Open 3XPLOSIV3 opened 3 months ago
Hi, thank you for the question. @ChloeXWang , could you please help with this?
Hi @subercui @ChloeXWang, I am also experiencing this reproducibility issue. Has any insight been gained towards the solution? Thanks!
Hi @klshen8386 By luck I found the source of the issue (I was actually dealing with installation errors regarding flash-attn): the version of scanpy on env file is specified with ">=" and not "==". Thus the latest version is installed, changing the algorithm for high variable gene selection.
If you use scanpy==1.9.1, that should solve the issue (I don't know what is the exact version used by @subercui @ChloeXWang though).
NB, when installing scanpy, the version installed for the igraph dependency could induced an error in Leiden clusterisation (sc.pp.leiden). To solve that, I used the latest version (igraph==0.9.11). Check also that the leidenalg dependency is installed with version >= 0.8.* (should be good by default). Cf. https://github.com/scverse/scanpy/issues/2341
I also had an issue with the networkx package, I now use networkx==3.2.1. (I don't know if it is related, just mentioning in case you has the same issue)
Hope it works for you!
Hello,
I have been working with scGPT for the last week and I am studying currently the GRN part. By running the Tutorial_GRN notebook, and downloading the data/model in the links provided, I am unable to reproduce the article figure (that are the saved outputs of the notebook).
By looking it step by step, it seems that the Preprocessor object is causing the trouble. After the preprocessing I don't have CD3E genes in the data dataset, and I assume that .
I have run the following (same as provided with some print added)
And obtain this : scGPT - INFO - Loading data ... scGPT - INFO - CD3E gene in data : True, CD3G : True, CD3D : True scGPT - INFO - Initial number of genes : 12303 scGPT - INFO - Filtering genes by counts ... scGPT - INFO - Normalizing total counts ... scGPT - INFO - Subsetting highly variable genes ... scGPT - INFO - Binning data ... scGPT - INFO - CD3E gene in data : False, CD3G : False, CD3D : False Final number of genes : 1200
I haven't change either the data or the parameter of Preprocessor provided, so I don't understand why the result is different...
Thank you for your answer!