Comments (6)
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.
Related Issues (20)
- package installation suddenly stopped working HOT 1
- file and link not accessible HOT 1
- Process bar? HOT 2
- option to only use intersection of features between datasets
- Add support for SCT normalization HOT 7
- Deletion of FilterAnchors.STACAS HOT 2
- IntegrateData.STACAS function not found HOT 1
- How to tune the STACAS guide tree HOT 1
- Preparing PCA embeddings for objects... 1/5 2/5 3/5 4/5 5/5 Finding integration anchors... Error in Embeddings(object = object.list[[x]][[nn.reduction]])[, dims] : subscript out of bounds HOT 1
- new.assay.name in Run.STACAS HOT 1
- Error while finding anchors with semi supervised approach HOT 4
- Dataset not integrating when increasing features. HOT 4
- Finding markers after integration HOT 2
- Discrepancy in genes included in block list between Human and Mouse
- Run.STACAS Error in GetAssayData HOT 1
- Error in StandardizeGeneSymbols() HOT 1
- working with Seurat v5 layers? HOT 3
- CheckGC() not imported from SeuratObject HOT 1
- implement option for automatic sketching of input datasets
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from stacas.