I'm currently learning to analyze single cell RNAseq and compare my result with the analysis by bioinformatician. We analyzed the same data of 9 individual patients from 10X. His UMAP looks nice, but mine looks a bit messy and aggregated together. I could not access his code or have any contact with him. The following are my codes. I am not sure whether I did anything wrong or do I need to specify more parameter. I have followed the Seurat QC and filtered all low quality cells etc. I would say they are good for QC. Could you examine my codes and see whether you could suggest something to improve my UMAP. We both use Seurat pipeline and Harmony for integration. Looking forward to your suggestion.
#Merge Seurat Object! All_arthritis <- merge(sampleA, y=c(sampleB, sampleC, sampleD, sampleE, sampleF, SampleG, SampleH)) #Normalize the data All_arthritis_normal <- NormalizeData(All_arthritis) All_arthritis_normal <- FindVariableFeatures(All_arthritis_normal) #Scale the data! All_arthritis_normal_scale <- ScaleData(All_arthritis_normal) #Run PCA ! All_arthritis_normal_pca <- RunPCA(All_arthritis_normal_scale, verbose = FALSE) #Plot PC components ! Elboplot<- ElbowPlot(All_arthritis_normal_pca, ndims = 50, reduction = "pca") #n= 30 is okay ! Elboplot #Integration using harmony for umap library(harmony) ## Harmony All_arthritis_harm<- RunHarmony(All_arthritis_normal_pca, group.by.vars = "orig.ident") All_arthritis_harm_umap <- RunUMAP(All_arthritis_harm, dims = 1:30) All_arthritis_neigh <- FindNeighbors(All_arthritis_harm_umap, dims = 1:30) All_arthritis_findclus <- FindClusters(All_arthritis_neigh, resolution = 0.1) DimPlot(All_arthritis_findclus, reduction = "umap", group.by = "seurat_clusters")