Error when running STACAS on spatial data about stacas HOT 6 CLOSED

Hello!
we have never tested STACAS for spatial data, so would be curious to see how that goes. Can you provide more details about your analysis, e.g. how was the object list prepared, in which assay are the data stored, what is n.this in your example?

-m

from stacas.

Hi , created from list of processed Seurat objects, here's the code:

ob.list <- list()
orig.ids <- c()
for (sId in sIds) {
    sPath = paste0(dataDir,sId,".res.rds")
    print(sId)
    ob.list[[sId]] <- readRDS(sPath)
    orig.ids <- c(orig.ids,rep(sId,ncol(ob.list[[sId]])))
}

Run.STACAS(ob.list)

But for Seurat objects I did not compute variable genes, for normalization per sample SCTransform() was used. Is it sufficient, or more details for per sample analysis would be useful?

from stacas.

Hi, the method by default calculates highly variable genes to reduce the dimensionality, but you can bypass this step by providing your own list of genes – in your case, all genes.

For example you could try something like:

all.genes <- rownames(ob.list[[1]])
integrated <- Run.STACAS(ob.list, anchor.features = all.genes)

-m

from stacas.

Thanks for the reply, I used the genes present in each sample (intersect), the analysis worked well, currently inspecting the result. However small issue occred. There were several warnings like this:

Warning in irlba(A = t(x = object), nv = npcs, ...) :
  You're computing too large a percentage of total singular values, use a standard svd instead.

Should be this handled somehow via params?

Also, is there a way to find the markers of new clusters? FindAllMarkers() function did not work properly.

from stacas.

Good that it worked.

As for the warning, it should be fine to ignore it, see satijalab/seurat#1249 . You may also try to lower the number of PCs, since the dimensionality of your data is already quite low (use the parameter dims in STACAS).

I am not sure why FindAllMarkers() would fail, but perhaps try to re-normalize the original data before running the function. For example:

DefaultAssay(integrated) <- 'RNA'
integrated <- NormalizeData(integrated)
markers <- FindAllMarkers(integrated)

from stacas.

Great, everything worked, thanks for replies, closing the issue.

from stacas.