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Thank you very much for this great tool. I ran the Python version of scTenifoldKnK with default settings but I realise that after knocking my gene of interest, statistical analyses do not show significance in the knock out of the gene opf interest, i.e. the adjusted.p.value for the gene of interest is 1. Please what could explain this?
Code:
from scTenifold.data import get_test_df
from scTenifold import scTenifoldNet
from scTenifold.data import get_test_df
from scTenifold import scTenifoldKnk
sc = scTenifoldKnk(data=df,
ko_method="default",
ko_genes=["Il17rb"], # the gene you wants to knock out
qc_kws={"min_lib_size": 10, "min_percent": 0.001},
)
result = sc.build()
The text was updated successfully, but these errors were encountered:
Hello, I am the developer of scTenifoldpy. Thanks for using the package.
The problem you described sounds that the issues I fixed in the version 0.1.2.
Could you please specify the version of scTenifoldpy you are using?
Hello, I've ran both Python versions 0.1.2 and 0.1.3 of scTenifoldKnk with default settings, and with both versions my knocked out gene of interest is not significant (adj. p-val = 1), while other genes are significant.
Do you know what the reason for this is?
Thank you very much for this great tool. I ran the Python version of scTenifoldKnK with default settings but I realise that after knocking my gene of interest, statistical analyses do not show significance in the knock out of the gene opf interest, i.e. the adjusted.p.value for the gene of interest is 1. Please what could explain this?
Code:
The text was updated successfully, but these errors were encountered: