Integrating two Seurat multimodal objects
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5 months ago
roberts ▴ 40

Hello, I have two separate runs of 10x data. One data set is 9 hashed samples and stained with Cite-seq antibodies. The other data is one single individual that was also stained with Cite-seq antibodies but the staining does not seem to work. I am trying to integrate the two seurat objects which I made following this vignette Seurate multi-modal data. However when I try my usually process for merging data

`all_base<- merge(pooledbase,Base165.RNA)

all_base_list <- SplitObject(all_base,"sample")

for (i in 1:length(all_base_list)) {
  DefaultAssay( all_base_list[[i]]) <- "RNA"
  all_base_list[[i]] <- NormalizeData(all_base_list[[i]], verbose = FALSE)
  all_base_list[[i]] <- FindVariableFeatures(all_base_list[[i]], selection.method = "vst",
                                             nfeatures = 5000, verbose = FALSE)
  all_base_list[[i]] <- ScaleData(all_base_list[[i]])
  all_base_list[[i]] <- RunPCA(all_base_list[[i]])
  all_base_list[[i]] <- RunALRA(all_base_list[[i]])

base_merge<- Reduce(merge, all_base_list)` 

I get very ill defined clusters. Any suggestions? Thanks!

scRNA-seq Seurat R 10X • 306 views
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What leads you to believe that the staining didn't work, and how does the scRNA-seq data look by itself without integrating the CITE-seq data?

Entering edit mode

The data on the pooled samples looks good both rna and cite-seq data. The one sample by itself the rna data looks good but the Cite-seq data looks like every cell stained with every antibody. So It looks like every cell is expressing CD4, CD8, CD19, CD56, CD16 etc. There is no real clustering on just the cite-seq data for that sample, and WNN returns similar results.


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