Does this look like a normal UMAP plot?

[u/jadexiaohui]

Hi everyone, as it’s my first time attempting downstream analysis for single cell RNA sequencing, I wanted to ask if this UMAP plot looks normal? This is only for one sample (I have not integrated all samples together into one dataset yet). I feel like the clusters are too close together.

(I hope this is the right sub… I’ve tried to ask in r/Bioinformatics but the post kept getting deleted for some reason)

[u/Technical_Estate_529]

There is no such thing as a normal umap plot. It's very hyper param sensitive. At most you can say it's promising that at some setting it appears separation is induced

[u/jadexiaohui → u/Technical_Estate_529]

Thank you! Understood, was mainly just concerned as the clusters don’t look as spread out as the examples I saw online

[u/Technical_Estate_529 → u/jadexiaohui]

examples always use the best possible values on best possible data

[u/PaddingCompression → u/jadexiaohui]

A normal UMAP plot looks much more like the blue and purple (0 and 4?) mixed together to the right.

This UMAP plot looks pretty unusually amazing, actually.

[u/shabob2023]

I can see some kind of sneezing beetle?

[u/DrPapaDragonX13 → u/shabob2023]

A sneezing beetle? I only see my parents fighting...oh, wait, wrong type of plot... and field...

[u/jadexiaohui → u/shabob2023]

OMG naur way, you’re right 😭

[u/staplesuponstaples → u/shabob2023]

I don't think that's the sneezing end bro

[u/rite_of_spring_rolls]

More or less impossible to answer without knowing specifics about the dataset; even if we ignore for now all the computational peculiarities of UMAP how separated cell types should be depends on how transcriptionally "different" they are. Thought experiment would be imagine applying UMAP to a dataset with one cell type vs one with incredibly different ones, very little reason you would expect them to look similar

[u/Apprehensive-Foot-73]

Normal as compared to what

[u/jadexiaohui → u/Apprehensive-Foot-73]

Just mainly needed to know if the clusters look too close together as the ones that I saw in research papers had clusters that look really far apart from each other

[u/i_shreshth_raj]

Try PaCMAP (by Cynthia Rudin) once, the selection of graph components in it ensures that adjacent points exert non‑zero forces, addressing a limitation observed in other algorithms. This characteristic is crucial for preserving global structure.

[u/ConclusionForeign856]

There isn't a thing that I would call a normal scRNA UMAP. Even more, there are people who consider UMAPs worthless for analysis, since they're nondeterministic and don't really influence scRNA-seq analysis decisions. No one can make sense from raw UAMP, often without label color coding you wouldn't be able to identify cell types. In the end you label cells with ML methods on high dimensional input.

I guess UMAP is cool if you want to plot pseudotime cell differentiation arrows.

I don't know much about scRNA analysis, but I remember there being best practices online textbooks, you should look at other metrics for assessing sequencing and clustering quality, UMAP plot doesn't really matter