Installation Win

[RubyLiu206]

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 "", 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

[Starlitnightly]

Hi,

Thanks for your support, the same issue could be found in #70

[RubyLiu206]

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.

[Starlitnightly]

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.

[RubyLiu206]

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.