Comments (8)
Hi, Fangge,
Thanks for trying STREAM.
Would you mind sharing with us the data matrix ? We are happy to take a look.
Best,
Huidong
from stream.
from stream.
@fangge-Li Sure!
from stream.
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
from stream.
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.
from stream.
Hi Huidong,
Thanks for the quick response. I'll give it a try and let you know how it goes.
Thanks,
Chris
from stream.
Hi Huidong,
Thanks for the help. The fix worked nicely.
Best,
Chris
from stream.
That’s great! Feel free to let me know if you have any questions.
from stream.
Related Issues (20)
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