Bugs when I do not intend to use PCA to infer in-silico perturbation about dynamo-release HOT 4 CLOSED

Thanks for you using our tool! Under the hook, the in silico perturbation requires a vector field learned in the pca space. Therefore you will need to first run as suggested from the error message.

dyn.vf.VectorField(adata, basis='pca')

Once you did that you can visualize your perturbation prediction on any embedding.

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I see, thanks.

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Hi, sorry to disturb you again. I further intend to check my understanding of your implementation of in silico perturbation.

I think the code here is used to compute the delta_x_i (where i represents the gene),

dyn.pd.perturbation(adata_labeling, gene, [-100], emb_basis="umap")

And in the explaniation of the code, it said:

delta X = [0, 0, 0, delta x_i = 10, 0, 0, .., x_j = -10, 0, 0, 0])

Therefore, this approach can only generate the change of gene expression for one gene, while assuming the rest of the genes remain their expression levels. However, I think the change of tf (like gata1) will affect the gene expressions of other genes (like triggering the delta_x of other genes to change). Is it correct? Thanks.

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