text: user9698: I tried umap visualization with scanpy :
  sc.pp.normalize_total(adata, target_sum=1e6)
  sc.pp.log1p(adata, base=2)
  sc.pp.highly_variable_genes(adata, flavor='cell_ranger', n_top_genes=400)
  
  sc.pp.scale(adata, zero_center=True, max_value=None, copy=False, layer=None, obsm=None)
  sc.pp.pca(adata, n_comps=50, use_highly_variable=True, svd_solver='arpack')
  sc.pp.neighbors(adata, n_neighbors=50)
  
  sc.tl.umap(adata, min_dist=0.5, spread=1.0)
  sc.pl.umap(adata, color='fullname', use_raw=False, save='samples_umap.pdf')
  
  version
  anndata     0.7.5
  scanpy      1.6.1
  
  But the cells can't separate well
  
  I tried another small dataset with scanpy using the same parameters as before:
  
  sc.tl.umap still failed to down dimension the data properly.
  Then I tried the original umap package using the same data set:
  import umap
  import umap.plot
  mapper = umap.UMAP().fit(adata.X)
  umap.plot.points(mapper)
  
  Now the original umap package can do down dimension very well:
  
  I think there may be something wrong with the umap function in scanpy
  Can anyone please let me know the reason?
  Thanks a lot.
  
  user104: Without more code and data, this can't be debugged. But first question would be does the data pass basic QC (e.g. look at cellranger web summaries or equivalent). If yes, you could trying different normalisation (code not shown) and/or no scaling. Try sc.tl.umap() with default params. Plot the PCA, try t-SNE. Plot the basic QC metrics following the introductory scanpy tutorial. That is how I would start debugging
  
original_text: 9698: <p>I tried umap visualization with <a href="https://github.com/scverse/scanpy" rel="nofollow noreferrer"><code>scanpy</code></a> :</p>
  <pre><code>sc.pp.normalize_total(adata, target_sum=1e6)
  sc.pp.log1p(adata, base=2)
  sc.pp.highly_variable_genes(adata, flavor='cell_ranger', n_top_genes=400)
  
  sc.pp.scale(adata, zero_center=True, max_value=None, copy=False, layer=None, obsm=None)
  sc.pp.pca(adata, n_comps=50, use_highly_variable=True, svd_solver='arpack')
  sc.pp.neighbors(adata, n_neighbors=50)
  
  sc.tl.umap(adata, min_dist=0.5, spread=1.0)
  sc.pl.umap(adata, color='fullname', use_raw=False, save='samples_umap.pdf')
  </code></pre>
  <p>version</p>
  <pre><code>anndata     0.7.5
  scanpy      1.6.1
  </code></pre>
  <p>But the cells can't separate well
  <img src="https://user-images.githubusercontent.com/33963919/209233854-db64fddd-4266-4f87-805b-dced45b1547f.png" alt="image" /></p>
  <p>I tried another small dataset with <code>scanpy</code> using the same parameters as before:
  <img src="https://user-images.githubusercontent.com/33963919/209387824-3a5b1037-f226-49c8-9222-f54c04a62155.jpg" alt="1" /></p>
  <p><code>sc.tl.umap</code> still failed to down dimension the data properly.</p>
  <p>Then I tried the original <a href="https://umap-learn.readthedocs.io/en/latest/plotting.html" rel="nofollow noreferrer"><code>umap</code> package</a> using the same data set:</p>
  <pre><code>import umap
  import umap.plot
  mapper = umap.UMAP().fit(adata.X)
  umap.plot.points(mapper)
  </code></pre>
  <p>Now the original <code>umap</code> package can do down dimension very well:
  <img src="https://user-images.githubusercontent.com/33963919/209387903-0161dfa6-0ca5-48cc-8661-465930e23fef.jpg" alt="2" /></p>
  <p>I think there may be something wrong with the <code>umap</code> function in <code>scanpy</code>
  Can anyone please let me know the reason?
  Thanks a lot.</p>
   104: <p>Without more code and data, this can't be debugged. But first question would be does the data pass basic QC (e.g. look at cellranger web summaries or equivalent). If yes, you could trying different normalisation (code not shown) and/or no scaling. Try <code>sc.tl.umap()</code> with default params. Plot the PCA, try t-SNE. Plot the basic QC metrics following the introductory scanpy tutorial. That is how I would start debugging</p>
  