unexpected UMAP embeddings with 0.5.2 release using single-cell gene expression data via Scanpy

Hi, we are seeing unexpected UMAP embeddings using the 0.5.2 umap-learn version, run via Scanpy, with our single cell gene expression data (publicly available MERFISH data from Vizgen).

Our original embedding using version 0.5.1 looks like

and the embedding with 0.5.2 looks like

Zooming into the 0.5.2 embedding reveals that cells appear to be embedded into a lattice like structure

We’re wondering if this is being caused in part by some sort of a rounding error in the embedding.

We have included Colab notebooks demonstrating the normal behavior using version 0.5.1 and the new unexpected behavior using version 0.5.2. Please let us know if you have any issues running the notebooks - they require authentication via Google to load the publicly available data and there are static and interactive versions of the UMAP embeddings.

• Colab UMAP-Learn_0.5.1.ipynb

• Colab UMAP-Learn_0.5.2.ipynb

The only differences between the notebooks are where we use pip to install a specific version of umap-learn or use Scanpy’s version.

# pinning to previous 0.5.1 version
# otherwise scanpy grabs umap-learn==0.5.2 (see below)
###########################################
!pip install -q umap-learn==0.5.1

We also tested using the basic usage examples from the documentation and these examples appear to be working with the new 0.5.2 version - see colab notebook Basic_Usage_Test-UMAP_0.5.2.ipynb

[lmcinnes]

That is definitely disconcerting. I’ll try to look into what the issue may be. It looks rather like you are just getting the spectral initialization instead of the UMAP embedding out.

[cornhundred]

Closing since this is something that Scanpy will resolve.