HelenaLC
commented
2 years ago Hm. Definitely an interesting thing to ponder on; whether or not the method is applicable here and, if not, why not, isn't straight forward I'd say. I guess the key issue here is that Visium spots != single cells. With the current resolution, we'd expect ~10 cells on average per spot. These needn't come from the same cell subpopulation. So typically one would apply deconvolution to estimate the proportion of subpopulations per spot. I assume that at the moment you have annotated spots into clusters that may correspond to different layers / subpopulation compositions? I'd expect that subpopulations making up these layers exhibit different expression patterns, and needn't necessarily respond coherently to a given treatment. But it's really hard to tell. The main point remaining: Spots are not cells.
vkjain0006
Hi all,
Just need to understand if using pseudobulk approach of analyzing spatial data a right approach ? Well like single cell, data is available as spot by gene matrix and there's count data available. .
I am working on project where we have aprox. 30 sample each for 3 conditions (control, treatment1 and treatment2) and to look for differentially expressed genes based I ran muscat following basic commands as suggested. I do get DEGs but they are not much like 1/2 or 0 per cluster. I am not sure if I am doing something wrong or this is not the right approch for spatial data.
Below is the code I used :
converting seurat object to single cell experimnet format
DefaultAssay(sp_data) <- "Spatial" spdata_sce <- as.SingleCellExperiment(x = sp_data, assay = "Spatial")
initial defining of required variables
(spdata_sce <- prepSCE(spdata_sce, kid = "layers", # subpopulation assignments gid = "primary", # group IDs (ctrl/stim) sid = "sample", # sample IDs (ctrl/stim.1234) drop = TRUE)) # drop all other colData columns
nk <- length(kids <- levels(spdata_sce$cluster_id)) ns <- length(sids <- levels(spdata_sce$sample_id)) names(kids) <- kids; names(sids) <- sids
nb. of cells per cluster-sample
write.csv(t(table(spdata_sce$cluster_id, spdata_sce$sample_id)),file = "cluster-sample_info.csv") t(table(spdata_sce$cluster_id, spdata_sce$sample_id)) #
construct design & contrast matrix
ei <- metadata(spdata_sce)$experiment_info ei <- ei[order(ei$group_id),] mm <- model.matrix(~ 0 + ei$group_id) dimnames(mm) <- list(ei$sample_id, levels(ei$group_id)) contrast <- makeContrasts("Treatment1-Control", levels = mm)
pb <- aggregateData(spdata_sce, assay = "counts", fun = "sum", by = c("cluster_id", "sample_id"))
one sheet per subpopulation
assayNames(pb) t(head(assay(pb)))
run DS analysis
res <- pbDS(pb, design = mm, contrast = contrast, method = "edgeR")
After this when I check for sig DEGs per cluster there are hardly any - 1, 2 or 0 genes show up as sig DEGs between conditions per cluster. Can someone please suggest the best possible approch for spatial data ?
Thanks