Comments (4)
Ok I will try it now
from higashi.
Hi,
Thanks for your interest. It would be possible, especially when the coverage for each cell is relatively higher. For a dataset with smaller number of cells, It would also be easier to define subclusters based on one chromosome or a part of the chromosome of interest instead of whole genome. But of course, it depends on the composition of the cell population as well.
from higashi.
Great. If you don't mind, could you wait for a day or so? I'm updating the code base with a major speed improvement today, which I believe would enhance your experiences with Higashi a lot. 😄
from higashi.
Sounds good! I will just try using scHiCluster for today then.
from higashi.
Related Issues (20)
- question about cell order HOT 5
- Problem solved
- Error running Ramani data HOT 2
- higashi.process_data() won't finish HOT 20
- higashi.Higashi_backend.Modules import error HOT 5
- error when running scTAD.py HOT 1
- Error running simulated data
- The main_cell.py is so slow HOT 5
- Problem running Higashi on Ramani et al. HOT 5
- What are the configure options mean?
- Stop with OSError when run "higashi_model.train_for_imputation_nbr_0()" HOT 3
- Error in fh_model.prep_dataset() "Pack from sparse mtx to tensors" HOT 2
- ERROE when run process.py: no config file HOT 1
- Predicting structures from embedding vector HOT 2
- wrapper.fast_process_data() - method does not exist HOT 2
- ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (15361,) + inhomogeneous part. HOT 3
- RuntimeError: received 0 items of ancdata
- Higashi stuck on training at higashi_model.train_for_imputation_nbr_0() on SLURM system HOT 7
- ValueError: setting an array element with a sequence. HOT 1
- RuntimeError: CUDA out of memory.
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from higashi.