has2k1
commented
6 months ago This error is makes the most sense when making predictions outside the fitted data (x, the independent observations). There the solution is to use surface="direct" instead of interpolate. i.e. loess(x, y, span=span, degree=2, surface="direct")
In this case it happens when fitting the data which is unexpected. I would need to see the x and y values.
hi, A bug occurred when I was using scanpy for high variant gene arithmetic. I learned that the bug originated from scikit-misc, so I came to you for help.
adata.layers["counts"] = adata.X.copy() sc.pp.normalize_total(adata, target_sum=1e4) adata adata.raw = adata # keep full dimension safe sc.pp.highly_variable_genes( adata, flavor="seurat_v3",# n_top_genes=2000, layer="counts", batch_key="orig.ident", subset=True, span=1 )`ValueError Traceback (most recent call last) Cell In[13], line 3 2 adata.raw = adata # keep full dimension safe ----> 3 sc.pp.highly_variable_genes( 4 adata, 5 flavor="seurat_v3",# 6 n_top_genes=2000, 7 layer="counts", 8 batch_key="orig.ident", 9 subset=True, 10 span=1 11 ) 13 filename = 'melanoma_sw_high_var.h5ad' 14 adata.write(filename)
File ~/miniconda3/envs/scanpy/lib/python3.12/site-packages/scanpy/preprocessing/_highly_variable_genes.py:441, in highly_variable_genes(adata, layer, n_top_genes, min_disp, max_disp, min_mean, max_mean, span, n_bins, flavor, subset, inplace, batch_key, check_values) 439 sig = signature(_highly_variable_genes_seurat_v3) 440 n_top_genes = cast(int, sig.parameters["n_top_genes"].default) --> 441 return _highly_variable_genes_seurat_v3( 442 adata, 443 layer=layer, 444 n_top_genes=n_top_genes, 445 batch_key=batch_key, 446 check_values=check_values, 447 span=span, 448 subset=subset, 449 inplace=inplace, 450 ) 452 if batch_key is None: 453 df = _highly_variable_genes_single_batch( 454 adata, 455 layer=layer, (...) 462 flavor=flavor, 463 )
File ~/miniconda3/envs/scanpy/lib/python3.12/site-packages/scanpy/preprocessing/_highly_variable_genes.py:87, in _highly_variable_genes_seurat_v3(adata, layer, n_top_genes, batch_key, check_values, span, subset, inplace) 85 x = np.log10(mean[not_const]) 86 model = loess(x, y, span=span, degree=2) ---> 87 model.fit() 88 estimat_var[not_const] = model.outputs.fitted_values 89 reg_std = np.sqrt(10**estimat_var)
File _loess.pyx:927, in _loess.loess.fit()
ValueError: b'Extrapolation not allowed with blending'`