Not sure how related the cause is, but the fix seems to apply too.
In single cell genomics you often have a large correlation between the data and the length of the sequencing reads.
You can calculate the correlation between your metadata and every PC dimension and exclude the highly correlating dimensions before doing the umap. Similar can be done for other metrics. I've seen it done for patient gender for example. I could imagine looking at the correlation for publish year or genre.
In single cell genomics you often have a large correlation between the data and the length of the sequencing reads.
You can calculate the correlation between your metadata and every PC dimension and exclude the highly correlating dimensions before doing the umap. Similar can be done for other metrics. I've seen it done for patient gender for example. I could imagine looking at the correlation for publish year or genre.
Edit: fixed a misconception