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Please be as geeky as possible. Reference, reference, reference.
Also, please note that this is a mixed bag of math-gurus and mathematically challenged, so choose your words wisely :-)



Posts: 23

Joined: Thu Oct 31, 2013 7:58 pm

Post Sat Nov 09, 2013 12:20 am


viSNE is a recently published alternative to SPADE for high-dimensional data analysis [1]. Unlike SPADE, individual cells (not clusters of cells) are displayed on a 2-dimensional map which preserves the multi-dimensional separation of the cells. In this sense, it is somewhat similar to PCA analysis. Like SPADE, viSNE allows coloration of cells by marker intensity or fold-change. The original implementation of viSNE was limited to 10,000 events per display; the new algorithm is approximately 10-fold faster and thus can support up to 100,000 events. It is available from http://www.c2b2.columbia.edu/danapeerlab/html/cyt.html.


1. Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., et al. (2013). viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature Biotechnology. doi:10.1038/nbt.2594



Posts: 6

Joined: Mon Dec 02, 2013 11:51 am

Post Mon Dec 23, 2013 1:23 pm

Re: viSNE

I was interested in knowing whether people here prefer using SPADE or viSNE for high-dimensional data analysis.
I started using mostly viSNE, but does SPADE have any major advantages that I should be aware of?
SPADE is perhaps better-known and already used by flow cytometry people, but what can you say about the advantages of each method?



Posts: 4300

Joined: Fri Nov 01, 2013 5:30 pm

Location: Stanford HIMC, CA, USA

Post Thu Apr 03, 2014 5:03 pm

Re: viSNE

The two approaches are different.

viSNE gives more reproducible "island" shapes from run to run. With SPADE, you get more variation in "tree" structure from run to run. As I understand, this is primarily due to the downsampling.

In my limited experience with viSNE, I think it *can* do a better job of finding low-frequency cell populations that might differ by only a couple markers out of 30+ clustering markers. For instance, I was better able to narrow down on pDCs vs mDCs in viSNE. In my (rather T-cell focused) panel, SPADE often lumps these together because they're low frequency and are highly-similar in 31 of the 33 markers I used in the tree (note: basophils are usually easily resolved from DCs, due to the differing expression of HLA-DR).

However, viSNE is mainly used for visualization; it's not clear to me from the paper or from playing with it how you can extract "numbers" (cell count/frequency, median marker intensities, fold-changes, etc) from it like you can using SPADE.

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