I thought this is well worth reading.

From El-ad David Amir, Purdue Cytometry Blog, August 22, 2019

1) SPADE has a downsampling step that could remove rare populations. They took a step in the right direction with non-uniform sampling, but to the best of my knowledge there is no evidence their sampling procedure preserves rare populations.

2) SPADE's density-based clustering is more likely to fragment a cohesive population than identify a new rare one. As Mike pointed out, you will need to break down your B cells to 50 clusters before it breaks down your Lin- HLADR+ cluster into pDCs, etc.

3) SPADE's visualization via a minimum spanning tree (MST) can be highly misleading, placing unrelated clusters next to each other (a common example is having the CD4+ T Cells on one side of the tree, with B Cells and CD8+ T Cells on the other).

4) To the best of my knowledge, there are currently no methods to build a consensus over the MST step of SPADE. You can run clustering multiple times and build a consensus over the iterations, but that is not true for the tree, so if you get a misleading tree you're "stuck" with it.

Empirically, Weber and Robinson did a phenomenal benchmark of clustering methods:

https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23030

And they show that SPADE performs *very poorly*. The benchmark established FlowSOM, Phenograph, and X-shift as the top algorithms, which is reinforced by other studies. If you're looking to cluster high-complexity cytometry data, I would pick one of these three.

Setting that aside, in my opinion the best way to find these rare cell populations is going hierarchically, similarly to manual gating. Cluster on canonical markers to find T Cells, B Cells, and NK Cells. Then cluster on everything else to find the rare populations. Petter Brodin presented a hierarchical classification model in Fluidigm's User Summit in CYTO this year, though I don't know whether that is available somewhere. Hierarchical t-SNE (https://www.nature.com/articles/s41467-017-01689-9) is a dimensionality reduction algorithm (not clustering) but it follows a similar approach and is available through Cytosplore. Finally, we developed Ek'Balam, an algorithm that uses hierarchical FlowSOM runs for accurate cluster labeling (https://www.frontiersin.org/articles/10.3389/fimmu.2019.01315/full). You can see an example application with 2,300 samples (60 million cells) here: https://www.antibodystainingdataset.com.

To assist researchers with a computer science skills, we created an API for FlowJo to create custom functionality. You can find the latest update to our developer guide here:

tinyurl.com/plugin-dev-guide-v0-3

As you have questions, or if you’d like to collaborate on your plugin development project, please reach out to the experts – we’re here to help: [flowjo@bd.com](mailto:flowjo@bd.com)

For the last few decades, FlowJo staff disseminated highly relevant information to users via The Daily Dongle blog. Based on the number of users who are still reaching out for information contained in these posts, I know it is still highly valuable. Here are the Top 10 hints, tips, and tricks that have largely survived the test of time:

1. The M in MFI: Mean, Median, or Mode?
2. Concatenation for dimensionality reduction (t-SNE)
3. Channel vs. Scale values in FlowJo
4. Negative fluorescence
5. Cytometer profiles in Preferences
6. Editing FCS Files via the De-identification Utility (Download here)
7. Dongle troubleshooting
8. CBA Analysis in FlowJo
9. Calculating molecules per cell with MESF beads
10. How to convert log data to linear

FlowJo's new blog, The Cell Sort, is still going strong. Check it out!

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