program quits when the matrix is big about stream HOT 8 CLOSED

Hi, Fangge,

Thanks for trying STREAM.
Would you mind sharing with us the data matrix ? We are happy to take a look.

Best,
Huidong

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from stream.

@fangge-Li Sure!

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Hi,

I was wondering if there was any further update to this issue. I'm running into a similar situation where st.dimension_reduction(data) keeps quiting due to memory constraints (specifically I'm getting feedback that the process has been "killed" after about 2-3 hours of working). I'm working with a large dataset as well, 13230 cells with 10622 genes present (Drop-seq dataset). I'm doing this on a cluster allocating 30 cores and 128G of memory. Any help would be appreciated.

Thanks,
Chris

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Hi Chris @cstill1992 ,

Sorry about the issue. st.dimension_reduction(data) uses 'MLLE' as the default dimension reduction method, which currently is not efficiently implemented yet.

As an alternative solution, you can try

st.dimension_reduction(adata,method='umap', n_components=2)
st.seed_elastic_principal_graph(adata ,clustering='kmeans')

Of course you can also increase the n_components to get more detailed structure.

This will speed it up significantly for large dataset.

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Hi Huidong,

Thanks for the quick response. I'll give it a try and let you know how it goes.

Thanks,
Chris

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Hi Huidong,

Thanks for the help. The fix worked nicely.

Best,
Chris

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That’s great! Feel free to let me know if you have any questions.

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