSNE, which is a dimensionality-reduction algorithm that presents a multidimensional dataset in 2 dimensions (tSNE-1 and tSNE-2), SPADE is a clustering and graph-layout algorithm. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format. A favored algorithm in the flow cytometry community, SPADE is used when dealing with highly multidimensional or otherwise complex datasets. As a follow-up to our post on tSNE where we compared the speed of calculation in leading software packages, let’s consider the case of SPADE ( Spanning-tree Progression Analysis of Density-normalized Events).
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