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chris-mcginnis-ucsf / DoubletFinder

R package for detecting doublets in single-cell RNA sequencing data
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14% of cells in each of 5 sample are decided as doublets #58

Closed grimwoo closed 4 years ago

grimwoo commented 4 years ago

It's interesting that 14% of cells in each of 5 sample are decided as doublets. Is it due to some coincidence or due to settings in DoubletFinder? Or may be some of my code is wrong. Could you please tell me?

The cell numbers before and after subset (by "percent.mt <= Percent.mt", "DF_hi.lo == "Singlet"", or both) is listed below:

  Original Only after DF Percentof... Only after mt% Percentof... After DF & mt% Percentof...
1 11478 9833 86% 8009 70% 6666 58%
2 10416 8916 86% 9332 90% 7939 76%
3 11481 9839 86% 11174 97% 9558 83%
4 13464 11526 86% 12199 91% 10357 77%
5 12810 10962 86% 11800 92% 10082 79%

the original code is:

library(Seurat)
library(DoubletFinder)
Seurat.list <- c('GTE001', 'GTE002', 'GTE003', 'GTE009', 'GTE012')
for (i in 1:length(Seurat.list)) {
  Filename.brief <- paste('20191122_', Seurat.list[i], sep = '')
  data_dir <- paste('D:/Cell Ranger Processed Data/', Seurat.list[i], '/filtered_feature_bc_matrix', sep = '')
  Seurat.data <- Read10X(data.dir = data_dir)
  dense.size <- object.size(x = as.matrix(x = Seurat.data))
  Seurat.object <- CreateSeuratObject(counts = Seurat.data, project = Seurat.list[i], min.cells = 5)
  Seurat.object[["percent.mt"]] <- PercentageFeatureSet(Seurat.object, pattern = "^MT-")
  Seurat.object <- NormalizeData(Seurat.object, verbose = FALSE)
  Seurat.object <- FindVariableFeatures(Seurat.object, selection.method = "vst", nfeatures = 2000)
  Seurat.object <- ScaleData(Seurat.object, verbose = FALSE)
  Seurat.object <- RunPCA(Seurat.object, npcs = 30, verbose = FALSE, resolution = 0.1)
  Seurat.object <- RunTSNE(Seurat.object, dims = 1:10, resolution = 0.1)
  Seurat.object <- FindNeighbors(Seurat.object, reduction = "tsne", dims = 1:2)
  Seurat.object <- FindClusters(Seurat.object, resolution = 0.1)
  #Doublet Finder
  sweep.res.list_Seurat <- paramSweep_v3(Seurat.object, PCs = 1:10, sct=FALSE)
  sweep.stats_Seurat <- summarizeSweep(sweep.res.list_Seurat, GT = FALSE)
  bcmvn_Seurat <- find.pK(sweep.stats_Seurat)
  mpK<-as.numeric(as.vector(bcmvn_Seurat$pK[which.max(bcmvn_Seurat$BCmetric)]))
  annotations <- Seurat.object@meta.data$seurat_clusters
  homotypic.prop <- modelHomotypic(annotations)           ## ex: annotations <- seu_Seurat@meta.data$ClusteringResults
  nExp_poi <- round(0.075*length(Seurat.object$seurat_clusters))  ## Assuming 7.5% doublet formation rate - tailor for your dataset
  nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop))
  Seurat.object <- doubletFinder_v3(Seurat.object, PCs = 1:20, pN = 0.25, pK = mpK, nExp = nExp_poi, reuse.pANN = FALSE, sct = FALSE)
  Seurat.object <- doubletFinder_v3(Seurat.object, PCs = 1:10, pN = 0.25, pK = 0.09, nExp = nExp_poi.adj, reuse.pANN =colnames(Seurat.object@meta.data[1,])[13], sct = FALSE)
  Seurat.object@meta.data[,'DF_hi.lo'] <- "Singlet"
  Code_of_Temp <- paste("Seurat.object@meta.data$DF_hi.lo[which(Seurat.object@meta.data$", colnames(as.matrix(Seurat.object@meta.data[1,]))[13], " == 'Doublet')] <- 'Doublet_lo'", sep = '')
  eval(parse(text = Code_of_Temp))
  Code_of_Temp <- paste("Seurat.object@meta.data$DF_hi.lo[which(Seurat.object@meta.data$", colnames(as.matrix(Seurat.object@meta.data[1,]))[14], " == 'Doublet')] <- 'Doublet_hi'", sep = '')
  eval(parse(text = Code_of_Temp))
  #Subset: MT%<=12% & ==singlet
  Percent.mt <- 12
  Seurat.object_temp <- subset(Seurat.object, subset = percent.mt <= Percent.mt & DF_hi.lo == "Singlet" )
}
grimwoo commented 4 years ago

Thanks, I found the related code.

nExp_poi <- round(0.075*length(Patient.object$seurat_clusters))  
## Assuming 7.5% doublet formation rate - tailor for your dataset