Is there a way to view expression of a particular gene on the UMAP?

How can I get UMAPs of expression of a particular gene?

Yes, you can!

In Data Exploration there is an “eye icon” next to each gene’s name. For example this is a plot for HBB:

There is also a way to get the same information in a publishable plot format in Plots and Tables → Continuous Embedding:

Hope this helps!

What does the legend indicate here on the Umap projection of the plot? Is it an average expression of the gene?
I would be grateful for your help.

Hi Ania,

In the UMAP projection the expression value of a particular gene for each cell is represented visually. Specifically, the color of each cell corresponds to its gene expression value based on the legend’s thresholds. For instance, in this case cells with gene expression values lower than 1.2 will be colored with the first, lighter red shade. Cells with expression values between 1.2 and 2.4 will be colored with the second color in the legend, and so on.

Also, expression values can be adjusted to either “capped” or “uncapped” under the “Expression values” control. The default setting is capped. “Capped” and “uncapped” values refer to the approaches used in handling the expression levels of genes in a single-cell RNA-seq experiment.
In the case of “capped” values, gene expression levels that exceed a certain predetermined threshold, in this case it’s determined by the 95th percentile, are all set to this threshold. This approach is utilized to manage the potential high variability often found in scRNA-seq data. By capping the expression values at this threshold, one can mitigate the impact of extremely high outliers which may not be biologically relevant but rather artifacts or noise. However, a limitation of this approach is that it could potentially result in the loss of meaningful information about genes that are naturally expressed at extremely high levels or in specific cellular conditions.
On the other hand, “uncapped” values represent the actual normalized expression levels of the genes as detected in the experiment. While this approach maintains all the original data and could potentially provide more granular insights, it might also make the analysis more susceptible to noise.

I hope this answers your question!