Installation Win
Closed this issue · 4 comments
Hi,
Thank you for developing this wonderful tool! I was trying to install under my local environment, and I encountered with the following error:
File "<string>", line 437, in <module>
File "<string>", line 81, in run_make_print_config
File "C:\Users\Anaconda3\envs\omicverse\envs\omicverse\lib\subprocess.py", line 421, in check_output
return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
File "C:\Users\Anaconda3\envs\omicverse\envs\omicverse\lib\subprocess.py", line 503, in run
with Popen(*popenargs, **kwargs) as process:
File "C:\Users\Anaconda3\envs\omicverse\envs\omicverse\lib\subprocess.py", line 971, in __init__
self._execute_child(args, executable, preexec_fn, close_fds,
File "C:\Users\Anaconda3\envs\omicverse\envs\omicverse\lib\subprocess.py", line 1456, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
FileNotFoundError: [WinError 2] The system cannot find the file specified
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
I used pip to install, everything else went smoothly except the pip install -U omicverse
Hi,
Thanks for your support, the same issue could be found in #70
Hi,
Thanks for your support, the same issue could be found in #70
Thanks for forwarding. I actually successfully installed under HPC. However, I have a follow-up question, do you think VAE over-integrating, or over-performed that make all samples with the same cell type composition. Refer as the below image:
I used our in-house bulk rna data, and GEO immune cell data (n_obs × n_vars = 93900 × 28638), using 2000 genes for cell type deconvolution.
Hi @RubyLiu206 ,
I don't quite understand why you chose 2,000 highly variable genes for deconvolution, my tutorial uses raw counts and uses all genes(https://omicverse.readthedocs.io/en/latest/Tutorials-bulk2single/t_bulk2single/), you can scale down the number of cells and don't need to use the 90,000 single cell data as a reference, which speeds up the run.
I really appreciate you quick response!! I see you point. I choose 2k was because of CIBERSORT used signature matrix from 500 to 2000 number of genes. I will try with 28638 genes with 500 as top marker for sure.