Clustering with Seurat
Yes, this can be done, particularly if your concern is with the clustering / UMAP rather than the transformation process; I've done this recently with some Rhapsody single cell data that we generated. The main trick is adding 'return.only.var.genes = FALSE', as well as calculating a variable set that is a bit larger than the desired set.
Note that UMAP is a technique to place cells, rather than genes. If you have the information on where the cells should go, then plotting gene expression on top of that via FeaturePlot should work without any reprojection needed.
Here's what I did (with experiment-specific variables excluded):
sct.data &lt;- SCTransform(sct.data, vars.to.regress=&quot;percent.mt&quot;,
                        variable.features.n = 3000 + length(genesToExclude),
                        return.only.var.genes = FALSE);


sct.data &lt;- RunPCA(sct.data, verbose = TRUE,
                   features=setdiff(VariableFeatures(sct.data), genesToExclude));
sct.data &lt;- RunUMAP(sct.data);
sct.data &lt;- FindNeighbors(sct.data);
sct.data &lt;- FindClusters(sct.data);

All transformed genes should be available for querying in the results.